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

TL;DR

This paper introduces YieldNet, a deep learning model that predicts corn and soybean yields simultaneously from remote sensing data, leveraging transfer learning and a novel loss function to improve accuracy and enable early predictions.

Contribution

The paper presents a novel multi-crop yield prediction model using transfer learning and a new loss function, enabling concurrent estimation of corn and soybean yields from remote sensing data.

Findings

Accurately predicts yields 1-4 months before harvest.

Achieves MAE of approximately 8.7% of average yield.

Competitive with state-of-the-art methods.

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.