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Reliable seed yield estimation is an indispensable step in plant breeding programs geared towards cultivar development in major row crops. The objective of this study is to develop a machine learning (ML) approach adept at soybean ( Glycine max L. (Merr.)) pod counting to enable genotype seed yield rank prediction from in-field video data collected by a ground robot. To meet this goal, we developed a multiview image-based yield estimation framework utilizing deep learning architectures. Plant images captured from different angles were fused to estimate the yield and subsequently to rank soybean genotypes for application in breeding decisions. We used data from controlled imaging environment in field, as well as from plant breeding test plots in field to demonstrate the efficacy of our framework via comparing performance with manual pod counting and yield estimation. Our results demonstrate the promise of ML models in making breeding decisions with significant reduction of time and human effort and opening new breeding method avenues to develop cultivars.
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Soybean maturity prediction using two‐dimensional contour plots from drone‐based time series imagery
Abstract Plant breeding programs require assessment and understanding of days to maturity for accurate selection and placement of entries in appropriate tests. Soybean [Glycine max(L.) Merr.] breeding programs, in the early stages of the breeding pipeline, assign relative maturity ratings to experimental varieties that indicate their suitable maturity zones. Traditionally, the estimation of maturity rating value has involved breeders manually inspecting fields and assessing maturity value visually. This approach relies heavily on expert judgment, making it subjective and demanding considerable time and effort. This study aimed to develop a machine learning (ML) model for evaluating soybean maturity using uncrewed aerial system (UAS)–based time series imagery. Images were captured at 3‐day intervals, beginning as the earliest varieties started maturing and continuing until the last varieties fully matured. The data collected for this experiment consisted of 22,043 plots collected across 3 years and represent relative maturity groups 1.6–3.9. We utilized contour plot images extracted from the time series UAS imagery as input for a neural network model. This contour plot approach encoded the temporal and spatial variation within each plot into a single image. A deep learning model was trained to utilize this contour plot to predict maturity ratings. This model demonstrates a significant improvement in accuracy and robustness, achieving up to 85% accuracy. The predictive model offers a scalable, objective, and efficient means of assessing crop maturity, enabling phenomics and ML approaches to reduce the reliance on manual inspection and subjective assessment, thereby saving time and resources in a breeding program.
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- Award ID(s):
- 1954556
- PAR ID:
- 10673221
- Publisher / Repository:
- The Plant Phenome Journal
- Date Published:
- Journal Name:
- The Plant Phenome Journal
- Volume:
- 8
- Issue:
- 1
- ISSN:
- 2578-2703
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript
- Journal Article:
- https://doi.org/10.1002/ppj2.70036
