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Unmanned aerial system based tomato yield estimation using machine learning
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Paper Abstract

Recent years have witnessed enormous growth in Unmanned Aircraft System (UAS) and sensor technology which made it possible to collect high spatial and temporal resolutions data over the crops throughout the growing season. The objective of this research is to develop a novel machine learning framework for marketable tomato yield estimation using multi-source and spatio-temporal remote sensing data collected from 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, Excessive Greenness Index along with weather information such as humidity, precipitation, temperature, solar radiations and crop evapotranspiration (ETc) as input and predicts the corresponding marketable yield. The predicted yield is validated using the actual harvested yield. Breeders may be able to use the predicted yield as a parameter for genotype selection so that they can not only increase their experiment size for faster genotype selection but also to make efficient and informed decision on best performing genotypes. Moreover, yield prediction maps can be used to develop within-field management zones to optimize field management practices.

Paper Details

Date Published: 14 May 2019
PDF: 10 pages
Proc. SPIE 11008, Autonomous Air and Ground Sensing Systems for Agricultural Optimization and Phenotyping IV, 110080O (14 May 2019); doi: 10.1117/12.2519129
Show Author Affiliations
Akash Ashapure, Texas A&M Univ. Corpus Christi (United States)
Sungchan Oh, Texas A&M Univ. Corpus Christi (United States)
Thiago G. Marconi, Texas A&M AgriLife Research and Extension Ctr. (United States)
Anjin Chang, Texas A&M Univ. Corpus Christi (United States)
Jinha Jung, Texas A&M Univ. Corpus Christi (United States)
Juan Landivar, Texas A&M AgriLife Research and Extension Ctr. (United States)
Juan Enciso, Texas A&M AgriLife Research and Extension Ctr. (United States)


Published in SPIE Proceedings Vol. 11008:
Autonomous Air and Ground Sensing Systems for Agricultural Optimization and Phenotyping IV
J. Alex Thomasson; Mac McKee; Robert J. Moorhead, Editor(s)

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