Advancing abiotic stress monitoring in plants with a wearable non-destructive real-time salicylic acid laser-induced-graphene sensor

Drought and salinity stresses present significant challenges that exert a severe impact on crop productivity worldwide. Understanding the dynamics of salicylic acid (SA), a vital phytohormone involved in stress response, can provide valuable insights into the mechanisms of plant adaptation to cope with these challenging conditions. This paper describes and tests a sensor system that enables real-time and non-invasive monitoring of SA content in avocado plants exposed to drought and salinity. By using a reverse iontophoretic system in conjunction with a laser-induced graphene electrode, we demonstrated a sensor with high sensitivity (82.3 nA/[μmol L-1⋅cm-2]), low limit of detection (LOD, 8.2 μmol L-1), and fast sampling response (20 s). Significant differences were observed between the dynamics of SA accumulation in response to drought versus those of salt stress. SA response under drought stress conditions proved to be faster and more intense than under salt stress conditions. These different patterns shed light on the specific adaptive strategies that avocado plants employ to cope with different types of environmental stressors. A notable advantage of the proposed technology is the minimal interference with other plant metabolites, which allows for precise SA detection independent of any interfering factors. In addition, the system features a short extraction time that enables an efficient and rapid analysis of SA content.

A dataset of unmanned aerial vehicle multispectral images acquired over a field to identify nitrogen requirements

The technique of detecting and tracking an area's physical properties from a distance by measuring its reflected and emitted radiation is known as remote sensing. It gathered data accurately in near real-time. For this purpose, multispectral cameras mounted on UAVs that capture images with different bands can be used to generate vegetation indexes (NDVI, NDRE), which are useful in precision agriculture. In this study UAV image dataset contains 336 multispectral images from a 0.06 ha paddy field with three different phonological cycles of the crop (vegetative, reproductive, and ripening) in the north-western province of Sri Lanka. The selected sample rice variety is BG300. The images were taken over five days, starting from August 14 to October 5, 2023. The UAV flight took place at 30 m from the canopy level with the multispectral camera titled at an angle of 900. The SPAD Chlorophyll Meter was used to collect ground truth data, which is proportional to the nitrogen level of the leaf. There were 50 randomly selected readings throughout the paddy field. Relevant climate data for five days was provided by the Rice Research and Development Institute, Bathalagoda, which belongs to the paddy field. The purpose of this data creation was to aid researchers who are generally interested in disease diagnosis. Moreover, this dataset allows for studying the effect of using different tilt angles on the 3D reconstruction of the paddy fields and the generation of orthomosaics.

Analysis of small-scale soil CO2 fluxes in an orange orchard under irrigation and soil conservative practices

The quantification of soil carbon dioxide (CO2) flux represents an indicator of the agro-ecosystems sustainability. However, the monitoring of these fluxes is quite challenging due to their high spatially-temporally variability and dependence on environmental variables and soil management practices.In this study, soil CO2 fluxes were measured using a low-cost accumulation chamber, that was realized ad hoc for the surveys, in an orange orchard managed under different soil management (SM, bare versus mulched soils) and water regime (WR, full irrigation versus regulated deficit irrigation) strategies. In particular, the soil CO2 flux measurements were acquired in discontinuous and continuous modes, together with ancillary agrometeorological and soil-related information, and then compared to the agrosystem scale CO2 fluxes measured by the eddy covariance (EC) technique.Overall significant differences were obtained for the soil CO2 discontinuous fluxes as function of the WR (0.16 ± 0.01 and 0.14 ± 0.01 mg m-2 s-1 under full irrigation and regulated deficit irrigation, respectively). For the continuous soil CO2 measurements, the response observed for the SM factor varied from year to year, indicating for the overall reference period 2022-23 higher soil CO2 flux under the mulched soils (0.24 ± 0.01 mg m-2 s-1) than under bare soil conditions (0.15 ± 0.00 mg m-2 s-1). Inter-annual variations were also observed as function of the day-of-year (DOY), the SM and their interactions, resulting in higher soil CO2 flux under the mulched soils (0.24 ± 0.02 mg m-2 s-1) than under bare soil (0.15 ± 0.01 mg m-2 s-1) in certain periods of the years, according to the environmental conditions.

Results

suggest the importance of integrating soil CO2 flux measurements with ancillary variables that explain the variability of the agrosystem and the need to conduct the measurements using different operational modalities, also providing for night-time monitoring of CO2. In addition, the study underlines that the small-scale chamber measurements can be used to estimate soil CO2 fluxes at orchard scale if fluxes are properly scaled.

Enhancing surface drainage mapping in eastern Canada with deep learning applied to LiDAR-derived elevation data

Agricultural dykelands in Nova Scotia rely heavily on a surface drainage technique called land forming, which is used to alter the topography of fields to improve drainage. The presence of land-formed fields provides useful information to better understand land utilization on these lands vulnerable to rising sea levels. Current field boundaries delineation and classification methods, such as manual digitalization and traditional segmentation techniques, are labour-intensive and often require manual and time-consuming parameter selection. In recent years, deep learning (DL) techniques, including convolutional neural networks and Mask R-CNN, have shown promising results in object recognition, image classification, and segmentation tasks. However, there is a gap in applying these techniques to detecting surface drainage patterns on agricultural fields. This paper develops and tests a Mask R-CNN model for detecting land-formed fields on agricultural dykelands using LiDAR-derived elevation data. Specifically, our approach focuses on identifying groups of pixels as cohesive objects within the imagery, a method that represents a significant advancement over pixel-by-pixel classification techniques. The DL model developed in this study demonstrated a strong overall performance, with a mean Average Precision (mAP) of 0.89 across Intersection over Union (IoU) thresholds from 0.5 to 0.95, indicating its effectiveness in detecting land-formed fields. Results also revealed that 53% of Nova Scotia's dykelands are being used for agricultural purposes and approximately 75% (6924 hectares) of these fields were land-formed. By applying deep learning techniques to LiDAR-derived elevation data, this study offers novel insights into surface drainage mapping, enhancing the capability for precise and efficient agricultural land management in regions vulnerable to environmental changes.

Empowering agricultural research: A comprehensive custard apple (Annona squamosa) disease dataset for precise detection

The Custard Apple, known as sugar apple or sweetsop, spans diverse regions like India, Portugal, Thailand, Cuba, and the West Indies. This dataset holds 8226 images of Custard Apple (Annona squamosa) fruit and leaf diseases, categorized into six types: Athracnose, Blank Canker, Diplodia Rot, Leaf Spot on fruit, Leaf Spot on leaf, and Mealy Bug. It's a key resource for refining machine learning algorithms focused on detecting and classifying diseases in Custard Apple plants. Utilizing methods like deep learning, feature extraction, and pattern recognition, this dataset sharpens automated disease identification precision. Its extensive range suits testing and training disease identification techniques. Public access fosters collaboration, fast-tracking plant pathology advancements and supporting Custard Apple plant sustainability. This dataset fosters collaborative efforts, aiding disease prevention techniques to boost Custard Apple yield and refine farming. It enhances disease identification, monitoring, and management in Custard Apple production, aiming to elevate agricultural practices and crop yields.

Inter-coat protein loading of active ingredients into Tobacco mild green mosaic virus through partial dissociation and reassembly of the virion

Chemical pesticide delivery is a fundamental aspect of agriculture. However, the extensive use of pesticides severely endangers the ecosystem because they accumulate on crops, in soil, as well as in drinking and groundwater. New frontiers in nano-engineering have opened the door for precision agriculture. We introduced Tobacco mild green mosaic virus (TMGMV) as a viable delivery platform with a high aspect ratio and favorable soil mobility. In this work, we assess the use of TMGMV as a chemical nanocarrier for agriculturally relevant cargo. While plant viruses are usually portrayed as rigid/solid structures, these are "dynamic materials," and they "breathe" in solution in response to careful adjustment of pH or bathing media [e.g., addition of solvent such as dimethyl sulfoxide (DMSO)]. Through this process, coat proteins (CPs) partially dissociate leading to swelling of the nucleoprotein complexes-allowing for the infusion of active ingredients (AI), such as pesticides [e.g., fluopyram (FLP), clothianidin (CTD), rifampicin (RIF), and ivermectin (IVM)] into the macromolecular structure. We developed a "breathing" method that facilitates inter-coat protein cargo loading, resulting in up to  ~ 1000 AIs per virion. This is of significance since in the agricultural setting, there is a need to develop nanoparticle delivery strategies where the AI is not chemically altered, consequently avoiding the need for regulatory and registration processes of new compounds. This work highlights the potential of TMGMV as a pesticide nanocarrier in precision farming applications; the developed methods likely would be applicable to other protein-based nanoparticle systems.

LysipheN: a gravimetric IoT device for near real-time high-frequency crop phenotyping: a case study on common beans

Climate instability directly affects agro-environments. Water scarcity, high air temperature, and changes in soil biota are some factors caused by environmental changes. Verified and precise phenotypic traits are required for assessing the impact of various stress factors on crop performance while keeping phenotyping costs at a reasonable level. Experiments which use a lysimeter method to measure transpiration efficiency are often expensive and require complex infrastructures. This study presents the development and testing process of an automated, reliable, small, and low-cost prototype system using IoT with high-frequency potential in near-real time. Because of its waterproofness, our device-LysipheN-assesses each plant individually and can be deployed for experiments in different environmental conditions (farm, field, greenhouse, etc.). LysipheN integrates multiple sensors, automatic irrigation according to desired drought scenarios, and a remote, wireless connection to monitor each plant and device performance via a data platform. During testing, LysipheN proved to be sensitive enough to detect and measure plant transpiration, from early to ultimate plant developmental stages. Even though the results were generated on common beans, the LysipheN can be scaled up/adapted to other crops. This tool serves to screen transpiration, transpiration efficiency, and transpiration-related physiological traits. Because of its price, endurance, and waterproof design, LysipheN will be useful in screening populations in a realistic ecological and breeding context. It operates by phenotyping the most suitable parental lines, characterizing genebank accessions, and allowing breeders to make a target-specific selection using functional traits (related to the place where LysipheN units are located) in line with a realistic agronomic background.

SorghumWeedDataset_Classification and SorghumWeedDataset_Segmentation datasets for classification, detection, and segmentation in deep learning

The intuition behind this data acquisition is to encourage research for addressing the problem of weeds in agriculture through computer vision applications. Data is acquired in the form of images from uniform and random crop-spacing fields. In other words, we have taken a step forward to identify weeds from fields that follow any method of sowing, which ultimately leads to the transformation of traditional agriculture into precision agriculture. Sorghum crop and its associated weeds are chosen as the research objects during this process. These acquired data are used in framing two datasets. The first dataset termed 'SorghumWeedDataset_Classification' is a crop-weed classification dataset created with 4312 data samples for addressing crop-weed classification problems. The second dataset termed 'SorghumWeedDataset_Segmentation' is a crop-weed segmentation dataset that contains 5555 manually pixel-wise annotated data segments from 252 data samples for addressing crop-weed localization, detection, and segmentation problems. All data samples are acquired in April and May 2023 from Sri Ramaswamy Memorial (SRM) Care Farm, Chengalpattu district, Tamil Nadu, India. Manually annotated data samples and data segments are verified by agronomists. The datasets are made publicly available to the research community to solve the crop-weed problems using state-of-the-art image processing, machine learning, and deep learning algorithms. To the best of our knowledge, these are the first open-access crop-weed research datasets from Indian fields for classification and segmentation to deal with weed issues in uniform and random crop-spacing fields. However, other available datasets (from Indian fields) are either non-research datasets or available on subscription/request.

A novel dataset of potato leaf disease in uncontrolled environment

Potatoes are of the utmost importance for both food processing and daily consumption; however, they are also prone to pests and diseases, which can cause significant economic losses. To address this issue, the implementation of image processing and computer vision methods in conjunction with machine learning and deep learning techniques can serve as an alternative approach for quickly identifying diseases in potato leaves. Several studies have demonstrated promising results. However, the current research is limited by the use of a single dataset, the PlantVillage dataset, which may not accurately represent the diverse conditions of potato pests and diseases in real-world settings. Therefore, a new dataset that accurately depicts various types of diseases is crucial. We propose a novel dataset that offers several advantages over previous datasets, including data obtained in an uncontrolled environment that results in a diverse range of variables such as background and image angles. The proposed dataset comprises 3076 images categorized into seven classes, including leaves attacked by viruses, bacteria, fungi, pests, nematodes, phytophthora, and healthy leaves. This dataset aims to provide a more accurate representation of potato leaf diseases and facilitate advancements in the current research on potato leaf disease identification.

Mapping soil suitability using phenological information derived from MODIS time series data in a semi-arid region: A case study of Khouribga, Morocco

To address the increasing global demand for food, it is crucial to implement sustainable agricultural practices, which include effective soil management techniques for enhancing productivity and environmental conditions. In this regard, a study was conducted to assess the efficacy of utilizing phenological metrics derived from satellite data in order to map and identify suitable agricultural soil within a semi-arid region. Two distinct methodologies were compared: one based on physicochemical soil parameters and the other utilizing the phenological response of vegetation through the application of the Normalized Difference Vegetation Index (NDVI) Modis-time series. The study findings indicated that the NDVI-based approach successfully identified a specific class of soil suitability for agriculture (referred to as S1) that could not be effectively mapped using the multi-criteria analysis (MCAD) method relying on soil physicochemical parameters. This S1 class of soil suitability accounted for approximately 5 % of the total study area. These outcomes suggest that phenological-based approaches offer greater potential for spatio-temporal monitoring of soil suitability status compared to MCAD, which heavily relies on discrete observations and necessitates frequent updates of soil parameters. The approach developed to map the soil-suitability is a valuable tool for sustainable agricultural development, and it can play an effective role in ensuring food security and conducting a land agriculture assessment.

Nanotechnology in precision agriculture: Advancing towards sustainable crop production

Nanotechnology offers many potential solutions for sustainable agroecosystem, including improvement in nutrient use efficiency, efficacy of pest management, and minimizing the adverse environmental effects of agricultural production. Herein, we first highlighted the integrated application of nanotechnology and precision agriculture for sustainable productivity. Application of nanoparticle mediated material and advanced biosensors in precision agriculture is only possible by nanochips or nanosensors. Nanosensors offers the measurement of various stresses, soil quality parameters and detection of heavy metals along with the enhanced data collection, enabling precise decision-making and resource management in agricultural systems. Nanoencapsulation of conventional chemical fertilizers (known as nanofertilizers), and pesticides (known as nanopesticides) helps in sustained and slow release of chemicals to soils and results in precise dosage to plants. Further, nano-based disease detection kits are popular tools for early and speedy detection of viral diseases. Many other innovative approaches including biosynthesized nanoparticles have been evaluated and proposed at various scales, but in fact there are some barriers for practical application of nanotechnology in soil-plant system, including safety and regulatory concerns, efficient delivery at field levels, and consumer acceptance. Finally, we outlined the policy options and actions required for sustainable agricultural productivity, and proposed various research pathways that may help to overcome the upcoming challenges regarding practical implications of nanotechnology.

AgDataBox-IoT - application development for agrometeorological stations in smart

Currently, Brazil is one of the world's largest grain producers and exporters. Agriculture has already entered its 4.0 version (2017), also known as digital agriculture, when the industry has entered the 4.0 era (2011). This new paradigm uses Internet of Things (IoT) techniques, sensors installed in the field, network of interconnected sensors in the plot, drones for crop monitoring, multispectral cameras, storage and processing of data in Cloud Computing, and Big Data techniques to process the large volumes of generated data. One of the practical options for implementing precision agriculture is the segmentation of the plot into management zones, aiming at maximizing profits according to the productive potential of each zone, being economically viable even for small producers. Considering that climate factors directly influence yield, this study describes the development of a sensor network for climate monitoring of management zones (microclimates), allowing the identification of climate factors that influence yield at each of its stages.•Application of the internet of things to assist in decision making in the agricultural production system.•AgDataBox (ADB-IoT) web platform has an Application Programming Interface (API).•An agrometeorological station capable of monitoring all meteorological parameters was developed (Kate 3.0).

Review: When worlds collide - poultry modeling in the 'Big Data' era

Within poultry production systems, models have provided vital decision support, opportunity analysis, and performance optimization capabilities to nutritionists and producers for decades. In recent years, due to the advancement of digital and sensor technologies, 'Big Data' streams have emerged, optimally positioned to be analyzed by machine-learning (ML) modeling approaches, with strengths in forecasting and prediction. This review explores the evolution of empirical and mechanistic models in poultry production systems, and how these models may interact with new digital tools and technologies. This review will also examine the emergence of ML and Big Data in the poultry production sector, and the emergence of precision feeding and automation of poultry production systems. There are several promising directions for the field, including: (1) application of Big Data analytics (e.g., sensor-based technologies, precision feeding systems) and ML methodologies (e.g., unsupervised and supervised learning algorithms) to feed more precisely to production targets given a 'known' individual animal, and (2) combination and hybridization of data-driven and mechanistic modeling approaches to bridge decision support with improved forecasting capabilities.

Dataset of temperature, humidity, and actuator states of an east-facing South African Greenhouse Tunnel

A greenhouse tunnel in Stellenbosch, South Africa was used for testing a generic sensing system for monitoring and control of climatic conditions in the tunnel. Three temperature and humidity sensors were used to record data throughout the day in 5 min intervals. Bambara Nuts, a climate change-resilient and nutritious crop, were grown in a separate study in the tunnel using an aeroponics system. These were chosen as it is regarded as the norm in autonomous greenhouse temperature control in the region. During data collection, the sensors were placed at the front, middle, and back of the tunnel. At the front, there was an industrial extraction fan, and at the back, there was an evaporative cooling wet wall. The fan and wet wall were controlled using the middle sensor data that was averaged every minute to determine if the fan and wet wall should be on or off. The hysteresis band used as a threshold was to turn the fan on when the middle temperature reached 30 °C and to turn it off it was 22 °C. This data collection method extended from 31 December 2022 to 13 June 2023, collecting 162 days of temperature and humidity data for that period.

VineLiDAR: High-resolution UAV-LiDAR vineyard dataset acquired over two years in northern Spain

LiDAR (Light Detection and Ranging) technology's precision in data collection has gained immense traction in the field of remote sensing, particularly in Precision Agriculture using Unmanned Aerial Vehicles (UAVs). To fulfill the pressing need for public UAV LiDAR datasets in the domain of Agricultural Sciences, especially for woody crops such as vineyards, this study presents an extensive dataset of LiDAR data collected from vineyards in northern Spain. The DJI M300 multi-rotor platform, equipped with a DJI Zenmuse L1 LiDAR sensor, conducted UAV flights at 20, 30, and 50 meters above ground level (AGL) across two vineyards during three development stages in 2021 and 2022. This dataset is composed of ten high-density 3D LiDAR point clouds stored in .laz format with embedded RGB information in each point. It provides insights into vineyard morphology and development, thereby aiding in the optimization of vineyard management strategies. Furthermore, it serves as a valuable tool for agricultural robotics, offering comprehensive terrain information for developing efficient flight paths and navigation algorithms. Finally, it serves as a reliable "ground truth" dataset to validate satellite-derived models, facilitating the creation of highly accurate digital elevation models (DEMs) and other derived models.

Failure to scale in digital agronomy: An analysis of site-specific nutrient management decision-support tools in developing countries

While many have extolled the potential impacts of digital advisory services for smallholder agriculture, the evidence for sustained uptake of such tools remains limited. This paper utilizes a survey of tool developers and researchers, as well as a systematic meta-analysis of prior studies, to assess the extent and challenges of scaling decision support tools for site-specific soil nutrient management (SSNM-DST) across smallholder farming systems, where "scaling" is defined as a significant increase in tool usage beyond pilot levels. Our evaluation draws on relevant literature, expert opinion and apps available in different repositories. Despite their acclaimed yield benefits, we find that SSNM-DST have struggled to reach scale over the last few decades and, with strong heterogeneity in adoption among intended stakeholders and tools. For example, the log odds of a SSNM-DST reaching 5-10 % of the target farmers compared with reaching none, decreases by ∼200% when a technical problem is stated as a reason for the tools' failure to be used at scale. We find a similar decrease in odds ratios when technical, socioeconomic, policy, and R&D constraints were identified as barriers to scaling by national extension and private systems. Meta-regression analysis indicates that the response ratio of using SSNM-DST over Farmer Fertilizer Practice (FFP) varies by non-tool related covariates, such as initial crop yield potential under FFP, current and past crop types, acidity class of the soil, temperature and rainfall regimes, and the amount of input under FFP. In general, the SSNM-DST have moved one step forward compared with the traditional 'blanket' fertilizer recommendation by accounting for in-field heterogeneities in soil and crop characteristics, while remaining undifferentiated in terms of demographic and socioeconomic heterogeneities among users, which potentially constrains adoption at scale. The SSNM-DSTs possess reasonable applicability and can be labeled 'ready' from purely scientific viewpoints, although their readiness for system-level uptake at scale remains limited, especially where socio-technical and institutional constraints are prevalent.

A state-of-the-art review of image motion deblurring techniques in precision agriculture

Image motion deblurring is a crucial technology in computer vision that has gained significant attention attracted by its outstanding ability for accurate acquisition of motion image information, processing and intelligent decision making, etc. Motion blur has recently been considered as one of the major challenges for applications of computer vision in precision agriculture. Motion blurred images seriously affect the accuracy of information acquisition in precision agriculture scene image such as testing, tracking, and behavior analysis of animals, recognition of plant phenotype, critical characteristics of pests and diseases, etc. On the other hand, the fast motion and irregular deformation of agriculture livings, and motion of image capture device all introduce great challenges for image motion deblurring. Hence, the demand of more efficient image motion deblurring method is rapidly increasing and developing in the applications with dynamic scene. Up till now, some studies have been carried out to address this challenge, e.g., spatial motion blur, multi-scale blur and other types of blur. This paper starts with categorization of causes of image blur in precision agriculture. Then, it gives detail introduction of general-purpose motion deblurring methods and their the strengthen and weakness. Furthermore, these methods are compared for the specific applications in precision agriculture e.g., detection and tracking of livestock animal, harvest sorting and grading, and plant disease detection and phenotyping identification etc. Finally, future research directions are discussed to push forward the research and application of advancing in precision agriculture image motion deblurring field.

The potential of remote sensing of cover crops to benefit sustainable and precision fertilization

Cover crops and precision fertilization are two core strategies to advance sustainable agriculture. Based on a review of proven achievements in remote sensing of vegetation, a novel approach is proposed to use remote-sensing of cover crops to map soil nutrient availability and to produce prescription maps for precision basal fertilization prior to sowing the following cash crop. The first goal of this manuscript is to introduce the concept of using remote-sensing of cover crops as 'reflectors' or 'bio-indicators' of soil nutrient availability. This concept has two components: 1. mapping nitrogen availability using remote-sensing of cover crops; 2. using remotely-detected visual symptoms of cover crops' nutrient deficiencies to guide sampling schemes. The second goal was to describe two case studies that initially evaluated the feasibility of this concept in a 20 ha field. In the first case study, cover crops mixtures containing legumes and cereals were sown during two seasons in soils with different nitrogen levels. Cereals dominated the mixture when soil nitrogen levels were low, while legumes dominated when levels were high. Plant height and texture analysis derived from UAV-RGB-images were used to measure differences between the dominant species as an indicator of soil nitrogen availability. In the second case study, in an oat cover crop, three different appearances of visual symptoms (phenotypes) were observed throughout the field, and laboratory analysis showed they significantly differed in their nutrient levels. Spectral vegetation indices and plant height derived from UAV-RGB-images were analyzed by a multi-stage classification procedure to differentiate between the phenotypes. The classified product was interpreted and interpolated to generate a high-resolution map showing nutrient uptake for the whole field. The suggested concept essentially elevates the services cover crops can provide to benefit sustainable agriculture if incorporated with remote-sensing. The potentials, limitations and open questions concerning the suggested concept are discussed.

Better richer than environmentally friendly? Describing preferences toward and factors affecting precision agriculture adoption in Italy

Precision agriculture is expected to support and strengthen the sustainability of food production. In spite of the demonstrated benefits of the application of Information Technology to improve agricultural practices, such as yield increase and input reduction, in Italy its adoption still lags behind. In order to understand limits of and perspectives on the adoption of such technologies, we conducted an explorative study. A survey with a choice experiment was carried out in Italy among 471 farmers and people interested in agricultural machinery and technologies. The results highlight how specific factors, such as excessive costs and lack of incentive policies, may limit the spread of precision agriculture. Conversely, the provision of adequate technical support would likely favor its adoption. Furthermore, latent class modeling was used to identify three segments of potential buyers: sustainability seekers; precision agriculture best features supporters; low emissions fans. Potential policy and market implications of this explorative study are discussed in the conclusion.

Growing degree-hours and degree-days in two management zones for each phenological stage of wheat (Triticum aestivum L.)

Monitoring the climatic conditions of crops is essential for smart agriculture development and adaptation of agricultural systems in the era of global change. Thereby, it is possibly better to understand the stages of development of the crop, thus adopting management practices more efficiently and planning the harvest with greater accuracy. This study was developed to analyze the growing degree-hours and degree-days in two management zones (MZs) for each phenological stage of wheat (Triticum aestivum L.) and the application of low-cost agroclimatological stations to monitor the climatic conditions of the field production. The study was developed in a Ferralsol in Céu-Azul/Brazil. Ten low-cost agrometeorological stations were installed in two MZs delineated based on elevation data using the web platform AgDataBox. Data on solar radiation, atmospheric pressure, wind speed, precipitation, relative humidity, air, and soil temperature were evaluated over two wheat crop seasons. Our results showed different climatic conditions, especially humidity and temperature, between MZs and crop seasons, which could probably cause yield variability. By the low-cost agroclimatological stations, it is possible to collect data on the thermal accumulation by the culture in growing degree-hours, which is a more accurate parameter than the growing degree-days (commonly used in similar studies). With the growing degree-hours data, it was possible to follow the development of the phenological stages of wheat. In conclusion, the results obtained suggest the importance of evaluating agroclimatological parameters in monitoring wheat crops. However, more studies are needed in regions with greater slopes, which may have microclimates that intensely influence the crop.

Mediation of gaseous emissions and improving plant productivity by DCD and DMPP nitrification inhibitors: Meta-analysis of last three decades

Nitrification inhibitors (NIs), especially dicyandiamide (DCD) and 3,4-dimethylpyrazole phosphate (DMPP), have been extensively investigated to mitigate nitrogen (N) losses from the soil and thus improve crop productivity by enhancing N use efficiency. However, to provide crop and soil-specific guidelines about using these NIs, a quantitative assessment of their efficacy in mitigating gaseous emissions, worth for nitrate leaching, and improving crop productivity under different crops and soils is yet required. Therefore, based upon 146 peer-reviewed research studies, we conducted a meta-analysis to quantify the effect of DCD and DMPP on gaseous emissions, nitrate leaching, soil inorganic N, and crop productivity under different variates. The efficacy of the NIs in reducing the emissions of CO2, CH4, NO, and N2O highly depends on the crop, soil, and experiment types. The comparative efficacy of DCD in reducing N2O emission was higher than the DMPP under maize, grasses, and fallow soils in both organic and chemical fertilizer amended soils. The use of DCD was linked to increased NH3 emission in vegetables, rice, and grasses. Depending upon the crop, soil, and fertilizer type, both the NIs decreased nitrate leaching from soils; however, DMPP was more effective. Nevertheless, the effect of DCD on crop productivity indicators, including N uptake, N use efficiency, and biomass/yield was higher than DMPP due to certain factors. Moreover, among soils, crops, and fertilizer types, the response by plant productivity indicators to the application of NIs ranged between 35 and 43%. Overall, the finding of this meta-analysis strongly suggests the use of DCD and DMPP while considering the crop, fertilizer, and soil types.

The q-rung fuzzy LOPCOW-VIKOR model to assess the role of unmanned aerial vehicles for precision agriculture realization in the Agri-Food 4.0 era

Smart agriculture is gaining a lot of attention recently, owing to technological advancement and promotion of sustainable habits. Unmanned aerial vehicles (UAVs) play a crucial role in smart agriculture by aiding in different phases of agriculture. The contribution of UAVs to sustainable and precision agriculture is a critical and challenging issue to be taken into account, particularly for smallholder farmers in order to save time and money, and improve their agricultural skills. Thence, this study targets to propose an integrated group decision-making framework to determine the best agricultural UAV. Previous studies on UAV evaluation, (i) could not model uncertainty effectively, (ii) weights of experts are not methodically determined; (iii) importance of experts and criteria types are not considered during criteria weight calculation, and (iv) personalized ranking of UAVs is lacking along with consideration to dual weight entities. Herein, nine critical selection criteria are identified, drawing upon the relevant literature and experts' opinions, and five extant UAVs are considered for evaluation. To circumvent the gaps, in this work, a new integrated framework is developed considering q-rung orthopair fuzzy numbers (q-ROFNs) for apt UAV selection. Specifically, methodical estimation of experts' weights is achieved by presenting the regret measure. Further, weighted logarithmic percentage change-driven objective weighting (LOPCOW) technique is formulated for criteria weight calculation, and an algorithm for personalized ranking of UAVs is presented with visekriterijumska optimizacija i kompromisno resenje (VIKOR) approach combined with Copeland strategy. The findings show that the foremost criteria in agricultural UAV selection are "camera," "power system," and "radar system," respectively. Further, it is inferred that the most promising UAV is the DJ AGRAS T30. Since the applicability of UAV in agriculture will get inevitable, the developed framework can be an effective decision support system for farmers, managers, policymakers, and other stakeholders.

Non-invasive in-vivo glucose-based stress monitoring in plants

Plant stress responses involve a suite of genetically encoded mechanisms triggered by real-time interactions with their surrounding environment. Although sophisticated regulatory networks maintain proper homeostasis to prevent damage, the tolerance thresholds to these stresses vary significantly among organisms. Current plant phenotyping techniques and observables must be better suited to characterize the real-time metabolic response to stresses. This impedes practical agronomic intervention to avoid irreversible damage and limits our ability to breed improved plant organisms. Here, we introduce a sensitive, wearable electrochemical glucose-selective sensing platform that addresses these problems. Glucose is a primary plant metabolite, a source of energy produced during photosynthesis, and a critical molecular modulator of various cellular processes ranging from germination to senescence. The wearable-like technology integrates a reverse iontophoresis glucose extraction capability with an enzymatic glucose biosensor that offers a sensitivity of 22.7 nA/(μM·cm²), a limit of detection (LOD) of 9.4 μM, and a limit of quantification (LOQ) of 28.5 μM. The system's performance was validated by subjecting three different plant models (sweet pepper, gerbera, and romaine lettuce) to low-light and low-high temperature stresses and demonstrating critical differential physiological responses associated with their glucose metabolism. This technology enables non-invasive, non-destructive, real-time, in-situ, and in-vivo identification of early stress response in plants and provides a unique tool for timely agronomic management of crops and improving breeding strategies based on the dynamics of genome-metabolome-phenome relationships.

Development of maize plant dataset for intelligent recognition and weed control

This paper focuses on the development of maize plant datasets for the purposes of recognizing maize plants and weed species, as well as the precise automated application of herbicides to the weeds. The dataset includes 36,374 images captured with a high-resolution digital camera during the weed survey and 500 images annotated with the Labelmg suite. Images of the eighteen farmland locations in North Central Nigeria, containing the maize plants and their associated weeds were captured using a high-resolution camera in each location. This dataset will serve as a benchmark for computer vision and machine learning tasks in the intelligent maize and weed recognition research.

Current understanding, challenges and perspective on portable systems applied to plant monitoring and precision agriculture

The devastating effects of global climate change on crop production and exponential population growth pose a major challenge to agricultural yields. To cope with this problem, crop performance monitoring is becoming increasingly necessary. In this scenario, the use of sensors and biosensors capable of detecting changes in plant fitness and predicting the evolution of their morphology and physiology has proven to be a useful strategy to increase crop yields. Flexible sensors and nanomaterials have inspired the emerging fields of wearable and on-plant portable devices that provide continuous and accurate long-term sensing of morphological, physiological, biochemical, and environmental parameters. This review provides an overview of novel plant sensing technologies by discussing wearable and integrated devices proposed for engineering plant and monitoring its morphological traits and physiological processes, as well as plant-environment interactions. For each application scenario, the state-of-the-art sensing solutions are grouped according to the plant organ on which they have been installed highlighting their main technological advantages and features. Finally, future opportunities, challenges and perspectives are discussed. We anticipate that the application of this technology in agriculture will provide more accurate measurements for farmers and plant scientists with the ability to track crop performance in real time. All of this information will be essential to enable rapid optimization of plants development through tailored treatments that improve overall plant health even under stressful conditions, with the ultimate goal of increasing crop productivity in a more sustainable manner.

MangoLeafBD: A comprehensive image dataset to classify diseased and healthy mango leaves

Agriculture is one of the few remaining sectors that is yet to receive proper attention from the machine learning community. The importance of datasets in the machine learning discipline cannot be overemphasized. The lack of standard and publicly available datasets related to agriculture impedes practitioners of this discipline to harness the full benefit of these powerful computational predictive tools and techniques. To improve this scenario, we develop, to the best of our knowledge, the first-ever standard, ready-to-use, and publicly available dataset of mango leaves. The images are collected from four mango orchards of Bangladesh, one of the top mango-growing countries of the world. The dataset contains 4000 images of about 1800 distinct leaves covering seven diseases. Although the dataset is developed using mango leaves of Bangladesh only, since we deal with diseases that are common across many countries, this dataset is likely to be applicable to identify mango diseases in other countries as well, thereby boosting mango yield. This dataset is expected to draw wide attention from machine learning researchers and practitioners in the field of automated agriculture.

Dataset on unmanned aerial vehicle multispectral images acquired over a vineyard affected by Botrytis cinerea in northern Spain

Remote sensing makes it possible to gather data rapidly, precisely, accurately, and non-destructively, allowing it to assess grapevines accurately in near real-time. In addition, multispectral cameras capture information in different bands, which can be combined to generate vegetation indices useful in precision agriculture. This dataset contains 16,504 multispectral images from a 1.06 ha vineyard affected by Botrytis cinerea, in the north of Spain. The photos were taken throughout four UAV flights at 30 m height with varying camera angles on 16 September 2021, the same date as the grape harvest. The first flight took place with the camera tilted at 0° (nadir angle), the second flight at 30°, the third flight at 45°, and the fourth flight was also performed at 0° but was scheduled in the afternoon to capture the shadows of the plants projected on the ground. This dataset was created to support researchers interested in disease detection and, in general, UAV remote sensing in vineyards and other woody crops. Moreover, it allows digital photogrammetry and 3D reconstruction in the context of precision agriculture, enabling the study of the effect of different tilt angles on the 3D reconstruction of the vineyard and the generation of orthomosaics.

Dataset on UAV RGB videos acquired over a vineyard including bunch labels for object detection and tracking

Counting the number of grape bunches at an early stage of development offers relevant information to the winegrower about the potential yield to be harvested. However, manual counting on the fields is laborious and time-consuming. Remote sensing, and more precisely unmanned aerial vehicles mounted with RGB or multispectral cameras, facilitate this task rapidly and accurately. This dataset contains 40 RGB videos from a 1.06-ha vineyard located in northern Spain. Moreover, the dataset includes mask labels of visible grape bunches. The videos were acquired throughout four UAV flights with an RGB camera tilted at 60 degrees. Each flight recorded one side of a row of the vineyard. The grape berries were between pea-size (BBCH75) and bunch closure (BBCH79) stage, which is two months before harvesting. No operations other than those usual in a commercial vineyard, such as pruning, cane tying, fertilization, and pest treatment, have been carried out, hence, the dataset presents leaf occlusion. The dataset was gathered and labelled to train object detection and tracking algorithms for grape bunch counting. Furthermore, it eases the work of winegrowers to check the sanitary status of the vineyard.

In-field hyperspectral imaging dataset of Manzanilla and Gordal olive varieties throughout the season

Because spectral technology has exhibited benefits in food-related applications, an increasing amount of effort is being dedicated to develop new food-related spectral technologies. In recent years, the use of remote sensing or unmanned aerial vehicles for precision agriculture has increased. As spectral technology continues to improve, portable spectral devices become available in the market, offering the possibility of realising in-field monitoring. This study demonstrates hyperspectral imaging and spectral olive signatures of the Manzanilla and Gordal cultivars analysed throughout the table-olive season from May to September. The data were acquired using an in-field technique and sampled via a non-destructive approach. The olives were monitored periodically during the season using a hyperspectral camera. A white reference was used to normalise the illumination variability in the spectra. The acquired data were saved in files named raw, normalised, and processed data. The normalised data were calculated by the sensor by correcting the white and black levels using the acquired reflectance values. The olive spectral signature of the images is saved in the processed data files. The images were labelled and processed using an algorithm to retrieve the olive spectral signatures. The results were stored as a chart with 204 columns and 'n' rows. Each row represents the pixel of an olive in the image, and the columns contain the reflectance information at that specific band. These data provide information about two olive cultivars during the season, which can be used for various research purposes. Statistical and artificial intelligence approaches correlate spectral signatures with olive characteristics such as growth level, organoleptic properties, or even cultivar classification.

Dataset for the determination of phosphorus in soil through the analysis of hyperspectral images

This article presents the capture protocol to acquire hyperspectral images, which can be used to quantify the concentration of total phosphorus in soil samples. 152 soil samples were prepared, and a hyperspectral cube made up of 145 images in the VIS-NIR bands, between 420 and 1000 nm, was obtained from each of them. The images obtained were taken with the Bayspec OCIF Series hyperspectral camera, in push-broom function, using a platform that includes an illumination system that offers a continuous spectrum in the range of interest. The samples were prepared with a soil from the Santander de Quilichao region, Cauca, Colombia, and mixed with known concentrations of P₂O₅ fertilizer, so that a total mass of 50 g was obtained. Each sample was deposited in a round black plastic container, 6 cm in diameter and a depth of 1 cm. The soil samples were analyzed in the laboratory to establish the concentration of total phosphorus. Therefore, the database is made up of the images associated with the hyperspectral cube of each sample, and four tables: the first describes the properties of the soil used to obtain the mixtures, the second the composition of the fertilizer used, the third describes the soil-fertilizer ratio to make up the samples, and the fourth was the laboratory analysis of the total phosphorus content of the analyzed samples.

Satellite imagery dataset of manure application on pasture fields

Applying manure to pasture fields is a very common method of fertilization. However, rainfall can cause the manure to leach into water bodies near the field, contaminating the water and damaging the environment and the animals living in it, ultimately affecting human life. This paper presents a dataset consisting of images of 30 plots after manure application, verified by on-site investigations. This involved visiting 38 different plots, of which 8 were discarded because they were not suitable, either because of their small size, the lack of a specific manure application date, or the images being too cloudy in that period. The imagery is collected through Google Earth Engine using the satellite Sentinel-2, which offers 13 hyperspectral bands in the range of ultraviolet and near-infrared wavelengths including the visible spectrum. From these 13 bands, the most common hyperspectral indices in the literature for precision agriculture are calculated and added into the images as channels. 51 hyperspectral indices are calculated, summing up to a total of 64 channels per image when adding the raw bands from Sentinel-2. No normalization has been performed on any of the channels. The data can be used for further research of automatic classification of manure application to control its use and prevent contamination.

Composite SOSM controller for path tracking control of agricultural tractors subject to wheel slip

In the work, the path tracking control methods are developed for autonomous agricultural tractors subject to wheel slip constraints by using second-order sliding mode (SOSM) and finite-time disturbance observer (FDOB) techniques. First of all, the path tracking error dynamics derived from the kinematic model is given and applied to controller design. In order to deal with the inevitable chattering problem existing in the conventional first-order sliding mode (FOSM) controller, a SOSM controller is constructed by regarding the controller derivative as the new control law, which means that the practical control law can be seen as an integration of the SOSM controller. Through a combination of the FDOB and the designed SOSM controller, the composite path tracking controller is further constructed to avoid high control gains in the designed SOSM controller. The strict Lyapunov stability analysis is carried out to ensure that the sliding variable can be finite-time stabilized to the origin under the proposed control algorithms. Finally, the comparative simulation results confirm that the developed guidance laws can achieve good tracking performance and strong robustness even in the presence of slipping effects.

UAV-spray application in vineyards: Flight modes and spray system adjustment effects on canopy deposit, coverage, and off-target losses

Improvements in the spray application of plant protection products enhance agricultural sustainability by reducing environmental contamination, but by increasing food quality and human safety. Currently, Unmanned Aerial Vehicles (UAVs) are raising interest in spray applications in 3D crops. However, operational configurations of UAV-spray systems need further investigation to maximise the deposition in the canopy and minimise the off-target losses. Our experimental research focused on investigating the effects on the canopy spray deposition and coverage due to different UAV-spray system configurations. Twelve configurations were tested under field conditions in an experimental vineyard (cv. Barbera), derived from the combination of different UAV flight modes (band and broadcast spray applications), nozzle types (conventional and air inclusion), and UAV cruise speeds (1 and 3 m s^-1). Also, the best treatment, among those tested, by using the UAV-spray system and a traditional airblast sprayer were compared. The data was analysed by testing the effects of the three operational parameters and their two- and three-way interactions by means of linear mixed models. The results indicated that the flight mode deeply affects spray application efficiency. Compared to the broadcast spray modes, the band spray mode was able to increase the average canopy deposition from 0.052 to 0.161 μL cm^-2 (+ 309 %) and reduce the average ground losses from 0.544 to 0.246 μL cm^-2 (- 54 %). The conventional airblast sprayer, operated at a low spray application rate, showed higher canopy coverage and lower ground losses in comparison to the best UAV-spray system configuration.

Multiparameter optimization system with DCNN in precision agriculture for advanced irrigation planning and scheduling based on soil moisture estimation

Agriculture is a distinct sector of a country's economy. In recent years, new patterns have evolved in the agricultural industry. In conjunction with sensor scaling down and precision agriculture, the field of remote sensor networks, such as the wireless sensor network (WSN), was developed. Its major purpose is to make horticultural operations simpler to identify, assess, and manage. This paper uses the proposed DCNN to predict soil moisture and plan irrigation for precision agriculture farmers to reduce water consumption used for cultivation and increase production yield by comparing water content during various stages of plant growth and integrating IoT applications into agriculture. It also optimizes the water level for future irrigation decisions to maintain crop growth and water stability. The data must be served and stored in the form of a grid view, according to Apriori and GRU (gated recurrent unit). Using numerous sensor and parameter modelling methodologies, this system assists in the prediction of irrigation planning based on irrigation needs. The predicted parameters include soil moisture, temperature, and humidity. This observed experimental data supports smart irrigation in crop production with a high yield and little water use. DCNN has a 98.5% experimental result accuracy rate and the MSE value is predicted in DCNN 99.25% of the time.

Vegetation detection using vegetation indices algorithm supported by statistical machine learning

In precision agriculture (PA), the usage of image processing, artificial intelligence, data analysis, and internet of things provides an increase in efficiency, energy, and time saving. In image processing-based applications, vegetation detection, in other words, segmentation that allows monitoring of plant growth and health as well as identification of weeds has a great importance. Vegetation indices (VIs) are widely used algorithms for segmentation. Their advantages include low computational cost and easy implementation and handling compared to the other algorithms. Nevertheless, they require a manual threshold detection that customizes the process and prevents generalization. In this study, a novel automatic segmentation method, which does not require a manual threshold detection by combining VIs with a classification algorithm, is proposed. It deals with the segmentation process as a two class classification problem (vegetation and background). As the classification algorithm, Discriminative Common Vector Approach (DCVA) that has a high discrimination power is used. Each image pixel is represented with a 3 × 1 dimensional vector whose elements correspond to Excess Green (ExG), Green minus Blue (GB), and Color Index of Vegetation (CIVE); VI values are obtained. Then, on the sample space accepting this pixel vector as a sample, DCVA is applied and a discriminative common vector for each class which is unique and describes that class in the best way possible is obtained and it is used for classification. Proposed segmentation method's performance is compared with Convolutional Neural Networks (CNN) and Random Forest (RF) algorithm. The proposed segmentation algorithm outperformed both CNN's and RF's performance.

Factors influencing intention to apply spatial approaches to on-farm experimentation: insights from the Australian winegrape sector

Grape growers are often constrained by available time and labor to conduct trials that deliver informative results. Spatially distributed trial designs coupled with data collection using sensing technologies can introduce efficiencies and also account for the impact of land variability on trial results. Various spatial approaches have been proposed, yet how farmers perceive them is largely unknown. We collaborated with four wine businesses in Australia to explore how grape growers and viticultural consultants perceive a simplified spatial approach to experimentation involving one or more vineyard rows or "strips." In each case, the simplified strip approach was applied alongside growers' or consultants' own methods to compare the perceived value of different methods. The Theory of Planned Behavior was used as an analytical framework to identify factors influencing participants' intentions towards adopting the strip approach. Our findings show that growers and consultants perceived several advantages of the strip approach over their own methods. Key factors impeding uptake were resource constraints for collecting trial data and lack of skills and knowledge to use and analyze spatial data to position the trial and interpret results. These constraints highlight the need to support growers and consultants who see value in this approach by developing automated and affordable measurements for viticultural variables beyond yield, and by providing training on how to analyze and interpret spatial and response data. This study provides novel insights for private and public sectors on where to focus efforts to facilitate adoption of spatial approaches to On-Farm Experimentation by specific target audiences.

A survey on deep learning-based identification of plant and crop diseases from UAV-based aerial images

The agricultural crop productivity can be affected and reduced due to many factors such as weeds, pests, and diseases. Traditional methods that are based on terrestrial engines, devices, and farmers' naked eyes are facing many limitations in terms of accuracy and the required time to cover large fields. Currently, precision agriculture that is based on the use of deep learning algorithms and Unmanned Aerial Vehicles (UAVs) provides an effective solution to achieve agriculture applications, including plant disease identification and treatment. In the last few years, plant disease monitoring using UAV platforms is one of the most important agriculture applications that have gained increasing interest by researchers. Accurate detection and treatment of plant diseases at early stages is crucial to improving agricultural production. To this end, in this review, we analyze the recent advances in the use of computer vision techniques that are based on deep learning algorithms and UAV technologies to identify and treat crop diseases.

Assessing spatial variability of selected soil properties in Upper Kabete Campus coffee farm, University of Nairobi, Kenya

This study aimed to evaluate spatial variability of selected soil parameters as a smart agricultural technology guide to precise fertilizer application. A farm designated as Field 3 which is under Arabica coffee within a bigger Soil Mapping Unit (SMU) was selected for a more detailed soil observation at a scale of 1:5000. Soil samples were taken at depths of 0–15 and 15–30 cm across 20 sample locations in grids and selected properties analysed in the laboratory. Kriging interpolation method was used to estimate the accuracy of interpolation through cross-validation of the top soil parameters. In 0 to 15 and 15–30 cm depth, soil reaction, percentage organic carbon and percent nitrogen showed low variability of 5.1% and 5.8%, 10.4% and 12.7%, 14.5% and 17.6% respectively. Phosphorus was deficient in both depths and showed moderate variability of 36.2% and 42.3% in 0–15 and 15–30 cm respectively. Calcium and Magnesium ranged from sufficient to rich and showed moderate and low variability in top and bottom depths, respectively. All micronutrients were sufficient in the soil. The soils were classified as Mollic Nitisols. Results showed that soil parameters varied spatially within the field therefore, there is need for variable input application depending on the levels of these elements and purchasing of fertilizer blends that are suitable for nutrient deficiencies. Precision agriculture is highly recommended in the field to capitalize on soil heterogeneity.

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There is need for variable agricultural input application in farms.

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Field spatial variability is a panacea to economically sound soil management.

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Precision agriculture is recommended for profits and environmental protection.

Radiative transfer model inversion using high-resolution hyperspectral airborne imagery - Retrieving maize LAI to access biomass and grain yield

Mapping crop within-field yield variability provide an essential piece of information for precision agriculture applications. Leaf Area Index (LAI) is an important parameter that describes maize growth, vegetation structure, light absorption and subsequently maize biomass and grain yield (GY). The main goal for this study was to estimate maize biomass and GY through LAI retrieved from hyperspectral aerial images using a PROSAIL model inversion and compare its performance with biomass and GY estimations through simple vegetation index approaches. This study was conducted in two separate maize fields of 12 and 20 ha located in north-west Mexico. Both fields were cultivated with the same hybrid. One field was irrigated by a linear pivot and the other by a furrow irrigation system. Ground LAI data were collected at different crop growth stages followed by maize biomass and GY at the harvesting time. Through a weekly/biweekly airborne flight campaign, a total of 19 mosaics were acquired between both fields with a micro-hyperspectral Vis-NIR imaging sensor ranging from 400 to 850 nanometres (nm) at different crop growth stages. The PROSAIL model was calibrated and validated for retrieving maize LAI by simulating maize canopy spectral reflectance based on crop-specific parameters. The model was used to retrieve LAI from both fields and to subsequently estimate maize biomass and GY. Additionally, different vegetation indices were calculated from the aerial images to also estimate maize yield and compare the indices with PROSAIL based estimations. The PROSAIL validation to retrieve LAI from hyperspectral imagery showed a R² value of 0.5 against ground LAI with RMSE of 0.8 m²/m². Maize biomass and GY estimation based on NDRE showed the highest accuracies, followed by retrieved LAI, GNDVI and NDVI with R² value of 0.81, 0.73, 0.73 and 0.65 for biomass, and 0.83, 0.69, 0.73 and 0.62 for GY estimation, respectively. Furthermore, the late vegetative growth stage at V16 was found to be the best stage for maize yield prediction for all studied indices.

Assessing expected utility and profitability to support decision-making for disease control strategies in ornamental heather production

Many farmers hesitate to adopt new management strategies with actual or perceived risks and uncertainties. Especially in ornamental plant production, farmers often stick to current production strategies to avoid the risk of economically harmful plant losses, even though they may recognize the need to optimize farm management. This work focused on the economically important and little-researched production system of ornamental heather (Calluna vulgaris) to help farmers find appropriate measures to sustainably improve resource use, plant quality, and profitability despite existing risks. Probabilistic cost-benefit analysis was applied to simulate alternative disease monitoring strategies. The outcomes for more intensive visual monitoring, as well as sensor-based monitoring using hyperspectral imaging were simulated. Based on the results of the probabilistic cost-benefit analysis, the expected utility of the alternative strategies was assessed as a function of the farmer's level of risk aversion. The analysis of expected utility indicated that heather production is generally risky. Concerning the alternative strategies, more intensive visual monitoring provides the highest utility for farmers for almost all levels of risk aversion compared to all other strategies. Results of the probabilistic cost-benefit analysis indicated that more intensive visual monitoring increases net benefits in 68% of the simulated cases. The application of sensor-based monitoring leads to negative economic outcomes in 85% of the simulated cases. This research approach is widely applicable to predict the impacts of new management strategies in precision agriculture. The methodology can be used to provide farmers in other data-scarce production systems with concrete recommendations that account for uncertainties and risks.

SUPPLEMENTARY INFORMATION

The online version contains supplementary material available at 10.1007/s11119-022-09909-z.

Impact of Climate Change on Dryland Agricultural Systems: A Review of Current Status, Potentials, and Further Work Need

Dryland agricultural system is under threat due to climate extremes and unsustainable management. Understanding of climate change impact is important to design adaptation options for dry land agricultural systems. Thus, the present review was conducted with the objectives to identify gaps and suggest technology-based intervention that can support dry land farming under changing climate. Careful management of the available agricultural resources in the region is a current need, as it will play crucial role in the coming decades to ensure food security, reduce poverty, hunger, and malnutrition. Technology based regional collaborative interventions among Universities, Institutions, Growers, Companies etc. for water conservation, supplemental irrigation, foliar sprays, integrated nutrient management, resilient crops-based cropping systems, artificial intelligence, and precision agriculture (modeling and remote sensing) are needed to support agriculture of the region. Different process-based models have been used in different regions around the world to quantify the impacts of climate change at field, regional, and national scales to design management options for dryland cropping systems. Modeling include water and nutrient management, ideotype designing, modification in tillage practices, application of cover crops, insect, and disease management. However, diversification in the mixed and integrated crop and livestock farming system is needed to have profitable, sustainable business. The main focus in this work is to recommend different agro-adaptation measures to be part of policies for sustainable agricultural production systems in future.

On-line monitoring of plant water status: Validation of a novel sensor based on photon attenuation of radiation through the leaf

Non-destructive real-time monitoring of leaf water status is important for precision irrigation practice to increase water productivity and reduce its use. To this end, we tested and validated a novel leaf sensor (Leaf Water Meter, LWM), based on the photon attenuation during the passage of the light through the leaf, to monitor plant water status. Four woody species were subjected to multiple cycles of dehydration and re-hydration, and the signals recorded by the LWM were compared with classical measurements of plant water relations (relative water content and water potential). A good agreement between the signals recorded by LWM sensor and the destructive measurements, throughout the repeated water stress and rewatering cycles, was found across all species. These results demonstrate that LWM sensor is a sensitive, non-destructive and easy-to-handle device to reliably monitor in continuous fashion leaf water status. In conclusion, this sensor may be considered a promising tool for smart irrigation scheduling in precision agriculture context to decrease water wastage in light of global change and increasing conflicts over water demand.

A loss function to evaluate agricultural decision-making under uncertainty: a case study of soil spectroscopy

Modern sensor technologies can provide detailed information about soil variation which allows for more precise application of fertiliser to minimise environmental harm imposed by agriculture. However, growers should lose neither income nor yield from associated uncertainties of predicted nutrient concentrations and thus one must acknowledge and account for uncertainties. A framework is presented that accounts for the uncertainty and determines the cost-benefit of data on available phosphorus (P) and potassium (K) in the soil determined from sensors. For four fields, the uncertainty associated with variation in soil P and K predicted from sensors was determined. Using published fertiliser dose-yield response curves for a horticultural crop the effect of estimation errors from sensor data on expected financial losses was quantified. The expected losses from optimal precise application were compared with the losses expected from uniform fertiliser application (equivalent to little or no knowledge on soil variation). The asymmetry of the loss function meant that underestimation of P and K generally led to greater losses than the losses from overestimation. This study shows that substantial financial gains can be obtained from sensor-based precise application of P and K fertiliser, with savings of up to £121 ha^-1 for P and up to £81 ha^-1 for K, with concurrent environmental benefits due to a reduction of 4-17 kg ha^-1 applied P fertiliser when compared with uniform application.

SUPPLEMENTARY INFORMATION

The online version contains supplementary material available at 10.1007/s11119-022-09887-2.

High-throughput phenotyping allows the selection of soybean genotypes for earliness and high grain yield

BACKGROUND

Precision agriculture techniques are widely used to optimize fertilizer and soil applications. Furthermore, these techniques could also be combined with new statistical tools to assist in phenotyping in breeding programs. In this study, the research hypothesis was that soybean cultivars show phenotypic differences concerning wavelength and vegetation index measurements.

RESULTS

In this research, we associate variables obtained via high-throughput phenotyping with the grain yield and cycle of soybean genotypes. The experiment was carried out during the 2018/2019 and 2019/2020 crop seasons, under a randomized block design with four replications. The evaluated soybean genotypes included 7067, 7110, 7739, 8372, Bonus, Desafio, Maracai, Foco, Pop, and Soyouro. The phenotypic traits evaluated were: first pod height (FPH), plant height (PH), number of branches (NB), stem diameter (SD), days to maturity (DM), and grain yield (YIE). The spectral variables evaluated were wavelengths and vegetation indices (NDVI, SAVI, GNDVI, NDRE, SCCCI, EVI, and MSAVI). The genotypes Maracai and Foco showed the highest grain yields throughout the crop seasons, in addition to belonging to the groups with the highest means for all VIs. YIE was positively correlated with the NDVI and certain wavelengths (735 and 790 nm), indicating that genotypes with higher values for these spectral variables are more productive. By path analyses, GNDVI and NDRE had the highest direct effects on the dependent variable DM, while NDVI had a higher direct effect on YIE.

CONCLUSIONS

Our findings revealed that early and productive genotypes can be selected based on vegetation indices and wavelengths. Soybean genotypes with a high grain yield have higher means for NDVI and certain wavelengths (735 and 790 nm). Early genotypes have higher means for NDRE and GNDVI. These results reinforce the importance of high-throughput phenotyping as an essential tool in soybean breeding programs.

Physical soil attributes in areas under forest/pasture conversion in northern Rondônia, Brazil

The main cause of physical degradation in pasture areas is overgrazing, and when combined with poorly productive soils, it causes the loss of millions of hectares of agricultural soils a year. Thus, work is needed to indicate which physical attributes are most sensitive to degradation, generating information so that soil management can be proposed, with a view to economic, social, and environmental aspects. Therefore, the objective of the work was to evaluate the impacts caused on the physical attributes of the soil, in forests converted to pastures in northern Rondônia, Brazil. The study was carried out in three areas within the municipality of Porto Velho, Rondônia, one area with forest and two with pastures (brachiaria and mombaça grass). In the field, deformed soil samples were collected at a depth of 0.00-0.10 and 0.10-0.20 m in the three study areas. In the laboratory, physical analyses of texture, aggregates and porosity, compaction, and an additional analysis of soil organic carbon were carried out. Then, univariate, bivariate, and multivariate analyses were performed, as well as geostatistical analysis. The conversion of forest to pasture had a negative impact on aggregates, compaction, porosity, and accumulation of organic carbon in the soil. The studied environments are influenced by the high levels of sand and clay, which interfere in the aggregation, compaction, porosity, and accumulation of organic carbon in the soil. We observed greater spatial variability of physical attributes in the environment with mombaça grass and attributed this to the greater grazing and trampling intensity of the animals.

PFuji-Size dataset: A collection of images and photogrammetry-derived 3D point clouds with ground truth annotations for Fuji apple detection and size estimation in field conditions

The PFuji-Size dataset is comprised of a collection of 3D point clouds of Fuji apple trees (Malus domestica Borkh. cv. Fuji) scanned at different maturity stages and annotated for fruit detection and size estimation. Structure-from-motion and multi-view stereo techniques were used to generate the 3D point clouds of 6 complete Fuji apple trees containing a total of 615 apples. The resulting point clouds were 3D segmented by identifying the 3D points corresponding to each apple (3D instance segmentation), obtaining a single point cloud for each apple. All segmented apples were labelled with ground truth diameter annotations. Since the data was acquired in field conditions and at different maturity stages, the set includes different fruit diameters -from 26.9 mm to 94.8 mm- and different fruit occlusion percentages due to foliage. In addition, 25 apples were photographed 360° in laboratory conditions, obtaining high resolution 3D point clouds of this sub-set. To the best of the authors' knowledge, this is the first publicly available dataset for apple size estimation in field conditions. This dataset was used to evaluate different fruit size estimation methods in the research article titled "In-field apple size estimation using photogrammetry-derived 3D point clouds: comparison of 4 different methods considering fruit occlusion" (Gené-Mola et al., 2021).

Using unmanned aerial systems and deep learning for agriculture mapping in Dubai

As part of the sustainable future vision, sustainable agriculture has become an essential pillar of the food security strategies formulated by the Dubai Government. Therefore, the Dubai Emirate began relying on new technology to increase productivity and efficiency. Agriculture applications also depend on accurate land monitoring for timely food security control and support actions. However, traditional monitoring requires field surveys to be performed by experts, which is costly, slow, and rare. Agriculture monitoring systems must be furnished with sustainable land use monitoring solutions, starting with remote sensing using drone surveys for affordable, efficient, and time-sensitive agriculture mapping. Hence, the Dubai Municipality is currently using Unmanned Aerial Vehicles (UAVs) to map the farming areas all over the Emirate, support locating lands conducive to cultivation, and create an accurate agriculture database contributing to the decision-making process in determining areas suitable for crop growth. This study used a novel object detection method coupled with geospatial analysis as an integrated workflow to detect individual crops. The UAV flights were executed using a Trimble UX5 (HP) over twelve communities across the Dubai Emirate for six months. Detection methods were applied to high-resolution drone images, consisting of RGB and near-infrared (NIR) bands. Advanced geoprocessing tools were also used to analyze, evaluate, and enhance the results. The performance of detection of the selected deep learning models are discussed (vegetation cover accuracy = 85.4%, F1-scores for date palms and ghaf trees = 96.03% and 94.54% respectively, with respect to visual interpretation ground truth); moreover, sample images from the datasets are used for demonstrations. The main aim is to offer specialists a solution for measuring and assessing living green vegetation cover derived from the processed images that is integrated. The results provide insight into using UAS and deep learning algorithms as a solution for sustainable agricultural mapping on a large scale.

MOF-based multi-stimuli-responsive supramolecular nanoplatform equipped with macrocycle nanovalves for plant growth regulation

Controllable and on-demand delivery of agrochemicals such as plant hormones is conducive to improving agrochemicals utilization, tackling water and environmental pollution, reducing soil acidification, and realizing the goals of precision agriculture. Herein, a smart plant hormone delivery system based on metal-organic frameworks (MOFs) and supramolecular nanovalves, namely gibberellin (GA)-loaded CLT6@PCN-Q, is constructed through supramolecular host-guest interaction to regulate the growth of dicotyledonous Chinese cabbage and monocotyledonous wheat. The porous nanoscale MOF (NMOF) with a uniform diameter of 97 nm modified by quaternary ammonium (Q) stalks is served as a cargo reservoir, followed by the decoration of carboxylated leaning tower[6]arene (CLT6) based nanovalves on NMOF surfaces through host-guest interactions to fabricate CLT6@PCN-Q with a diameter of ∼101 nm and a zeta potential value of -13.2 mV. Interestingly, the as-fabricated supramolecular nanoplatform exhibits efficient cargo loading and multi-stimuli-responsive release under various external stimuli including pH, temperature, and competitive agent spermine (SPM), which can realize the on-demand release of cargo. In addition, GA-loaded CLT6@PCN-Q is capable of effectively promoting the seeds germination of wheat and stem growth of dicotyledonous Chinese cabbage and monocotyledonous wheat (1.86 and 1.30 times of control groups, respectively). The smart supramolecular nanoplatform based on MOFs and supramolecular nanovalves paves a way for the controlled delivery of plant hormones and other agrochemicals for promoting plant growth, offering new insights and methods to realize precision agriculture. STATEMENT OF SIGNIFICANCE: To achieve controllable and sustainable release of cargos such as agrochemicals, a smart MOF-based multi-stimuli-responsive supramolecular nanoplatform equipped with supramolecular nanovalves was fabricated via the host-guest interaction between quaternary ammonium stalks-functionalized nanoMOFs and water-soluble leaning tower[6]arene. The as-prepared supramolecular nanoplatform with uniform diameter distribution demonstrated good cargo release in response to various external stimuli. The installation of synthetic macrocycles could effectively reduce cargo loss in the pre-treatment process. This type of supramolecular nanoplatform exhibited good promoting effect on seed germination and plant growth dicotyledonous Chinese cabbage and monocotyledonous wheat. As an eco-friendly, controlled, and efficient cargo delivery system, this supramolecular nanoplatform will be a promising candidate in precision agriculture and controlled drug release to attract the broad readership.

Large-scale data analysis for robotic yeast one-hybrid platforms and multi-disciplinary studies using GateMultiplex

BACKGROUND

Yeast one-hybrid (Y1H) is a common technique for identifying DNA-protein interactions, and robotic platforms have been developed for high-throughput analyses to unravel the gene regulatory networks in many organisms. Use of these high-throughput techniques has led to the generation of increasingly large datasets, and several software packages have been developed to analyze such data. We previously established the currently most efficient Y1H system, meiosis-directed Y1H; however, the available software tools were not designed for processing the additional parameters suggested by meiosis-directed Y1H to avoid false positives and required programming skills for operation.

RESULTS

We developed a new tool named GateMultiplex with high computing performance using C++. GateMultiplex incorporated a graphical user interface (GUI), which allows the operation without any programming skills. Flexible parameter options were designed for multiple experimental purposes to enable the application of GateMultiplex even beyond Y1H platforms. We further demonstrated the data analysis from other three fields using GateMultiplex, the identification of lead compounds in preclinical cancer drug discovery, the crop line selection in precision agriculture, and the ocean pollution detection from deep-sea fishery.

CONCLUSIONS

The user-friendly GUI, fast C++ computing speed, flexible parameter setting, and applicability of GateMultiplex facilitate the feasibility of large-scale data analysis in life science fields.

Spatially variable pesticide application in olive groves: Evaluation of potential pesticide-savings through stochastic spatial simulation algorithms

Site-specific management using spatial crown volume characterization can greatly reduce the amount of pesticides applied in agricultural treatments performed with air-assisted sprayers, while helping farmers achieve the European legislation on safe use of pesticides. Nevertheless, variable rate treatments in olive groves have received little attention. Thus, field research was conducted in a 20.6-ha traditional olive grove. Two attributes of the trees - tree crown volume (V) and tree projected area - were determined, using 67 samples for V and all trees of the field (1433) for tree projected area. Spatial continuity of both attributes was modelled with exponential variograms. To gain a measure of local uncertainty, stochastic simulation algorithms were applied. One hundred simulated images were obtained for tree projected area using direct sequential simulation. Tree projected area simulations were used to improve spatial prediction of V, more difficult and more expensive to obtain, taking advantage of the high linear correlation between both variables (r_xy = 0.72,p < 0.001). Thus, direct sequential cosimulation was employed to predict the spatial distribution of V, obtaining 100 geostatistical realizations of V. In order to estimate the potential reduction of pesticide use in the farm with variable rate treatments, two cut-off values of V were considered (50 and 100 m³_{crown volume}). Local uncertainty, understood as the probability of each tree belonging to a given crown volume interval was determined. Probability maps were further transformed to morphological maps and finally to variable prescription maps. Two scenarios with 2 and 3 management zones (MZs) were obtained. In comparison with a conventional phytosanitary application, the variable rate treatments could reduce the pesticide amounts by 21.3% with 2 MZs, and by 38% with 3 MZs. The joint use of V and tree projected area in stochastic sequential simulation algorithms has shown to be useful to determine MZs in olive groves.