Field phenotyping for African crops: overview and perspectives

Improvements in crop productivity are required to meet the dietary demands of the rapidly-increasing African population. The development of key staple crop cultivars that are high-yielding and resilient to biotic and abiotic stresses is essential. To contribute to this objective, high-throughput plant phenotyping approaches are important enablers for the African plant science community to measure complex quantitative phenotypes and to establish the genetic basis of agriculturally relevant traits. These advances will facilitate the screening of germplasm for optimum performance and adaptation to low-input agriculture and resource-constrained environments. Increasing the capacity to investigate plant function and structure through non-invasive technologies is an effective strategy to aid plant breeding and additionally may contribute to precision agriculture. However, despite the significant global advances in basic knowledge and sensor technology for plant phenotyping, Africa still lags behind in the development and implementation of these systems due to several practical, financial, geographical and political barriers. Currently, field phenotyping is mostly carried out by manual methods that are prone to error, costly, labor-intensive and may come with adverse economic implications. Therefore, improvements in advanced field phenotyping capabilities and appropriate implementation are key factors for success in modern breeding and agricultural monitoring. In this review, we provide an overview of the current state of field phenotyping and the challenges limiting its implementation in some African countries. We suggest that the lack of appropriate field phenotyping infrastructures is impeding the development of improved crop cultivars and will have a detrimental impact on the agricultural sector and on food security. We highlight the prospects for integrating emerging and advanced low-cost phenotyping technologies into breeding protocols and characterizing crop responses to environmental challenges in field experimentation. Finally, we explore strategies for overcoming the barriers and maximizing the full potential of emerging field phenotyping technologies in African agriculture. This review paper will open new windows and provide new perspectives for breeders and the entire plant science community in Africa.

Genomics and phenomics enabled prebreeding improved early-season chilling tolerance in Sorghum

In temperate climates, earlier planting of tropical-origin crops can provide longer growing seasons, reduce water loss, suppress weeds, and escape post-flowering drought stress. However, chilling sensitivity of sorghum, a tropical-origin cereal crop, limits early planting, and over 50 years of conventional breeding has been stymied by coinheritance of chilling tolerance (CT) loci with undesirable tannin and dwarfing alleles. In this study, phenomics and genomics-enabled approaches were used for prebreeding of sorghum early-season CT. Uncrewed aircraft systems (UAS) high-throughput phenotyping platform tested for improving scalability showed moderate correlation between manual and UAS phenotyping. UAS normalized difference vegetation index values from the chilling nested association mapping population detected CT quantitative trait locus (QTL) that colocalized with manual phenotyping CT QTL. Two of the 4 first-generation Kompetitive Allele Specific PCR (KASP) molecular markers, generated using the peak QTL single nucleotide polymorphisms (SNPs), failed to function in an independent breeding program as the CT allele was common in diverse breeding lines. Population genomic fixation index analysis identified SNP CT alleles that were globally rare but common to the CT donors. Second-generation markers, generated using population genomics, were successful in tracking the donor CT allele in diverse breeding lines from 2 independent sorghum breeding programs. Marker-assisted breeding, effective in introgressing CT allele from Chinese sorghums into chilling-sensitive US elite sorghums, improved early-planted seedling performance ratings in lines with CT alleles by up to 13-24% compared to the negative control under natural chilling stress. These findings directly demonstrate the effectiveness of high-throughput phenotyping and population genomics in molecular breeding of complex adaptive traits.

Determination of Sugars and Acids in Grape Must Using Miniaturized Near-Infrared Spectroscopy

An automatic determination of grape must ingredients during the harvesting process would support cellar logistics and enables an early termination of the harvest if quality parameters are not met. One of the most important quality-determining characteristics of grape must is its sugar and acid content. Among others, the sugars in particular determine the quality of the must and wine. Chiefly in wine cooperatives, in which a third of all German winegrowers are organized, these quality characteristics serve as the basis for payment. They are acquired upon delivery at the cellar of the cooperative or the winery and result in the acceptance or rejection of grapes and must. The whole process is very time-consuming and expensive, and sometimes grapes that do not meet the quality requirements for sweetness, acidity, or healthiness are destroyed or not used at all, which leads to economic loss. Near-infrared spectroscopy is now a widely used technique to detect a wide variety of ingredients in biological samples. In this study, a miniaturized semi-automated prototype apparatus with a near-infrared sensor and a flow cell was used to acquire spectra (1100 nm to 1350 nm) of grape must at defined temperatures. Data of must samples from four different red and white Vitis vinifera (L.) varieties were recorded throughout the whole growing season of 2021 in Rhineland Palatinate, Germany. Each sample consisted of 100 randomly sampled berries from the entire vineyard. The contents of the main sugars (glucose and fructose) and acids (malic acid and tartaric acid) were determined with high-performance liquid chromatography. Chemometric methods, using partial least-square regression and leave-one-out cross-validation, provided good estimates of both sugars (RMSEP = 6.06 g/L, R² = 89.26%), as well as malic acid (RMSEP = 1.22 g/L, R² = 91.10%). The coefficient of determination (R²) was comparable for glucose and fructose with 89.45% compared to 89.08%, respectively. Although tartaric acid was predictable for only two of the four varieties using near-infrared spectroscopy, calibration and validation for malic acid were accurate for all varieties in an equal extent like the sugars. These high prediction accuracies for the main quality determining grape must ingredients using this miniaturized prototype apparatus might enable an installation on a grape harvester in the future.

Effects of irrigation on root growth and development of soybean: A 3-year sandy field experiment

Increasing the water use efficiency of crops is an important agricultural goal closely related to the root system -the primary plant organ for water and nutrient acquisition. In an attempt to evaluate the response of root growth and development of soybean to water supply levels, 200 genotypes were grown in a sandy field for 3 years under irrigated and non-irrigated conditions, and 14 root traits together with shoot fresh weight and plant height were investigated. Three-way ANOVA revealed a significant effect of treatments and years on growth of plants, accounting for more than 80% of the total variability. The response of roots to irrigation was consistent over the years as most root traits were improved by irrigation. However, the actual values varied between years because the growth of plants was largely affected by the field microclimatic conditions (i.e., temperature, sunshine duration, and precipitation). Therefore, the best linear unbiased prediction values for each trait were calculated using the original data. Principal component analysis showed that most traits contributed to principal component (PC) 1, whereas average diameter, the ratio of thin and medium thickness root length to total root length contributed to PC2. Subsequently, we focused on selecting genotypes that exhibited significant improvements in root traits under irrigation than under non-irrigated conditions using the increment (I-index) and relative increment (RI-index) indices calculated for all traits. Finally, we screened for genotypes with high stability and root growth over the 3 years using the multi-trait selection index (MTSI).Six genotypes namely, GmJMC130, GmWMC178, GmJMC092, GmJMC068, GmWMC075, and GmJMC081 from the top 10% of genotypes scoring MTSI less than the selection threshold of 7.04 and 4.11 under irrigated and non-irrigated conditions, respectively, were selected. The selected genotypes have great potential for breeding cultivars with improved water usage abilities, meeting the goal of water-saving agriculture.

High-throughput field crop phenotyping: current status and challenges

In contrast to the rapid advances made in plant genotyping, plant phenotyping is considered a bottleneck in plant science. This has promoted high-throughput plant phenotyping (HTP) studies, resulting in an exponential increase in phenotyping-related publications. The development of HTP was originally intended for use as indoor HTP technologies for model plant species under controlled environments. However, this subsequently shifted to HTP for use in crops in fields. Although HTP in fields is much more difficult to conduct due to unstable environmental conditions compared to HTP in controlled environments, recent advances in HTP technology have allowed these difficulties to be overcome, allowing for rapid, efficient, non-destructive, non-invasive, quantitative, repeatable, and objective phenotyping. Recent HTP developments have been accelerated by the advances in data analysis, sensors, and robot technologies, including machine learning, image analysis, three dimensional (3D) reconstruction, image sensors, laser sensors, environmental sensors, and drones, along with high-speed computational resources. This article provides an overview of recent HTP technologies, focusing mainly on canopy-based phenotypes of major crops, such as canopy height, canopy coverage, canopy biomass, and canopy stressed appearance, in addition to crop organ detection and counting in the fields. Current topics in field HTP are also presented, followed by a discussion on the low rates of adoption of HTP in practical breeding programs.

Review: Application of Artificial Intelligence in Phenomics

Plant phenomics has been rapidly advancing over the past few years. This advancement is attributed to the increased innovation and availability of new technologies which can enable the high-throughput phenotyping of complex plant traits. The application of artificial intelligence in various domains of science has also grown exponentially in recent years. Notably, the computer vision, machine learning, and deep learning aspects of artificial intelligence have been successfully integrated into non-invasive imaging techniques. This integration is gradually improving the efficiency of data collection and analysis through the application of machine and deep learning for robust image analysis. In addition, artificial intelligence has fostered the development of software and tools applied in field phenotyping for data collection and management. These include open-source devices and tools which are enabling community driven research and data-sharing, thereby availing the large amounts of data required for the accurate study of phenotypes. This paper reviews more than one hundred current state-of-the-art papers concerning AI-applied plant phenotyping published between 2010 and 2020. It provides an overview of current phenotyping technologies and the ongoing integration of artificial intelligence into plant phenotyping. Lastly, the limitations of the current approaches/methods and future directions are discussed.

Remote Sensing Energy Balance Model for the Assessment of Crop Evapotranspiration and Water Status in an Almond Rootstock Collection

One of the objectives of many studies conducted by breeding programs is to characterize and select rootstocks well-adapted to drought conditions. In recent years, field high-throughput phenotyping methods have been developed to characterize plant traits and to identify the most water use efficient varieties and rootstocks. However, none of these studies have been able to quantify the behavior of crop evapotranspiration in almond rootstocks under different water regimes. In this study, remote sensing phenotyping methods were used to assess the evapotranspiration of almond cv. "Marinada" grafted onto a rootstock collection. In particular, the two-source energy balance and Shuttleworth and Wallace models were used to, respectively, estimate the actual and potential evapotranspiration of almonds grafted onto 10 rootstock under three different irrigation treatments. For this purpose, three flights were conducted during the 2018 and 2019 growing seasons with an aircraft equipped with a thermal and multispectral camera. Stem water potential (Ψ _{s t e m} ) was also measured concomitant to image acquisition. Biophysical traits of the vegetation were firstly assessed through photogrammetry techniques, spectral vegetation indices and the radiative transfer model PROSAIL. The estimates of canopy height, leaf area index and daily fraction of intercepted radiation had root mean square errors of 0.57 m, 0.24 m m^-1 and 0.07%, respectively. Findings of this study showed significant differences between rootstocks in all of the evaluated parameters. Cadaman^® and Garnem^® had the highest canopy vigor traits, evapotranspiration, Ψ _{s t e m} and kernel yield. In contrast, Rootpac^® 20 and Rootpac^® R had the lowest values of the same parameters, suggesting that this was due to an incompatibility between plum-almond species or to a lower water absorption capability of the rooting system. Among the rootstocks with medium canopy vigor, Adesoto and IRTA 1 had a lower evapotranspiration than Rootpac^® 40 and Ishtara^®. Water productivity (WP) (kg kernel/mm water evapotranspired) tended to decrease with Ψ _{s t e m} , mainly in 2018. Cadaman^® and Garnem^® had the highest WP, followed by INRA GF-677, IRTA 1, IRTA 2, and Rootpac^® 40. Despite the low Ψ _{s t e m} of Rootpac^® R, the WP of this rootstock was also high.

Temperature response of wheat affects final height and the timing of stem elongation under field conditions

In wheat, temperature affects the timing and intensity of stem elongation. Genetic variation for this process is therefore important for adaptation. This study investigates the genetic response to temperature fluctuations during stem elongation and its relationship to phenology and height. Canopy height of 315 wheat genotypes (GABI wheat panel) was scanned twice weekly in the field phenotyping platform (FIP) of ETH Zurich using a LIDAR. Temperature response was modelled using linear regressions between stem elongation and mean temperature in each measurement interval. This led to a temperature-responsive (slope) and a temperature-irresponsive (intercept) component. The temperature response was highly heritable (H2=0.81) and positively related to a later start and end of stem elongation as well as final height. Genome-wide association mapping revealed three temperature-responsive and four temperature-irresponsive quantitative trait loci (QTLs). Furthermore, putative candidate genes for temperature-responsive QTLs were frequently related to the flowering pathway in Arabidopsis thaliana, whereas temperature-irresponsive QTLs corresponded to growth and reduced height genes. In combination with Rht and Ppd alleles, these loci, together with the loci for the timing of stem elongation, accounted for 71% of the variability in height. This demonstrates how high-throughput field phenotyping combined with environmental covariates can contribute to a smarter selection of climate-resilient crops.

Temperature response of wheat affects final height and the timing of stem elongation under field conditions

In wheat, temperature affects the timing and intensity of stem elongation. Genetic variation for this process is therefore important for adaptation. This study investigates the genetic response to temperature fluctuations during stem elongation and its relationship to phenology and height. Canopy height of 315 wheat genotypes (GABI wheat panel) was scanned twice weekly in the field phenotyping platform (FIP) of ETH Zurich using a LIDAR. Temperature response was modelled using linear regressions between stem elongation and mean temperature in each measurement interval. This led to a temperature-responsive (slope) and a temperature-irresponsive (intercept) component. The temperature response was highly heritable (H2=0.81) and positively related to a later start and end of stem elongation as well as final height. Genome-wide association mapping revealed three temperature-responsive and four temperature-irresponsive quantitative trait loci (QTLs). Furthermore, putative candidate genes for temperature-responsive QTLs were frequently related to the flowering pathway in Arabidopsis thaliana, whereas temperature-irresponsive QTLs corresponded to growth and reduced height genes. In combination with Rht and Ppd alleles, these loci, together with the loci for the timing of stem elongation, accounted for 71% of the variability in height. This demonstrates how high-throughput field phenotyping combined with environmental covariates can contribute to a smarter selection of climate-resilient crops.

Automatic wheat ear counting using machine learning based on RGB UAV imagery

In wheat (Triticum aestivum L) and other cereals, the number of ears per unit area is one of the main yield-determining components. An automatic evaluation of this parameter may contribute to the advance of wheat phenotyping and monitoring. There is no standard protocol for wheat ear counting in the field, and moreover it is time consuming. An automatic ear-counting system is proposed using machine learning techniques based on RGB (red, green, blue) images acquired from an unmanned aerial vehicle (UAV). Evaluation was performed on a set of 12 winter wheat cultivars with three nitrogen treatments during the 2017-2018 crop season. The automatic system uses a frequency filter, segmentation and feature extraction, with different classification techniques, to discriminate wheat ears in micro-plot images. The relationship between the image-based manual counting and the algorithm counting exhibited high levels of accuracy and efficiency. In addition, manual ear counting was conducted in the field for secondary validation. The correlations between the automatic and the manual in-situ ear counting with grain yield were also compared. Correlations between the automatic ear counting and grain yield were stronger than those between manual in-situ counting and GY, particularly for the lower nitrogen treatment. Methodological requirements and limitations are discussed.

Phenotyping: New Windows into the Plant for Breeders

Plant phenotyping enables noninvasive quantification of plant structure and function and interactions with environments. High-capacity phenotyping reaches hitherto inaccessible phenotypic characteristics. Diverse, challenging, and valuable applications of phenotyping have originated among scientists, prebreeders, and breeders as they study the phenotypic diversity of genetic resources and apply increasingly complex traits to crop improvement. Noninvasive technologies are used to analyze experimental and breeding populations. We cover the most recent research in controlled-environment and field phenotyping for seed, shoot, and root traits. Select field phenotyping technologies have become state of the art and show promise for speeding up the breeding process in early generations. We highlight the technologies behind the rapid advances in proximal and remote sensing of plants in fields. We conclude by discussing the new disciplines working with the phenotyping community: data science, to address the challenge of generating FAIR (findable, accessible, interoperable, and reusable) data, and robotics, to apply phenotyping directly on farms.

Crop Phenomics and High-Throughput Phenotyping: Past Decades, Current Challenges, and Future Perspectives

Since whole-genome sequencing of many crops has been achieved, crop functional genomics studies have stepped into the big-data and high-throughput era. However, acquisition of large-scale phenotypic data has become one of the major bottlenecks hindering crop breeding and functional genomics studies. Nevertheless, recent technological advances provide us potential solutions to relieve this bottleneck and to explore advanced methods for large-scale phenotyping data acquisition and processing in the coming years. In this article, we review the major progress on high-throughput phenotyping in controlled environments and field conditions as well as its use for post-harvest yield and quality assessment in the past decades. We then discuss the latest multi-omics research combining high-throughput phenotyping with genetic studies. Finally, we propose some conceptual challenges and provide our perspectives on how to bridge the phenotype-genotype gap. It is no doubt that accurate high-throughput phenotyping will accelerate plant genetic improvements and promote the next green revolution in crop breeding.

Estimating Biomass and Canopy Height With LiDAR for Field Crop Breeding

Above-ground biomass (AGB) is a trait with much potential for exploitation within wheat breeding programs and is linked closely to canopy height (CH). However, collecting phenotypic data for AGB and CH within breeding programs is labor intensive, and in the case of AGB, destructive and prone to assessment error. As a result, measuring these traits is seldom a priority for breeders, especially at the early stages of a selection program. LiDAR has been demonstrated as a sensor capable of collecting three-dimensional data from wheat field trials, and potentially suitable for providing objective, non-destructive, high-throughput estimates of AGB and CH for use by wheat breeders. The current study investigates the deployment of a LiDAR system on a ground-based high-throughput phenotyping platform in eight wheat field trials across southern Australia, for the non-destructive estimate of AGB and CH. LiDAR-derived measurements were compared to manual measurements of AGB and CH collected at each site and assessed for their suitability of application within a breeding program. Correlations between AGB and LiDAR Projected Volume (LPV) were generally strong (up to r = 0.86), as were correlations between CH and LiDAR Canopy Height (LCH) (up to r = 0.94). Heritability (H2) of LPV (H2 = 0.32-0.90) was observed to be greater than, or similar to, the heritability of AGB (H2 = 0.12-0.78) for the majority of measurements. A similar level of heritability was observed for LCH (H2 = 0.41-0.98) and CH (H2 = 0.49-0.98). Further to this, measurements of LPV and LCH were shown to be highly repeatable when collected from either the same or opposite direction of travel. LiDAR scans were collected at a rate of 2,400 plots per hour, with the potential to further increase throughput to 7,400 plots per hour. This research demonstrates the capability of LiDAR sensors to collect high-quality, non-destructive, repeatable measurements of AGB and CH suitable for use within both breeding and research programs.

Management and Characterization of Abiotic Stress via PhénoField®, a High-Throughput Field Phenotyping Platform

In order to evaluate the impact of water deficit in field conditions, researchers or breeders must set up large experiment networks in very restrictive field environments. Experience shows that half of the field trials are not relevant because of climatic conditions that do not allow the stress scenario to be tested. The PhénoField® platform is the first field based infrastructure in the European Union to ensure protection against rainfall for a large number of plots, coupled with the non-invasive acquisition of crops' phenotype. In this paper, we will highlight the PhénoField® production capability using data from 2017-wheat trial. The innovative approach of the PhénoField® platform consists in the use of automatic irrigating rainout shelters coupled with high throughput field phenotyping to complete conventional phenotyping and micrometeorological densified measurements. Firstly, to test various abiotic stresses, automatic mobile rainout shelters allow fine management of fertilization or irrigation by driving daily the intensity and period of the application of the desired limiting factor on the evaluated crop. This management is based on micro-meteorological measurements coupled with a simulation of a carbon, water and nitrogen crop budget. Furthermore, as high-throughput plant-phenotyping under controlled conditions is well advanced, comparable evaluation in field conditions is enabled through phenotyping gantries equipped with various optical sensors. This approach, giving access to either similar or innovative variables compared manual measurements, is moreover distinguished by its capacity for dynamic analysis. Thus, the interactions between genotypes and the environment can be deciphered and better detailed since this gives access not only to the environmental data but also to plant responses to limiting hydric and nitrogen conditions. Further data analyses provide access to the curve parameters of various indicator kinetics, all the more integrative and relevant of plant behavior under stressful conditions. All these specificities of the PhénoField® platform open the way to the improvement of various categories of crop models, the fine characterization of variety behavior throughout the growth cycle and the evaluation of particular sensors better suited to a specific research question.

Evaluation of the Phenotypic Repeatability of Canopy Temperature in Wheat Using Continuous-Terrestrial and Airborne Measurements

Infrared canopy temperature (CT) is a well-established surrogate measure of stomatal conductance. There is ample evidence showing that genotypic variation in stomatal conductance is associated with grain yield in wheat. Our goal was to determine when CT repeatability is greatest (throughout the season and within the day) to guide CT deployment for research and wheat breeding. CT was measured continuously with ArduCrop wireless infrared thermometers from post-tillering to physiological maturity, and with airborne thermography on cloudless days from manned helicopter at multiple times before and after flowering. Our experiments in wheat, across two years contrasting for water availability, showed that repeatability for CT was greatest later in the season, during grain-filling, and usually in the afternoon. This was supported by the observation that repeatability for ArduCrop, and more so for airborne CT, was significantly associated (P < 0.0001) with calculated clear-sky solar radiation and to a lesser degree, vapor pressure deficit. Adding vapor pressure deficit to a model comprising either clear-sky solar radiation or its determinants, day-of-year and hour-of-day, made little to no improvement to the coefficient of determination. Phenotypic correlations for airborne CT afternoon sampling events were consistently high between events in the same year, more so for the year when soil water was plentiful (r = 0.7 to 0.9) than the year where soil water was limiting (r = 0.4 to 0.9). Phenotypic correlations for afternoon airborne CT were moderate between years contrasting in soil water availability (r = 0.1 to 0.5) and notably greater on two separate days following irrigation or rain in the drier year, ranging from r = 0.39 to 0.53 (P < 0.0001) for the midday events. For ArduCrop CT the pattern of phenotypic correlations, within a given year, was similar for both years: phenotypic correlations were higher during the grain-filling months of October and November and for hours-of-day from 11 onwards. The lowest correlations comprised events from hours-of-day 8 and 9 across all months. The capacity for the airborne method to instantaneously sample CT on hundreds of plots is more suited to large field experiments than the static ArduCrop sensors which measure CT continuously on a single experimental plot at any given time. Our findings provide promising support for the reliable deployment of CT phenotyping for research and wheat breeding, whereby the high repeatability and high phenotypic correlations between afternoon sampling events during grain-filling could enable reliable screening of germplasm from only one or two sampling events.

Translating High-Throughput Phenotyping into Genetic Gain

Inability to efficiently implement high-throughput field phenotyping is increasingly perceived as a key component that limits genetic gain in breeding programs. Field phenotyping must be integrated into a wider context than just choosing the correct selection traits, deployment tools, evaluation platforms, or basic data-management methods. Phenotyping means more than conducting such activities in a resource-efficient manner; it also requires appropriate trial management and spatial variability handling, definition of key constraining conditions prevalent in the target population of environments, and the development of more comprehensive data management, including crop modeling. This review will provide a wide perspective on how field phenotyping is best implemented. It will also outline how to bridge the gap between breeders and 'phenotypers' in an effective manner.

Unmanned Aerial Vehicle Remote Sensing for Field-Based Crop Phenotyping: Current Status and Perspectives

Phenotyping plays an important role in crop science research; the accurate and rapid acquisition of phenotypic information of plants or cells in different environments is helpful for exploring the inheritance and expression patterns of the genome to determine the association of genomic and phenotypic information to increase the crop yield. Traditional methods for acquiring crop traits, such as plant height, leaf color, leaf area index (LAI), chlorophyll content, biomass and yield, rely on manual sampling, which is time-consuming and laborious. Unmanned aerial vehicle remote sensing platforms (UAV-RSPs) equipped with different sensors have recently become an important approach for fast and non-destructive high throughput phenotyping and have the advantage of flexible and convenient operation, on-demand access to data and high spatial resolution. UAV-RSPs are a powerful tool for studying phenomics and genomics. As the methods and applications for field phenotyping using UAVs to users who willing to derive phenotypic parameters from large fields and tests with the minimum effort on field work and getting highly reliable results are necessary, the current status and perspectives on the topic of UAV-RSPs for field-based phenotyping were reviewed based on the literature survey of crop phenotyping using UAV-RSPs in the Web of Science™ Core Collection database and cases study by NERCITA. The reference for the selection of UAV platforms and remote sensing sensors, the commonly adopted methods and typical applications for analyzing phenotypic traits by UAV-RSPs, and the challenge for crop phenotyping by UAV-RSPs were considered. The review can provide theoretical and technical support to promote the applications of UAV-RSPs for crop phenotyping.

Vinobot and Vinoculer: Two Robotic Platforms for High-Throughput Field Phenotyping

In this paper, a new robotic architecture for plant phenotyping is being introduced. The architecture consists of two robotic platforms: an autonomous ground vehicle (Vinobot) and a mobile observation tower (Vinoculer). The ground vehicle collects data from individual plants, while the observation tower oversees an entire field, identifying specific plants for further inspection by the Vinobot. The advantage of this architecture is threefold: first, it allows the system to inspect large areas of a field at any time, during the day and night, while identifying specific regions affected by biotic and/or abiotic stresses; second, it provides high-throughput plant phenotyping in the field by either comprehensive or selective acquisition of accurate and detailed data from groups or individual plants; and third, it eliminates the need for expensive and cumbersome aerial vehicles or similarly expensive and confined field platforms. As the preliminary results from our algorithms for data collection and 3D image processing, as well as the data analysis and comparison with phenotype data collected by hand demonstrate, the proposed architecture is cost effective, reliable, versatile, and extendable.

Towards Automated Large-Scale 3D Phenotyping of Vineyards under Field Conditions

In viticulture, phenotypic data are traditionally collected directly in the field via visual and manual means by an experienced person. This approach is time consuming, subjective and prone to human errors. In recent years, research therefore has focused strongly on developing automated and non-invasive sensor-based methods to increase data acquisition speed, enhance measurement accuracy and objectivity and to reduce labor costs. While many 2D methods based on image processing have been proposed for field phenotyping, only a few 3D solutions are found in the literature. A track-driven vehicle consisting of a camera system, a real-time-kinematic GPS system for positioning, as well as hardware for vehicle control, image storage and acquisition is used to visually capture a whole vine row canopy with georeferenced RGB images. In the first post-processing step, these images were used within a multi-view-stereo software to reconstruct a textured 3D point cloud of the whole grapevine row. A classification algorithm is then used in the second step to automatically classify the raw point cloud data into the semantic plant components, grape bunches and canopy. In the third step, phenotypic data for the semantic objects is gathered using the classification results obtaining the quantity of grape bunches, berries and the berry diameter.

A Novel Remote Sensing Approach for Prediction of Maize Yield Under Different Conditions of Nitrogen Fertilization

Maize crop production is constrained worldwide by nitrogen (N) availability and particularly in poor tropical and subtropical soils. The development of affordable high-throughput crop monitoring and phenotyping techniques is key to improving maize cultivation under low-N fertilization. In this study several vegetation indices (VIs) derived from Red-Green-Blue (RGB) digital images at the leaf and canopy levels are proposed as low-cost tools for plant breeding and fertilization management. They were compared with the performance of the normalized difference vegetation index (NDVI) measured at ground level and from an aerial platform, as well as with leaf chlorophyll content (LCC) and other leaf composition and structural parameters at flowering stage. A set of 10 hybrids grown under five different nitrogen regimes and adequate water conditions were tested at the CIMMYT station of Harare (Zimbabwe). Grain yield and leaf N concentration across N fertilization levels were strongly predicted by most of these RGB indices (with R (2)~ 0.7), outperforming the prediction power of the NDVI and LCC. RGB indices also outperformed the NDVI when assessing genotypic differences in grain yield and leaf N concentration within a given level of N fertilization. The best predictor of leaf N concentration across the five N regimes was LCC but its performance within N treatments was inefficient. The leaf traits evaluated also seemed inefficient as phenotyping parameters. It is concluded that the adoption of RGB-based phenotyping techniques may significantly contribute to the progress of plant breeding and the appropriate management of fertilization.

Soil coring at multiple field environments can directly quantify variation in deep root traits to select wheat genotypes for breeding

We aim to incorporate deep root traits into future wheat varieties to increase access to stored soil water during grain development, which is twice as valuable for yield as water captured at younger stages. Most root phenotyping efforts have been indirect studies in the laboratory, at young plant stages, or using indirect shoot measures. Here, soil coring to 2 m depth was used across three field environments to directly phenotype deep root traits on grain development (depth, descent rate, density, length, and distribution). Shoot phenotypes at coring included canopy temperature depression, chlorophyll reflectance, and green leaf scoring, with developmental stage, biomass, and yield. Current varieties, and genotypes with breeding histories and plant architectures expected to promote deep roots, were used to maximize identification of variation due to genetics. Variation was observed for deep root traits (e.g. 111.4-178.5cm (60%) for depth; 0.09-0.22cm/°C day (144%) for descent rate) using soil coring in the field environments. There was significant variation for root traits between sites, and variation in the relative performance of genotypes between sites. However, genotypes were identified that performed consistently well or poorly at both sites. Furthermore, high-performing genotypes were statistically superior in root traits than low-performing genotypes or commercial varieties. There was a weak but significant negative correlation between green leaf score (-0.5), CTD (0.45), and rooting depth and a positive correlation for chlorophyll reflectance (0.32). Shoot phenotypes did not predict other root traits. This study suggests that field coring can directly identify variation in deep root traits to speed up selection of genotypes for breeding programmes.