Updating "machine learning imagery dataset for maize crop: A case of Tanzania" with expanded data to cover the new farming season

Maize Lethal Necrosis (MLN) and Maize Streak Virus (MSV) are among maize diseases which affect productivity in Tanzania and Africa at large. These diseases can be detected early for timely interventions and minimal losses. Machine learning (ML) has emerged as a powerful tool for automated diseases detection, offering several advantages over traditional methods. This article presents the updated dataset of 9356 imagery maize leaves to assist researchers in developing technological solutions for addressing crop diseases. The high-resolution imagery data presented in this dataset were captured using smartphone cameras in farm fields which were not selected in the previously published dataset. Also, data collection was taken in the range of three months from November 2022 to January 2023 to incorporate farming season not covered in the previously published dataset. The presented dataset can be used by researchers in the field of Artificial Intelligence (AI) to develop ML solutions and eliminate the need of manual inspection and reduce human bias. Developing ML solutions require large amount of data therefore, the updated and previously published datasets can be combined to accommodate diverse and wider applicability.

Panorama of biogenic nano-fertilizers: A road to sustainable agriculture

The ever-increasing demand for food from the growing population has augmented the consumption of fertilizers in global agricultural practices. However, the excessive usage of chemical fertilizers with poor efficacy is drastically deteriorating ecosystem health through the degradation of soil fertility by diminishing soil microflora, environment contamination, and human health by inducing chemical remnants to the food chain. These challenges have been addressed by the integration of nanotechnological and biotechnological approaches resulting in nano-enabled biogenic fertilizers (NBF), which have revolutionized agriculture sector and food production. This review critically details the state-of-the-art NBF production, types, and mechanism involved in cultivating crop productivity/quality with insights into genetic, physiological, morphological, microbiological, and physiochemical attributes. Besides, it explores the associated challenges and future routes to promote the adoption of NBF for intelligent and sustainable agriculture. Furthermore, diverse applications of nanotechnology in precision agriculture including plant biosensors and its impact on agribusiness and environmental management are discussed.

A labeled spectral dataset with cassava disease occurrences using virus titre determination protocol

In this work, we present a novel dataset composed of spectral data and images of cassava crops with and without diseases. Together with the description of the dataset, we describe the protocol to collect such data in a controlled environment and in an open field where pests are not controlled. Crop disease diagnosis has been done in the past through the analysis of plant images taken with a smartphone camera. However, in some cases, disease symptoms are not visible. Furthermore, for some cassava diseases, once symptoms have manifested on the aerial part of the plant, the root which is the edible part of the plant has been totally destroyed. The goal of collecting this multimodality of the crop disease is early intervention, following the hypothesis that diseased crops without visible symptoms can be detected using spectral information. We collected visible and near-infrared spectra captured from leaves infected with two common cassava diseases namely; Cassava Brown Streak Disease and Cassava Mosaic Disease, as well as from healthy plants. Together, we also captured leaf imagery data that corresponds to the spectral information. In our experiments, biochemical data is collected and taken as the ground truth. Finally, agricultural experts provided a disease score per plant leaf from 1 to 5, 1 representing healthy and 5 severely diseased. The process of disease monitoring and data collection took 19 and 15 consecutive weeks for screenhouse and open field, respectively, until disease symptoms were visibly seen by the human eye.

CocoaMFDB: A dataset of cocoa pod maturity and families in an uncontrolled environment in Côte d'Ivoire

Cocoa cultivation is the basis for chocolate production; it has a unique aroma that makes it useful in the production of snacks and usable for cooking or baking. The maximum harvest period of cocoa is normally once or twice a year and spread over several months, depending on the country. Determining the best harvesting period for cocoa pods plays a major role in the export process and the pods quality. The degree of ripening of the pods affects the quality of the resulting beans. Also, unripe pods do not have enough sugar and may prevent proper bean fermentation. As for too-mature pods, they are usually dry, and their beans may germinate inside the pods, or they may develop a fungal disease and cannot be used. Computer-based determination of the ripeness of cocoa pods throughout image analysis could facilitate massive cocoa ripeness detection. Recent technological advances in computing power, communication systems, and machine learning techniques provide opportunities for agricultural engineering and computer scientists to meet the demands of the manual. The need for diverse and representative sets of pod images is essential for developing and testing automatic cocoa pod maturity detection systems. In this perspective, we collected images of cocoa pods to set up a database of cocoa pods of the Côte d'Ivoire named CocoaMFDB. We performed a pre-processing step using the CLAHE algorithm to improve the quality of the images since the effect of the light was not controlled on our data set. CocoaMFDB allows the characterization of cocoa pods according to their maturity level and provides information on the pod family for each image. Our dataset comprises three large families, namely Amelonado, Angoleta, and Guiana, grouped into two maturity categories: the ripe and unripe pods. It is, therefore, perfect for developing and evaluating image analysis algorithms for future research.

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.

The prediction of collective Economic development based on the PSO-LSTM model in smart agriculture

Smart agriculture can promote the rural collective economy's resource coordination and market access through the Internet of Things and artificial intelligence technology and guarantee the collective economy's high-quality, sustainable development. The collective agricultural economy (CAE) is non-linear and uncertain due to regional weather, policy and other reasons. The traditional statistical regression model has low prediction accuracy and weak generalization ability on such issues. This article proposes a production prediction method using the particle swarm optimization-long short term memory (PSO-LSTM) model to predict CAE. Specifically, the LSTM method in the deep recurrent neural network is applied to predict the regional CAE. The PSO algorithm is utilized to optimize the model to improve global accuracy. The experimental results demonstrate that the PSO-LSTM method performs better than LSTM without parameter optimization and the traditional machine learning methods by comparing the RMSE and MAE evaluation index. This proves that the proposed model can provide detailed data references for the development of CAE.

A new deep learning-based technique for rice pest detection using remote sensing

Background

Agriculture plays a vital role in the country's economy and human society. Rice production is mainly focused on financial improvements as it is demanding worldwide. Protecting the rice field from pests during seedling and after production is becoming a challenging research problem. Identifying the pest at the right time is crucial so that the measures to prevent rice crops from pests can be taken by considering its stage. In this article, a new deep learning-based pest detection model is proposed. The proposed system can detect two types of rice pests (stem borer and Hispa) using an unmanned aerial vehicle (UAV).

Methodology

The image is captured in real time by a camera mounted on the UAV and then processed by filtering, labeling, and segmentation-based technique of color thresholding to convert the image into greyscale for extracting the region of interest. This article provides a rice pests dataset and a comparative analysis of existing pre-trained models. The proposed approach YO-CNN recommended in this study considers the results of the previous model because a smaller network was regarded to be better than a bigger one. Using additional layers has the advantage of preventing memorization, and it provides more precise results than existing techniques.

Results

The main contribution of the research is implementing a new modified deep learning model named Yolo-convolution neural network (YO-CNN) to obtain a precise output of up to 0.980 accuracies. It can be used to reduce rice wastage during production by monitoring the pests regularly. This technique can be used further for target spraying that saves applicators (fertilizer water and pesticide) and reduces the adverse effect of improper use of applicators on the environment and human beings.

IoT technologies in smart environment: security issues and future enhancements

IoT plays an important role in the overall development and advancement of the country as it is the key ingredient for the development of the smart environment. IoT is a network of physical objects, devices that contain embedded technologies such as sensors, controllers, etc., which can sense, communicate, and interact with the system to carry out desired operations. The advancement in technology over the past years has provided a new era for computational processing and sensing to facilitate the vision of a smart environment. Researchers have put several efforts to use IoT to facilitate our lives. This paper purposes on an integrated smart environment using IoT. Various sectors such as agriculture, transportation, garbage collection, security issues, sensors, etc. are discussed along with the key technologies including RFID, IP, EPC, Wi-Fi, Bluetooth, and ZigBee. This paper will provide a complete insight into the one who wants to research in the field of IoT. It also highlights the unprecedented opportunities brought by IoT-based technologies to human life. Finally, we have discussed the future enhancements in IoT.

Low cost climate station for smart agriculture applications with photovoltaic energy and wireless communication

Measuring climatic conditions is a fundamental task for a wide array of scientific and practical fields. Weather variables change depending on position and time, especially in tropical zones without seasons. Additionally, the increasing development of precision or smart agriculture makes it necessary to improve the measurement systems while widely distributing them at the location of crops. For these reasons, in this work, the design, construction and fabrication of an adaptable autonomous solar-powered climatic station with wireless 3G or WiFi communication is presented. The station measures relative humidity, temperature, atmospheric pressure, precipitation, wind speed, and light radiation. In addition, the system monitors the charge state of the main battery and the energy generated by the photovoltaic module to act as a reference cell for solar energy generation capability and agrivoltaic potential in the installation area. The station can be remotely controlled and reconfigured. The collected data from all sensors can be uploaded to the cloud in real-time. This initiative aims at enhancing the development of free and open source hardware that can be used by the agricultural sector and that allows professionals in the area to improve harvest yield and production conditions.

Design and implementation of 3-D printed radiation shields for environmental sensors

The measurement of outdoor environmental and climatic variables is needed for many applications such as precision agriculture, environmental pollution monitoring, and the study of ecosystems. Some sensors deployed for these purposes such as temperature, relative humidity, atmospheric pressure, and carbon dioxide sensors require protection from climate factors to avoid bias. Radiation shields hold and protect sensors to avoid this bias, but commercial systems are limited, often expensive, and difficult to implement in low-cost contexts or large deployments for collaborative sensing. To overcome these challenges, this work presents an open source, easily adapted and customized design of a radiation shield. The device can be fabricated with inexpensive off-the-shelf parts and 3-D printed components and can be adapted to protect and isolate different types of sensors. Two material approaches are tested here: polylactic acid (PLA), the most common 3-D printing filament, and acrylonitrile styrene acrylate (ASA), which is known to offer better resistance against UV radiation, greater hardness, and generally higher resistance to degradation. To validate the designs, the two prototypes were installed on a custom outdoor meteorological system and temperature and humidity measurements were made in several locations for one month and compared against a proprietary system and a system with no shield. The 3-D printed materials were also both tested multiple times for one month for UV stability of their mechanical properties, their optical transmission and deformation under outdoor high-heat conditions. The results showed that ASA is the preferred material for this design and that the open source radiation shield could match the performance of proprietary systems. The open source system can be constructed for about nine US dollars, which enables mass development of flexible weather stations for monitoring needed in smart agriculture.

Discussion paper: Sustainable increase of crop production through improved technical strategies, breeding and adapted management - A European perspective

During the next decade it will be necessary to develop novel combinations of management strategies to sustainably increase crop production and soil resilience. Improving agricultural productivity, while conserving and enhancing biotic and abiotic resources, is an essential requirement to increase global food production on a sustainable basis. The role of farmers in increasing agricultural productivity growth sustainably will be crucial. Farmers are at the center of any process of change involving natural resources and for this reason they need to be encouraged and guided, through appropriate incentives and governance practices, to conserve natural ecosystems and their biodiversity, and minimize the negative impact agriculture can have on the environment. Farmers and stakeholders need to revise traditional approaches not as productive as the modern approaches but more friendly with natural and environmental ecosystems values as well as emerging novel tools and approaches addressing precise farming, organic amendments, lowered water consumption, integrated pest control and beneficial plant-microbe interactions. While practical solutions are developing, science based recommendations for crop rotations, breeding and harvest/postharvest strategies leading to environmentally sound and pollinator friendly production and better life in rural areas have to be provided.