Multi-Spectral Imaging from an Unmanned Aerial Vehicle Enables the Assessment of Seasonal Leaf Area Dynamics of Sorghum Breeding Lines

Computer vision-based phenotyping for improvement of plant productivity: a machine learning perspective

Employing computer vision to extract useful information from images and videos is becoming a key technique for identifying phenotypic changes in plants. Here, we review the emerging aspects of computer vision for automated plant phenotyping. Recent advances in image analysis empowered by machine learning-based techniques, including convolutional neural network-based modeling, have expanded their application to assist high-throughput plant phenotyping. Combinatorial use of multiple sensors to acquire various spectra has allowed us to noninvasively obtain a series of datasets, including those related to the development and physiological responses of plants throughout their life. Automated phenotyping platforms accelerate the elucidation of gene functions associated with traits in model plants under controlled conditions. Remote sensing techniques with image collection platforms, such as unmanned vehicles and tractors, are also emerging for large-scale field phenotyping for crop breeding and precision agriculture. Computer vision-based phenotyping will play significant roles in both the nowcasting and forecasting of plant traits through modeling of genotype/phenotype relationships.

Clustering Field-Based Maize Phenotyping of Plant-Height Growth and Canopy Spectral Dynamics Using a UAV Remote-Sensing Approach

Phenotyping under field environmental conditions is often considered as a bottleneck in crop breeding. Unmanned aerial vehicle high throughput phenotypic platform (UAV-HTPP) mounted with multi-sensors offers an efficiency, non-invasive, flexible and low-cost solution in large-scale breeding programs compared to ground investigation, especially where measurements are time-sensitive. This study was conducted at the research station of the Xiao Tangshan National Precision Agriculture Research Center of China. Using the UAV-HTPP, RGB and multispectral images were acquired during four critical growth stages of maize. We present a method of extracting plant height (PH) at the plot scale using UAV-HTPP based on the spatial structure of the maize canopy. The core steps of this method are segmentation and spatial Kriging interpolation based on multiple neighboring maximum pixels from multiple plants in a plot. Then, the relationships between the PH extracted from imagery collected using UAV-HTPP and the ground truth were examined. We developed a semi-automated pipeline for extracting, analyzing and evaluating multiple phenotypic traits: canopy cover (CC), normalized vegetation index (NDVI), PH, average growth rate of plant height (AGRPH), and contribution rate of plant height (CRPH). For these traits, we identify genotypic differences and analyze and evaluate dynamics and development trends during different maize growth stages. Furthermore, we introduce a time series data clustering analysis method into breeding programs as a tool to obtain a novel representative trait: typical curve. We classified and named nine types of typical curves of these traits based on curve morphological features. We found that typical curves can detect differences in the genetic background of traits. For the best results, the recognition rate of an NDVI typical curve is 59%, far less than the 82.3% of the CRPH typical curve. Our study provides evidence that the PH trait is among the most heritable and the NDVI trait is among the most easily affected by the external environment in maize.

Yield and leaf area index estimations for sunflower plants using unmanned aerial vehicle images

Vegetation is commonly monitored to improve efficiency of various agricultural practices. Spatial and temporal changes in plant growth and development can be monitored with the aid of remote sensing techniques employing ground, aerial, and satellite platforms. Unmanned aerial vehicles (UAV) and multi-spectral cameras developed for UAVs have an important potential for agricultural management activities with high-resolution spatial and temporal images. However, UAV images should be assessed based on ground measurements for using these images as a decision-support tool in agriculture. This study was conducted to estimate sunflower leaf area index (LAI) and yield with the aid of Normalized Difference Vegetation Index (NDVI) images generated from raw UAV images. Furthermore, UAV-based NDVI values were compared with NDVI values calculated by using hyper-spectral measurements carried out with a ground-based spectroradiometer. Between July and August of 2017, six flight missions were conducted and spectral measurements were made simultaneously. A significant correlation (R² = 0.77) was determined between NDVI values that belong to UAV platform and spectroradiometer. Also, regression models developed for sunflower LAI and yield estimation depending UAV-based NDVI have R² values of 0.88 and 0.91, respectively.

Elucidating Sorghum Biomass, Nitrogen and Chlorophyll Contents With Spectral and Morphological Traits Derived From Unmanned Aircraft System

Unmanned aircraft systems (UAS) provide an efficient way to phenotype crop morphology with spectral traits such as plant height, canopy cover and various vegetation indices (VIs) providing information to elucidate genotypic responses to the environment. In this study, we investigated the potential use of UAS-derived traits to elucidate biomass, nitrogen and chlorophyll content in sorghum under nitrogen stress treatments. A nitrogen stress trial located in Nebraska, USA, contained 24 different sorghum lines, 2 nitrogen treatments and 8 replications, for a total of 384 plots. Morphological and spectral traits including plant height, canopy cover and various VIs were derived from UAS flights with a true-color RGB camera and a 5-band multispectral camera at early, mid and late growth stages across the sorghum growing season in 2017. Simple and multiple regression models were investigated for sorghum biomass, nitrogen and chlorophyll content estimations using the derived morphological and spectral traits along with manual ground truthed measurements. Results showed that, the UAS-derived plant height was strongly correlated with manually measured plant height (r = 0.85); and the UAS-derived biomass using plant height, canopy cover and VIs had strong exponential correlations with the sampled biomass of fresh stalks and leaves (maximum r = 0.85) and the biomass of dry stalks and leaves (maximum r = 0.88). The UAS-derived VIs were moderately correlated with the laboratory measured leaf nitrogen content (r = 0.52) and the measured leaf chlorophyll content (r = 0.69) in each plot. The methods developed in this study will facilitate genetic improvement and agronomic studies that require assessment of stress responses in large-scale field trials.

Integrating modelling and phenotyping approaches to identify and screen complex traits: transpiration efficiency in cereals

Following advances in genetics, genomics, and phenotyping, trait selection in breeding is limited by our ability to understand interactions within the plant and with the environment, and to identify traits of most relevance to the target population of environments. We propose an integrated approach that combines insights from crop modelling, physiology, genetics, and breeding to characterize traits valuable for yield gain in the target population of environments, develop relevant high-throughput phenotyping platforms, and identify genetic controls and their value in production environments. This paper uses transpiration efficiency (biomass produced per unit of water used) as an example of a complex trait of interest to illustrate how the approach can guide modelling, phenotyping, and selection in a breeding programme. We believe that this approach, by integrating insights from diverse disciplines, can increase the resource use efficiency of breeding programmes for improving yield gains in target populations of environments.

Improving nitrogen use efficiency in plants: effective phenotyping in conjunction with agronomic and genetic approaches

For global sustainable food production and environmental benefits, there is an urgent need to improve N use efficiency (NUE) in crop plants. Excessive and inefficient use of N fertiliser results in increased crop production costs and environmental pollution. Therefore, cost-effective strategies such as proper management of the timing and quantity of N fertiliser application, and breeding for better varieties are needed to improve NUE in crops. However, for these efforts to be feasible, high-throughput and reliable phenotyping techniques would be very useful for monitoring N status in planta, as well as to facilitate faster decisions during breeding and selection processes. This review provides an insight into contemporary approaches to phenotyping NUE-related traits and associated challenges. We discuss recent and advanced, sensor- and image-based phenotyping techniques that use a variety of equipment, tools and platforms. The review also elaborates on how high-throughput phenotyping will accelerate efforts for screening large populations of diverse genotypes in controlled environment and field conditions to identify novel genotypes with improved NUE.

High-Throughput Phenotyping of Canopy Cover and Senescence in Maize Field Trials Using Aerial Digital Canopy Imaging

In the crop breeding process, the use of data collection methods that allow reliable assessment of crop adaptation traits, faster and cheaper than those currently in use, can significantly improve resource use efficiency by reducing selection cost and can contribute to increased genetic gain through improved selection efficiency. Current methods to estimate crop growth (ground canopy cover) and leaf senescence are essentially manual and/or by visual scoring, and are therefore often subjective, time consuming, and expensive. Aerial sensing technologies offer radically new perspectives for assessing these traits at low cost, faster, and in a more objective manner. We report the use of an unmanned aerial vehicle (UAV) equipped with an RGB camera for crop cover and canopy senescence assessment in maize field trials. Aerial-imaging-derived data showed a moderately high heritability for both traits with a significant genetic correlation with grain yield. In addition, in some cases, the correlation between the visual assessment (prone to subjectivity) of crop senescence and the senescence index, calculated from aerial imaging data, was significant. We concluded that the UAV-based aerial sensing platforms have great potential for monitoring the dynamics of crop canopy characteristics like crop vigor through ground canopy cover and canopy senescence in breeding trial plots. This is anticipated to assist in improving selection efficiency through higher accuracy and precision, as well as reduced time and cost of data collection.