Version 1
: Received: 18 October 2023 / Approved: 19 October 2023 / Online: 19 October 2023 (07:00:51 CEST)
Version 2
: Received: 6 January 2024 / Approved: 8 January 2024 / Online: 8 January 2024 (09:30:26 CET)
How to cite:
Chattopadhyay, S.; Gupta, A.; Carroll, M.; Raigne, J.; Ganapathysubramanian, B.; Singh, A.; Sarkar, S. A Comprehensive Study on Soybean Yield Prediction Using Soil and Hyperspectral Reflectance Data. Preprints2023, 2023101232. https://doi.org/10.20944/preprints202310.1232.v2
Chattopadhyay, S.; Gupta, A.; Carroll, M.; Raigne, J.; Ganapathysubramanian, B.; Singh, A.; Sarkar, S. A Comprehensive Study on Soybean Yield Prediction Using Soil and Hyperspectral Reflectance Data. Preprints 2023, 2023101232. https://doi.org/10.20944/preprints202310.1232.v2
Chattopadhyay, S.; Gupta, A.; Carroll, M.; Raigne, J.; Ganapathysubramanian, B.; Singh, A.; Sarkar, S. A Comprehensive Study on Soybean Yield Prediction Using Soil and Hyperspectral Reflectance Data. Preprints2023, 2023101232. https://doi.org/10.20944/preprints202310.1232.v2
APA Style
Chattopadhyay, S., Gupta, A., Carroll, M., Raigne, J., Ganapathysubramanian, B., Singh, A., & Sarkar, S. (2024). A Comprehensive Study on Soybean Yield Prediction Using Soil and Hyperspectral Reflectance Data. Preprints. https://doi.org/10.20944/preprints202310.1232.v2
Chicago/Turabian Style
Chattopadhyay, S., Asheesh Singh and Soumik Sarkar. 2024 "A Comprehensive Study on Soybean Yield Prediction Using Soil and Hyperspectral Reflectance Data" Preprints. https://doi.org/10.20944/preprints202310.1232.v2
Abstract
Soybean yield prediction is a challenging problem in plant breeding that is often affected by many different factors simultaneously. Hyperspectral reflectance data from plants and soil data provide breeders with useful information about soybean plant health and using these different types of data to predict yield is an active area of research. Furthermore, breeding programs encounter challenges such as data imbalance and external factors like genotype variability across different environments, which present significant hurdles in the development of yield prediction models for large-scale breeding programs. In this work, we perform a comprehensive study of predicting yield using both hyperspectral reflectance and soil data to understand what scenario's offer the best chances of predicting yield with high accuracy. We demonstrate a cluster based ensemble approach for yield prediction using hyperspectral reflectance data that can perform well for large scale breeding programs by efficiently harnessing useful information from data through an unsupervised approach.
Keywords
ML:Ensemble methods; ML: applications
Subject
Biology and Life Sciences, Agricultural Science and Agronomy
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Commenter: Souradeep Chattopadhyay
Commenter's Conflict of Interests: Author