Performance of Experimental Biomass Sorghum Hybrids from the ISU Breeding Program

This research project is part of the sorghum breeding program at ISU, which has the ultimate goal of evaluating the potential of ISU inbred lines as parents of lignocellulosic hybrids. The objectives of this research project were: To determine the biomass yield of experimental hybrids, and its performance relative to commercial materials To determine the effect of lodging and planting density on final biomass yield To determine the level of association between plant height and lodgin

GPU-based Parallelization of a Sub-pixel Highresolution Stereo Matching Algorithm for Highthroughput Biomass Sorghum Phenotyping

To automate high-throughput phenotyping for infield biomass sorghum morphological traits characterization, a capable 3D vision system that can overcome challenges imposed by field conditions including variable lighting, strong wind and extreme plant height is needed. Among all available 3D sensors, traditional stereo cameras offer a viable solution to obtaining high-resolution 3D point-cloud data with the use of high-accuracy (sub-pixel) stereo matching algorithms, which, however, are inevitably highly computational. This paper reports a GPU-based parallelized implementation of the PatchMatch Stereo algorithm which reconstructs highly slanted leaf and stalk surfaces of sorghum at high speed from high-resolution stereo image pairs. Our algorithm enhanced accuracy and smoothness by using L2 norm for color distance calculation instead of L1 norm and speeded up convergence by testing the plane of the lowest cost within a local window in addition to the original spatial propagation. To better handle textureless regions, after left-right consistency check, the disparity of an occluded pixel is assigned to that of a nearby non-occluded pixel with the most similar pattern. Some of these occluded pixels in textureless region would survive a following left-right consistency check. Therefore more valid pixels would exist in textureless regions for occlusion filling. Accuracy and performance were evaluated on Middlebury datasets as well as our sorghum datasets. It achieved a high ranking in Middlebury table of subpixel precision and revealed subtle details on leaf and stalk surfaces. The output disparity maps were used to estimate stalk diameters of different varieties and growth stages. The results showed high correlation to hand measurement

Infield Biomass Sorghum Yield Component Traits Extraction Pipeline Using Stereo Vision

Yield component traits such as plant height and stem diameter are dominant phenotypic data for biomass sorghum yield prediction. Extraction of these traits by machine vision during the growing season significantly reduces labor and time cost for large breeding programs. An automated 3D point cloud processing pipeline was developed to quantify different phenotypic variations in plant architecture of infield biomass sorghum. The input point cloud was generated by three side-view stereo camera heads placed vertically to capture extremely high plants. The features were extracted on a row plot basis instead of individual due to severe occlusion caused by densely populated leaves. Available features include plant height, plant width, vegetation volume index, and vegetation area index. Our strategy was to slice the point cloud along row direction into several equal volume slices and sum up the feature values with weights based on the point population and distribution in each volume slice. Therefore, the results were robust against empty space and abnormal individuals in the row plot. In addition, a semi-automated user interface was developed for users to measure stem diameters from the stereo images according to their specific sampling strategies. Users only need to zoom in on a stem segment and pick four corners of the rectangular segment. Metric measurement is then computed automatically based on image patch stereo matching using normalized cross correlation. The extracted stem diameters were compared to manual measurements in the field and a high correlation was obtained. The extracted features revealed great potential for automated field-based high-throughput phenotyping for plant architecture

A high-throughput, field-based phenotyping technology for tall biomass crops

Recent advances in omics technologies have not been accompanied by equally efficient, cost-effective and accurate phenotyping methods required to dissect the genetic architecture of complex traits. Even though high-throughput phenotyping platforms have been developed for controlled environments, field-based aerial and ground technologies have only been designed and deployed for short stature crops. Therefore, we developed and tested Phenobot 1.0, an auto-steered and self-propelled field-based high-throughput phenotyping platform for tall dense canopy crops, such as sorghum (Sorghum bicolor L. Moench). Phenobot 1.0 was equipped with laterally positioned and vertically stacked stereo RGB cameras. Images collected from 307 diverse sorghum lines were reconstructed in 3D for feature extraction. User interfaces were developed and multiple algorithms were evaluated for their accuracy in estimating plant height and stem diameter. Tested feature extraction methods included: i) User-interactive Individual Plant Height Extraction based on dense stereo 3D reconstruction (UsIn-PHe); ii) Automatic Hedge-based Plant Height Extraction (Auto-PHe) based on dense stereo 3D reconstruction; iii) User-interactive Dense Stereo Matching Stem Diameter Extraction (DenS-Di); and iv) User-interactive Image Patch Stereo Matching Stem Diameter Extraction (IPaS-Di). Comparative genome-wide association analysis and ground-truth validation demonstrated that both UsIn-PHe and Auto-PHe were accurate methods to estimate plant height while Auto-PHe had the additional advantage of being a completely automated process. For stem diameter, IPaS-Di generated the most accurate estimates of this biomass-related architectural trait. In summary, our technology was proven robust to obtain ground-based high-throughput plant architecture parameters of sorghum, a tall and densely planted crop species

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