Recent years have witnessed enormous growth in Unmanned Aircraft System (UAS) and sensor technology which madeit possible to collect high spatial and temporal resolutions data over the crops throughout the growing season. Theobjective of this research is to develop a novel machine learning framework for marketable tomato yield estimation usingmulti-source and spatio-temporal remote sensing data collected from UAS. The proposed machine learning model isbased 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 correspondingmarketable yield. The predicted yield is validated using the actual harvested yield. Breeders may be able to use thepredicted yield as a parameter for genotype selection so that they can not only increase their experiment size for fastergenotype selection but also to make efficient and informed decision on best performing genotypes. Moreover, yieldprediction maps can be used to develop within-field management zones to optimize field management practices.
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