Unmanned aerial system based cotton genotype selection using machine learning (Conference Presentation)

Jinha Jung; Akash Ashapure; Murilo Maeda; Juan Landivar; Anjin Chang; Junho Yeom; Steven Hague; Wayne Smith

doi:10.1117/12.2323858

15 May 2018 Unmanned aerial system based cotton genotype selection using machine learning (Conference Presentation)

Jinha Jung, Akash Ashapure, Murilo Maeda, Juan Landivar, Anjin Chang, Junho Yeom, Steven Hague, Wayne Smith

Proceedings Volume 10664, Autonomous Air and Ground Sensing Systems for Agricultural Optimization and Phenotyping III; 106640P (2018) https://doi.org/10.1117/12.2323858
Event: SPIE Commercial + Scientific Sensing and Imaging, 2018, Orlando, FL, United States

Abstract

The objective of this research is to develop a novel machine learning framework for automatic cotton genotype selection using multi-source and spatio-temporal remote sensing data collected from Unmanned Aerial System (UAS). The proposed machine learning model is based on Artificial Neural Network (ANN) and it takes UAS based multi-temporal features such as canopy cover, canopy height, canopy volume, Normalized Difference Vegetation Index (NDVI), Excessive Greenness Index along with non-temporal features such as cotton boll count, boll size and boll volume as input and predicts the corresponding yield. Testing the performance of our model using actual yield resulted in an R square value of approximately 0.9. The proposed cotton genotype selection model is expected to revolutionize the cotton breeding research by providing valuable tools to cotton breeders so that they can not only increase their experiment size for faster genotype selection but also make efficient and informed decision on best performing genotype selection.

Conference Presentation

Citation Download Citation

Jinha Jung, Akash Ashapure, Murilo Maeda, Juan Landivar, Anjin Chang, Junho Yeom, Steven Hague, and Wayne Smith "Unmanned aerial system based cotton genotype selection using machine learning (Conference Presentation)", Proc. SPIE 10664, Autonomous Air and Ground Sensing Systems for Agricultural Optimization and Phenotyping III, 106640P (15 May 2018); https://doi.org/10.1117/12.2323858

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KEYWORDS

Machine learning

Artificial neural networks

Performance modeling

Remote sensing

Vegetation

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