Coupling of machine learning methods to improve estimation of ground coverage from unmanned aerial vehicle (UAV) imagery for high-throughput phenotyping of crops

Drone-Based Harvest Data Prediction Can Reduce On-Farm Food Loss and Improve Farmer Income

On-farm food loss (i.e., grade-out vegetables) is a difficult challenge in sustainable agricultural systems. The simplest method to reduce the number of grade-out vegetables is to monitor and predict the size of all individuals in the vegetable field and determine the optimal harvest date with the smallest grade-out number and highest profit, which is not cost-effective by conventional methods. Here, we developed a full pipeline to accurately estimate and predict every broccoli head size (n > 3,000) automatically and nondestructively using drone remote sensing and image analysis. The individual sizes were fed to the temperature-based growth model and predicted the optimal harvesting date. Two years of field experiments revealed that our pipeline successfully estimated and predicted the head size of all broccolis with high accuracy. We also found that a deviation of only 1 to 2 days from the optimal date can considerably increase grade-out and reduce farmer's profits. This is an unequivocal demonstration of the utility of these approaches to economic crop optimization and minimization of food losses.

Mapping Malaria Vector Habitats in West Africa: Drone Imagery and Deep Learning Analysis for Targeted Vector Surveillance

Disease control programs are needed to identify the breeding sites of mosquitoes, which transmit malaria and other diseases, in order to target interventions and identify environmental risk factors. The increasing availability of very-high-resolution drone data provides new opportunities to find and characterize these vector breeding sites. Within this study, drone images from two malaria-endemic regions in Burkina Faso and Côte d'Ivoire were assembled and labeled using open-source tools. We developed and applied a workflow using region-of-interest-based and deep learning methods to identify land cover types associated with vector breeding sites from very-high-resolution natural color imagery. Analysis methods were assessed using cross-validation and achieved maximum Dice coefficients of 0.68 and 0.75 for vegetated and non-vegetated water bodies, respectively. This classifier consistently identified the presence of other land cover types associated with the breeding sites, obtaining Dice coefficients of 0.88 for tillage and crops, 0.87 for buildings and 0.71 for roads. This study establishes a framework for developing deep learning approaches to identify vector breeding sites and highlights the need to evaluate how results will be used by control programs.

Opportunity and challenges of phenotyping plant salt tolerance

Salinity is a key factor limiting agricultural production worldwide. Recent advances in field phenotyping have enabled the recording of the environmental history and dynamic response of plants by considering both genotype × environment (G×E) interactions and envirotyping. However, only a few studies have focused on plant salt tolerance phenotyping. Therefore, we analyzed the potential opportunities and major challenges in improving plant salt tolerance using advanced field phenotyping technologies. RGB imaging and spectral and thermal sensors are the most useful and important sensing techniques for assessing key morphological and physiological traits of plant salt tolerance. However, field phenotyping faces challenges owing to its practical applications and high costs, limiting its use in early generation breeding and in developing countries.

A Systematic Review of Effective Hardware and Software Factors Affecting High-Throughput Plant Phenotyping

Plant phenotyping studies the complex characteristics of plants, with the aim of evaluating and assessing their condition and finding better exemplars. Recently, a new branch emerged in the phenotyping field, namely, high-throughput phenotyping (HTP). Specifically, HTP exploits modern data sampling techniques to gather a high amount of data that can be used to improve the effectiveness of phenotyping. Hence, HTP combines the knowledge derived from the phenotyping domain with computer science, engineering, and data analysis techniques. In this scenario, machine learning (ML) and deep learning (DL) algorithms have been successfully integrated with noninvasive imaging techniques, playing a key role in automation, standardization, and quantitative data analysis. This study aims to systematically review two main areas of interest for HTP: hardware and software. For each of these areas, two influential factors were identified: for hardware, platforms and sensing equipment were analyzed; for software, the focus was on algorithms and new trends. The study was conducted following the PRISMA protocol, which allowed the refinement of the research on a wide selection of papers by extracting a meaningful dataset of 32 articles of interest. The analysis highlighted the diffusion of ground platforms, which were used in about 47% of reviewed methods, and RGB sensors, mainly due to their competitive costs, high compatibility, and versatility. Furthermore, DL-based algorithms accounted for the larger share (about 69%) of reviewed approaches, mainly due to their effectiveness and the focus posed by the scientific community over the last few years. Future research will focus on improving DL models to better handle hardware-generated data. The final aim is to create integrated, user-friendly, and scalable tools that can be directly deployed and used on the field to improve the overall crop yield.

Estimation of Maize Foliar Temperature and Stomatal Conductance as Indicators of Water Stress Based on Optical and Thermal Imagery Acquired Using an Unmanned Aerial Vehicle (UAV) Platform

Climatic variability and extreme weather events impact agricultural production, especially in sub-Saharan smallholder cropping systems, which are commonly rainfed. Hence, the development of early warning systems regarding moisture availability can facilitate planning, mitigate losses and optimise yields through moisture augmentation. Precision agricultural practices, facilitated by unmanned aerial vehicles (UAVs) with very high-resolution cameras, are useful for monitoring farm-scale dynamics at near-real-time and have become an important agricultural management tool. Considering these developments, we evaluated the utility of optical and thermal infrared UAV imagery, in combination with a random forest machine-learning algorithm, to estimate the maize foliar temperature and stomatal conductance as indicators of potential crop water stress and moisture content over the entire phenological cycle. The results illustrated that the thermal infrared waveband was the most influential variable during vegetative growth stages, whereas the red-edge and near-infrared derived vegetation indices were fundamental during the reproductive growth stages for both temperature and stomatal conductance. The results also suggested mild water stress during vegetative growth stages and after a hailstorm during the mid-reproductive stage. Furthermore, the random forest model optimally estimated the maize crop temperature and stomatal conductance over the various phenological stages. Specifically, maize foliar temperature was best predicted during the mid-vegetative growth stage and stomatal conductance was best predicted during the early reproductive growth stage. Resultant maps of the modelled maize growth stages captured the spatial heterogeneity of maize foliar temperature and stomatal conductance within the maize field. Overall, the findings of the study demonstrated that the use of UAV optical and thermal imagery, in concert with prediction-based machine learning, is a useful tool, available to smallholder farmers to help them make informed management decisions that include the optimal implementation of irrigation schedules.

Combining Canopy Coverage and Plant Height from UAV-Based RGB Images to Estimate Spraying Volume on Potato

Canopy coverage and plant height are the main crop canopy parameters, which can obviously reflect the growth status of crops on the field. The ability to identify canopy coverage and plant height quickly is critical for farmers or breeders to arrange their working schedule. In precision agriculture, choosing the opportunity and amount of farm inputs is the critical part, which will improve the yield and decrease the cost. The potato canopy coverage and plant height were quickly extracted, which could be used to estimate the spraying volume using the evaluation model obtained by indoor tests. The vegetation index approach was used to extract potato canopy coverage, and the color point cloud data method at different height rates was formed to estimate the plant height of potato at different growth stages. The original data were collected using a low-cost UAV, which was mounted on a high-resolution RGB camera. Then, the Structure from Motion (SFM) algorithm was used to extract the 3D point cloud from ordered images that could form a digital orthophoto model (DOM) and sparse point cloud. The results show that the vegetation index-based method could accurately estimate canopy coverage. Among EXG, EXR, RGBVI, GLI, and CIVE, EXG achieved the best adaptability in different test plots. Point cloud data could be used to estimate plant height, but when the potato coverage rate was low, potato canopy point cloud data underwent rarefaction; in the vigorous growth period, the estimated value was substantially connected with the measured value (R2 = 0.94). The relationship between the coverage area of spraying on potato canopy and canopy coverage was measured indoors to form the model. The results revealed that the model could estimate the dose accurately (R2 = 0.878). Therefore, combining agronomic factors with data extracted from the UAV RGB image had the ability to predict the field spraying volume.

EasyIDP: A Python Package for Intermediate Data Processing in UAV-Based Plant Phenotyping

Unmanned aerial vehicle (UAV) and structure from motion (SfM) photogrammetry techniques are widely used for field-based, high-throughput plant phenotyping nowadays, but some of the intermediate processes throughout the workflow remain manual. For example, geographic information system (GIS) software is used to manually assess the 2D/3D field reconstruction quality and cropping region of interests (ROIs) from the whole field. In addition, extracting phenotypic traits from raw UAV images is more competitive than directly from the digital orthomosaic (DOM). Currently, no easy-to-use tools are available to implement previous tasks for commonly used commercial SfM software, such as Pix4D and Agisoft Metashape. Hence, an open source software package called easy intermediate data processor (EasyIDP; MIT license) was developed to decrease the workload in intermediate data processing mentioned above. The functions of the proposed package include (1) an ROI cropping module, assisting in reconstruction quality assessment and cropping ROIs from the whole field, and (2) an ROI reversing module, projecting ROIs to relative raw images. The result showed that both cropping and reversing modules work as expected. Moreover, the effects of ROI height selection and reversed ROI position on raw images to reverse calculation were discussed. This tool shows great potential for decreasing workload in data annotation for machine learning applications.

Scaling up high-throughput phenotyping for abiotic stress selection in the field

High-throughput phenotyping (HTP) is in its infancy for deployment in large-scale breeding programmes. With the ability to measure correlated traits associated with physiological ideotypes, in-field phenotyping methods are available for screening of abiotic stress responses. As cropping environments become more hostile and unpredictable due to the effects of climate change, the need to characterise variability across spatial and temporal scales will become increasingly important. The sensor technologies that have enabled HTP from macroscopic through to satellite sensors may also be utilised here to complement spatial characterisation using envirotyping, which can improve estimations of genotypic performance across environments by better accounting for variation at the plot, trial and inter-trial levels. Climate change is leading to increased variation at all physical and temporal scales in the cropping environment. Maintaining yield stability under circumstances with greater levels of abiotic stress while capitalising upon yield potential in good years, requires approaches to plant breeding that target the physiological limitations to crop performance in specific environments. This requires dynamic modelling of conditions within target populations of environments, GxExM predictions, clustering of environments so breeding trajectories can be defined, and the development of screens that enable selection for genetic gain to occur. High-throughput phenotyping (HTP), combined with related technologies used for envirotyping, can help to address these challenges. Non-destructive analysis of the morphological, biochemical and physiological qualities of plant canopies using HTP has great potential to complement whole-genome selection, which is becoming increasingly common in breeding programmes. A range of novel analytic techniques, such as machine learning and deep learning, combined with a widening range of sensors, allow rapid assessment of large breeding populations that are repeatable and objective. Secondary traits underlying radiation use efficiency and water use efficiency can be screened with HTP for selection at the early stages of a breeding programme. HTP and envirotyping technologies can also characterise spatial variability at trial and within-plot levels, which can be used to correct for spatial variations that confound measurements of genotypic values. This review explores HTP for abiotic stress selection through a physiological trait lens and additionally investigates the use of envirotyping and EC to characterise spatial variability at all physical scales in METs.