Simultaneous Corn and Soybean Yield Prediction from Remote Sensing Data Using Deep Transfer Learning

Abstract

Large-scale crop yield estimation is, in part, made possible due to the availability of remote sensing data allowing for the continuous monitoring of crops throughout their growth cycle. Having this information allows stakeholders the ability to make real-time decisions to maximize yield potential. Although various models exist that predict yield from remote sensing data, there currently does not exist an approach that can estimate yield for multiple crops simultaneously, and thus leads to more accurate predictions. A model that predicts the yield of multiple crops and concurrently considers the interaction between multiple crop yields. We propose a new convolutional neural network model called YieldNet which utilizes a novel deep learning framework that uses transfer learning between corn and soybean yield predictions by sharing the weights of the backbone feature extractor. Additionally, to consider the multi-target response variable, we propose a new loss function. We conduct our experiment using data from 1,132 counties for corn and 1,076 counties for soybean across the United States. Numerical results demonstrate that our proposed method accurately predicts corn and soybean yield from one to four months before the harvest with a MAE being 8.74% and 8.70% of the average yield, respectively, and is competitive to other state-of-the-art approaches.

arXiv comment14 pages, 8 figures, 7 tables

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Cited by in corpus (5)

• Winter wheat yield prediction using convolutional neural networks from environmental and phenological data
• Extreme Gradient Boosting for Yield Estimation compared with Deep Learning Approaches
• Common Practices and Taxonomy in Deep Multi-view Fusion for Remote Sensing Applications
• A systematic review of the use of Deep Learning in Satellite Imagery for Agriculture
• From Time-series Generation, Model Selection to Transfer Learning: A Comparative Review of Pixel-wise Approaches for Large-scale Crop Mapping