Leaf Angle eXtractor: A high-throughput image processing framework for leaf angle measurements in maize and sorghum

Phenotyping of Drought-Stressed Poplar Saplings Using Exemplar-Based Data Generation and Leaf-Level Structural Analysis

Drought stress is one of the main threats to poplar plant growth and has a negative impact on plant yield. Currently, high-throughput plant phenotyping has been widely studied as a rapid and nondestructive tool for analyzing the growth status of plants, such as water and nutrient content. In this study, a combination of computer vision and deep learning was used for drought-stressed poplar sapling phenotyping. Four varieties of poplar saplings were cultivated, and 5 different irrigation treatments were applied. Color images of the plant samples were captured for analysis. Two tasks, including leaf posture calculation and drought stress identification, were conducted. First, instance segmentation was used to extract the regions of the leaf, petiole, and midvein. A dataset augmentation method was created for reducing manual annotation costs. The horizontal angles of the fitted lines of the petiole and midvein were calculated for leaf posture digitization. Second, multitask learning models were proposed for simultaneously determining the stress level and poplar variety. The mean absolute errors of the angle calculations were 10.7° and 8.2° for the petiole and midvein, respectively. Drought stress increased the horizontal angle of leaves. Moreover, using raw images as the input, the multitask MobileNet achieved the highest accuracy (99% for variety identification and 76% for stress level classification), outperforming widely used single-task deep learning models (stress level classification accuracies of <70% on the prediction dataset). The plant phenotyping methods presented in this study could be further used for drought-stress-resistant poplar plant screening and precise irrigation decision-making.

SCAG: A Stratified, Clustered, and Growing-Based Algorithm for Soybean Branch Angle Extraction and Ideal Plant Architecture Evaluation

Three-dimensional (3D) phenotyping is important for studying plant structure and function. Light detection and ranging (LiDAR) has gained prominence in 3D plant phenotyping due to its ability to collect 3D point clouds. However, organ-level branch detection remains challenging due to small targets, sparse points, and low signal-to-noise ratios. In addition, extracting biologically relevant angle traits is difficult. In this study, we developed a stratified, clustered, and growing-based algorithm (SCAG) for soybean branch detection and branch angle calculation from LiDAR data, which is heuristic, open-source, and expandable. SCAG achieved high branch detection accuracy (F-score = 0.77) and branch angle calculation accuracy (r = 0.84) when evaluated on 152 diverse soybean varieties. Meanwhile, the SCAG outperformed 2 other classic algorithms, the support vector machine (F-score = 0.53) and density-based methods (F-score = 0.55). Moreover, after applying the SCAG to 405 soybean varieties over 2 consecutive years, we quantified various 3D traits, including canopy width, height, stem length, and average angle. After data filtering, we identified novel heritable and repeatable traits for evaluating soybean density tolerance potential, such as the ratio of average angle to height and the ratio of average angle to stem length, which showed greater potential than the well-known ratio of canopy width to height trait. Our work demonstrates remarkable advances in 3D phenotyping and plant architecture screening. The algorithm can be applied to other crops, such as maize and tomato. Our dataset, scripts, and software are public, which can further benefit the plant science community by enhancing plant architecture characterization and ideal variety selection.

The role of phenomics and genomics in delineating the genetic basis of complex traits in millets

Millets, comprising a diverse group of small-seeded grains, have emerged as vital crops with immense nutritional, environmental, and economic significance. The comprehension of complex traits in millets, influenced by multifaceted genetic determinants, presents a compelling challenge and opportunity in agricultural research. This review delves into the transformative roles of phenomics and genomics in deciphering these intricate genetic architectures. On the phenomics front, high-throughput platforms generate rich datasets on plant morphology, physiology, and performance in diverse environments. This data, coupled with field trials and controlled conditions, helps to interpret how the environment interacts with genetics. Genomics provides the underlying blueprint for these complex traits. Genome sequencing and genotyping technologies have illuminated the millet genome landscape, revealing diverse gene pools and evolutionary relationships. Additionally, different omics approaches unveil the intricate information of gene expression, protein function, and metabolite accumulation driving phenotypic expression. This multi-omics approach is crucial for identifying candidate genes and unfolding the intricate pathways governing complex traits. The review highlights the synergy between phenomics and genomics. Genomically informed phenotyping targets specific traits, reducing the breeding size and cost. Conversely, phenomics identifies promising germplasm for genomic analysis, prioritizing variants with superior performance. This dynamic interplay accelerates breeding programs and facilitates the development of climate-smart, nutrient-rich millet varieties and hybrids. In conclusion, this review emphasizes the crucial roles of phenomics and genomics in unlocking the genetic enigma of millets.

Bioinformatics in Plant Breeding and Research on Disease Resistance

In the context of plant breeding, bioinformatics can empower genetic and genomic selection to determine the optimal combination of genotypes that will produce a desired phenotype and help expedite the isolation of these new varieties. Bioinformatics is also instrumental in collecting and processing plant phenotypes, which facilitates plant breeding. Robots that use automated and digital technologies to collect and analyze different types of information to monitor the environment in which plants grow, analyze the environmental stresses they face, and promptly optimize suboptimal and adverse growth conditions accordingly, have helped plant research and saved human resources. In this paper, we describe the use of various bioinformatics databases and algorithms and explore their potential applications in plant breeding and for research on plant disease resistance.

Machine Learning in the Analysis of Multispectral Reads in Maize Canopies Responding to Increased Temperatures and Water Deficit

Real-time monitoring of crop responses to environmental deviations represents a new avenue for applications of remote and proximal sensing. Combining the high-throughput devices with novel machine learning (ML) approaches shows promise in the monitoring of agricultural production. The 3 × 2 multispectral arrays with responses at 610 and 680 nm (red), 730 and 760 nm (red-edge) and 810 and 860 nm (infrared) spectra were used to assess the occurrence of leaf rolling (LR) in 545 experimental maize plots measured four times for calibration dataset (n = 2180) and 145 plots measured once for external validation. Multispectral reads were used to calculate 15 simple normalized vegetation indices. Four ML algorithms were assessed: single and multilayer perceptron (SLP and MLP), convolutional neural network (CNN) and support vector machines (SVM) in three validation procedures, which were stratified cross-validation, random subset validation and validation with external dataset. Leaf rolling occurrence caused visible changes in spectral responses and calculated vegetation indexes. All algorithms showed good performance metrics in stratified cross-validation (accuracy >80%). SLP was the least efficient in predictions with external datasets, while MLP, CNN and SVM showed comparable performance. Combining ML with multispectral sensing shows promise in transition towards agriculture based on data-driven decisions especially considering the novel Internet of Things (IoT) avenues.

Advances in optical phenotyping of cereal crops

Optical sensors and sensing-based phenotyping techniques have become mainstream approaches in high-throughput phenotyping for improving trait selection and genetic gains in crops. We review recent progress and contemporary applications of optical sensing-based phenotyping (OSP) techniques in cereal crops and highlight optical sensing principles for spectral response and sensor specifications. Further, we group phenotypic traits determined by OSP into four categories - morphological, biochemical, physiological, and performance traits - and illustrate appropriate sensors for each extraction. In addition to the current status, we discuss the challenges of OSP and provide possible solutions. We propose that optical sensing-based traits need to be explored further, and that standardization of the language of phenotyping and worldwide collaboration between phenotyping researchers and other fields need to be established.

3D reconstruction identifies loci linked to variation in angle of individual sorghum leaves

Selection for yield at high planting density has reshaped the leaf canopy of maize, improving photosynthetic productivity in high density settings. Further optimization of canopy architecture may be possible. However, measuring leaf angles, the widely studied component trait of leaf canopy architecture, by hand is a labor and time intensive process. Here, we use multiple, calibrated, 2D images to reconstruct the 3D geometry of individual sorghum plants using a voxel carving based algorithm. Automatic skeletonization and segmentation of these 3D geometries enable quantification of the angle of each leaf for each plant. The resulting measurements are both heritable and correlated with manually collected leaf angles. This automated and scaleable reconstruction approach was employed to measure leaf-by-leaf angles for a population of 366 sorghum plants at multiple time points, resulting in 971 successful reconstructions and 3,376 leaf angle measurements from individual leaves. A genome wide association study conducted using aggregated leaf angle data identified a known large effect leaf angle gene, several previously identified leaf angle QTL from a sorghum NAM population, and novel signals. Genome wide association studies conducted separately for three individual sorghum leaves identified a number of the same signals, a previously unreported signal shared across multiple leaves, and signals near the sorghum orthologs of two maize genes known to influence leaf angle. Automated measurement of individual leaves and mapping variants associated with leaf angle reduce the barriers to engineering ideal canopy architectures in sorghum and other grain crops.

Resources for image-based high-throughput phenotyping in crops and data sharing challenges

High-throughput phenotyping (HTP) platforms are capable of monitoring the phenotypic variation of plants through multiple types of sensors, such as red green and blue (RGB) cameras, hyperspectral sensors, and computed tomography, which can be associated with environmental and genotypic data. Because of the wide range of information provided, HTP datasets represent a valuable asset to characterize crop phenotypes. As HTP becomes widely employed with more tools and data being released, it is important that researchers are aware of these resources and how they can be applied to accelerate crop improvement. Researchers may exploit these datasets either for phenotype comparison or employ them as a benchmark to assess tool performance and to support the development of tools that are better at generalizing between different crops and environments. In this review, we describe the use of image-based HTP for yield prediction, root phenotyping, development of climate-resilient crops, detecting pathogen and pest infestation, and quantitative trait measurement. We emphasize the need for researchers to share phenotypic data, and offer a comprehensive list of available datasets to assist crop breeders and tool developers to leverage these resources in order to accelerate crop breeding.