Field-based Robot Phenotyping of Sorghum Plant Architecture using Stereo Vision

Sorghum (Sorghum bicolor) is known as a major feedstock for biofuel production. To improve its biomass yield through genetic research, manually measuring yield component traits (e.g. plant height, stem diameter, leaf angle, leaf area, leaf number, and panicle size) in the field is the current best practice. However, such laborious and time‐consuming tasks have become a bottleneck limiting experiment scale and data acquisition frequency. This paper presents a high‐throughput field‐based robotic phenotyping system which performed side‐view stereo imaging for dense sorghum plants with a wide range of plant heights throughout the growing season. Our study demonstrated the suitability of stereo vision for field‐based three‐dimensional plant phenotyping when recent advances in stereo matching algorithms were incorporated. A robust data processing pipeline was developed to quantify the variations or morphological traits in plant architecture, which included plot‐based plant height, plot‐based plant width, convex hull volume, plant surface area, and stem diameter (semiautomated). These image‐derived measurements were highly repeatable and showed high correlations with the in‐field manual measurements. Meanwhile, manually collecting the same traits required a large amount of manpower and time compared to the robotic system. The results demonstrated that the proposed system could be a promising tool for large‐scale field‐based high‐throughput plant phenotyping of bioenergy crops

Novel Bayesian Networks for Genomic Prediction of Developmental Traits in Biomass Sorghum.

The ability to connect genetic information between traits over time allow Bayesian networks to offer a powerful probabilistic framework to construct genomic prediction models. In this study, we phenotyped a diversity panel of 869 biomass sorghum (Sorghum bicolor (L.) Moench) lines, which had been genotyped with 100,435 SNP markers, for plant height (PH) with biweekly measurements from 30 to 120 days after planting (DAP) and for end-of-season dry biomass yield (DBY) in four environments. We evaluated five genomic prediction models: Bayesian network (BN), Pleiotropic Bayesian network (PBN), Dynamic Bayesian network (DBN), multi-trait GBLUP (MTr-GBLUP), and multi-time GBLUP (MTi-GBLUP) models. In fivefold cross-validation, prediction accuracies ranged from 0.46 (PBN) to 0.49 (MTr-GBLUP) for DBY and from 0.47 (DBN, DAP120) to 0.75 (MTi-GBLUP, DAP60) for PH. Forward-chaining cross-validation further improved prediction accuracies of the DBN, MTi-GBLUP and MTr-GBLUP models for PH (training slice: 30-45 DAP) by 36.4-52.4% relative to the BN and PBN models. Coincidence indices (target: biomass, secondary: PH) and a coincidence index based on lines (PH time series) showed that the ranking of lines by PH changed minimally after 45 DAP. These results suggest a two-level indirect selection method for PH at harvest (first-level target trait) and DBY (second-level target trait) could be conducted earlier in the season based on ranking of lines by PH at 45 DAP (secondary trait). With the advance of high-throughput phenotyping technologies, our proposed two-level indirect selection framework could be valuable for enhancing genetic gain per unit of time when selecting on developmental traits

Mobile robotics platform for strawberry temporal–spatial yield monitoring within precision indoor farming systems

Plant phenotyping and production management are emerging fields to facilitate Genetics, Environment, & Management (GEM) research and provide production guidance. Precision indoor farming systems (PIFS), vertical farms with artificial light (aka plant factories) in particular, have long been suitable production scenes due to the advantages of efficient land utilization and year-round cultivation. In this study, a mobile robotics platform (MRP) within a commercial plant factory has been developed to dynamically understand plant growth and provide data support for growth model construction and production management by periodical monitoring of individual strawberry plants and fruit. Yield monitoring, where yield = the total number of ripe strawberry fruit detected, is a critical task to provide information on plant phenotyping. The MRP consists of an autonomous mobile robot (AMR) and a multilayer perception robot (MPR), i.e., MRP = the MPR installed on top of the AMR. The AMR is capable of traveling along the aisles between plant growing rows. The MPR consists of a data acquisition module that can be raised to the height of any plant growing tier of each row by a lifting module. Adding AprilTag observations (captured by a monocular camera) into the inertial navigation system to form an ATI navigation system has enhanced the MRP navigation within the repetitive and narrow physical structure of a plant factory to capture and correlate the growth and position information of each individual strawberry plant. The MRP performed robustly at various traveling speeds with a positioning accuracy of 13.0 mm. The temporal–spatial yield monitoring within a whole plant factory can be achieved to guide farmers to harvest strawberries on schedule through the MRP’s periodical inspection. The yield monitoring performance was found to have an error rate of 6.26% when the plants were inspected at a constant MRP traveling speed of 0.2 m/s. The MRP’s functions are expected to be transferable and expandable to other crop production monitoring and cultural tasks

In Vivo Human-Like Robotic Phenotyping of Leaf and Stem Traits in Maize and Sorghum in Greenhouse

In plant phenotyping, the measurement of morphological, physiological and chemical traits of leaves and stems is needed to investigate and monitor the condition of plants. The manual measurement of these properties is time consuming, tedious, error prone, and laborious. The use of robots is a new approach to accomplish such endeavors, which enables automatic monitoring with minimal human intervention. In this study, two plant phenotyping robotic systems were developed to realize automated measurement of plant leaf properties and stem diameter which could reduce the tediousness of data collection compare to manual measurements. The robotic systems comprised of a four degree of freedom (DOF) robotic manipulator and a Time-of-Flight (TOF) camera. Robotic grippers were developed to integrate an optical fiber cable (coupled to a portable spectrometer) for leaf spectral reflectance measurement, a thermistor for leaf temperature measurement, and a linear potentiometer for stem diameter measurement. An Image processing technique and deep learning method were used to identify grasping points on leaves and stems, respectively. The systems were tested in a greenhouse using maize and sorghum plants. The results from the leaf phenotyping robot experiment showed that leaf temperature measurements by the phenotyping robot were correlated with those measured manually by a human researcher (R2 = 0.58 for maize and 0.63 for sorghum). The leaf spectral measurements by the phenotyping robot predicted leaf chlorophyll, water content and potassium with moderate success (R2 ranged from 0.52 to 0.61), whereas the prediction for leaf nitrogen and phosphorus were poor. The total execution time to grasp and take measurements from one leaf was 35.5±4.4 s for maize and 38.5±5.7 s for sorghum. Furthermore, the test showed that the grasping success rate was 78% for maize and 48% for sorghum. The experimental results from the stem phenotyping robot demonstrated a high correlation between the manual and automated stem diameter measurements (R2 \u3e 0.98). The execution time for stem diameter measurement was 45.3 s. The system could successfully detect and localize, and also grasp the stem for all plants during the experiment. Both robots could decrease the tediousness of collecting phenotypes compare to manual measurements. The phenotyping robots can be useful to complement the traditional image-based high-throughput plant phenotyping in greenhouses by collecting in vivo morphological, physiological, and biochemical trait measurements for plant leaves and stems. Advisors: Yufeng Ge, Santosh Pitl

Linkage disequilibrium mapping of high-throughput image-derived descriptors of plant architecture traits under field conditions

Plant architectural traits are important factors in determining grain and biomass productivity of sorghum (Sorghum bicolor (L) Moench). However, collecting data on these architectural traits is labor-intensive and time-consuming, especially when using numerous lines in quantitative genetic studies or breeding programs. Therefore, we used the high-throughput field-based robotic platform PhenoBot 1.0 to collect whole canopy stereo images from a large association mapping panel under field conditions. These images were used to create a plot-based 3D reconstruction of the canopy from which phenotypic features were automatically extracted. These features included: plot-based plant height (PPH), plot-based plant width (PPW), plant surface area (PSA), and convex-hull volume (CHV). A small sub-set of sorghum lines were used to obtain groundtruth measurements to validate the image-derived descriptors, and determine their biological significance. PPH was highly correlated with manually measured plant height; PPW correlated with SinAL, defined as leaf length multiplied by the sine of its angle; PSA was associated with manually measured total plant surface area; and CHV was a function of both flag-leaf height and SinAL. Association mapping of PPH identified chromosomal regions containing known plant height genes, confirming the accuracy of the automatic feature extraction process. For the other phenotypic features, significant markers were identified within genomic regions that have been previously reported to control plant architectural characteristics in sorghum such as tiller number, shoot compactness, leaf length, surface area, and angle. The image processing method used in this study contributes new knowledge to the development of high-throughput phenotyping techniques and represents a novel tool for plant breeders

Field-based Robot Phenotyping of Sorghum Plant Architecture using Stereo Vision

Sorghum (Sorghum bicolor) is known as a major feedstock for biofuel production. To improve its biomass yield through genetic research, manually measuring yield component traits (e.g. plant height, stem diameter, leaf angle, leaf area, leaf number, and panicle size) in the field is the current best practice. However, such laborious and time‐consuming tasks have become a bottleneck limiting experiment scale and data acquisition frequency. This paper presents a high‐throughput field‐based robotic phenotyping system which performed side‐view stereo imaging for dense sorghum plants with a wide range of plant heights throughout the growing season. Our study demonstrated the suitability of stereo vision for field‐based three‐dimensional plant phenotyping when recent advances in stereo matching algorithms were incorporated. A robust data processing pipeline was developed to quantify the variations or morphological traits in plant architecture, which included plot‐based plant height, plot‐based plant width, convex hull volume, plant surface area, and stem diameter (semiautomated). These image‐derived measurements were highly repeatable and showed high correlations with the in‐field manual measurements. Meanwhile, manually collecting the same traits required a large amount of manpower and time compared to the robotic system. The results demonstrated that the proposed system could be a promising tool for large‐scale field‐based high‐throughput plant phenotyping of bioenergy crops.This is the peer-reviewed version of the following article: Bao, Yin, Lie Tang, Matthew W. Breitzman, Maria G. Salas Fernandez, and Patrick S. Schnable. "Field‐based robotic phenotyping of sorghum plant architecture using stereo vision." Journal of Field Robotics (2018), which has been published in final form at DOI: 10.1002/rob.21830. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Self-Archiving.Posted with permission.</p