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Iowa State University

Soybean

Mechanical Engineering Publications

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Computer Vision And Machine Learning Enabled Soybean Root Phenotyping Pipeline, Kevin G. Falk, Talukder Z. Jubery, Seyed V. Mirnezami, Kyle A. Parmley, Soumik Sarkar, Arti Singh, Baskar Ganapathysubramanian, Asheesh K. Singh Jan 2020

Computer Vision And Machine Learning Enabled Soybean Root Phenotyping Pipeline, Kevin G. Falk, Talukder Z. Jubery, Seyed V. Mirnezami, Kyle A. Parmley, Soumik Sarkar, Arti Singh, Baskar Ganapathysubramanian, Asheesh K. Singh

Mechanical Engineering Publications

Background Root system architecture (RSA) traits are of interest for breeding selection; however, measurement of these traits is difficult, resource intensive, and results in large variability. The advent of computer vision and machine learning (ML) enabled trait extraction and measurement has renewed interest in utilizing RSA traits for genetic enhancement to develop more robust and resilient crop cultivars. We developed a mobile, low-cost, and high-resolution root phenotyping system composed of an imaging platform with computer vision and ML based segmentation approach to establish a seamless end-to-end pipeline - from obtaining large quantities of root samples through image based trait processing and ...


Plant Disease Identification Using Explainable 3d Deep Learning On Hyperspectral Images, Koushik Nagasubramanian, Sarah Jones, Asheesh K. Singh, Soumik Sarkar, Arti Singh, Baskar Ganapathysubramanian Aug 2019

Plant Disease Identification Using Explainable 3d Deep Learning On Hyperspectral Images, Koushik Nagasubramanian, Sarah Jones, Asheesh K. Singh, Soumik Sarkar, Arti Singh, Baskar Ganapathysubramanian

Mechanical Engineering Publications

Background

Hyperspectral imaging is emerging as a promising approach for plant disease identification. The large and possibly redundant information contained in hyperspectral data cubes makes deep learning based identification of plant diseases a natural fit. Here, we deploy a novel 3D deep convolutional neural network (DCNN) that directly assimilates the hyperspectral data. Furthermore, we interrogate the learnt model to produce physiologically meaningful explanations. We focus on an economically important disease, charcoal rot, which is a soil borne fungal disease that affects the yield of soybean crops worldwide.

Results

Based on hyperspectral imaging of inoculated and mock-inoculated stem images, our 3D ...