Translator Disclaimer
Presentation + Paper
14 May 2019 Unmanned aerial system based tomato yield estimation using machine learning
Author Affiliations +
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.
Conference Presentation
© (2019) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Akash Ashapure, Sungchan Oh, Thiago G. Marconi, Anjin Chang, Jinha Jung, Juan Landivar, and Juan Enciso "Unmanned aerial system based tomato yield estimation using machine learning", Proc. SPIE 11008, Autonomous Air and Ground Sensing Systems for Agricultural Optimization and Phenotyping IV, 110080O (14 May 2019); https://doi.org/10.1117/12.2519129
PROCEEDINGS
10 PAGES + PRESENTATION

SHARE
Advertisement
Advertisement
Back to Top