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Environmental stresses such as drought and heat can cause substantial yieldloss in agriculture. As such, hybrid crops that are tolerant to drought andheat stress would produce more consistent yields compared to the hybrids thatare not tolerant to these stresses. In the 2019 Syngenta Crop Challenge,Syngenta released several large datasets that recorded the yield performancesof 2,452 corn hybrids planted in 1,560 locations between 2008 and 2017 andasked participants to classify the corn hybrids as either tolerant orsusceptible to drought stress, heat stress, and combined drought and heatstress. However, no data was provided that classified any set of hybrids astolerant or susceptible to any type of stress. In this paper, we present anunsupervised approach to solving this problem, which was recognized as one ofthe winners in the 2019 Syngenta Crop Challenge. Our results labeled 121hybrids as drought tolerant, 193 as heat tolerant, and 29 as tolerant to bothstresses.

Classification of Crop Tolerance to Heat and Drought: A Deep Convolutional Neural Networks Approach