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

arXiv: https://arxiv.org/abs/2012.03129 · HTML: https://mlsanity.com/paper/2012.03129/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Dec 2020.

## Built on by (most cited first, 16 of 16)

- [MMST-ViT: Climate Change-aware Crop Yield Prediction via Multi-Modal Spatial-Temporal Vision Transformer](https://mlsanity.com/paper/2309.09067/) (2023)
- [An Open and Large-Scale Dataset for Multi-Modal Climate Change-aware Crop Yield Predictions](https://mlsanity.com/paper/2406.06081/) (2024)
- [WheatNet: A Lightweight Convolutional Neural Network for High-throughput Image-based Wheat Head Detection and Counting](https://mlsanity.com/paper/2103.09408/) (2021)
- [Extreme Gradient Boosting for Yield Estimation compared with Deep Learning Approaches](https://mlsanity.com/paper/2208.12633/) (2022)
- [Common Practices and Taxonomy in Deep Multi-view Fusion for Remote Sensing Applications](https://mlsanity.com/paper/2301.01200/) (2022)
- [A systematic review of the use of Deep Learning in Satellite Imagery for Agriculture](https://mlsanity.com/paper/2210.01272/) (2022)
- [Winter wheat yield prediction using convolutional neural networks from environmental and phenological data](https://mlsanity.com/paper/2105.01282/) (2021)
- [Grouping Shapley Value Feature Importances of Random Forests for explainable Yield Prediction](https://mlsanity.com/paper/2304.07111/) (2023)
- [Deep Learning Meets Process-Based Models: A Hybrid Approach to Agricultural Challenges](https://mlsanity.com/paper/2504.16141/) (2025)
- [California Crop Yield Benchmark: Combining Satellite Image, Climate, Evapotranspiration, and Soil Data Layers for County-Level Yield Forecasting of Over 70 Crops](https://mlsanity.com/paper/2506.10228/) (2025)
- [From Time-series Generation, Model Selection to Transfer Learning: A Comparative Review of Pixel-wise Approaches for Large-scale Crop Mapping](https://mlsanity.com/paper/2507.12590/) (2025)
- [An Applied Deep Learning Approach for Estimating Soybean Relative Maturity from UAV Imagery to Aid Plant Breeding Decisions](https://mlsanity.com/paper/2108.00952/) (2021)
- [Bagged Polynomial Regression and Neural Networks](https://mlsanity.com/paper/2205.08609/) (2022)
- [In-Season Crop Progress in Unsurveyed Regions using Networks Trained on Synthetic Data](https://mlsanity.com/paper/2212.06896/) (2022)
- [DFYP: A Dynamic Fusion Framework with Spectral Channel Attention and Adaptive Operator learning for Crop Yield Prediction](https://mlsanity.com/paper/2507.05849/) (2025)
- [PhenoYieldNet: Learning Crop-Aware Phenological Responses for Multi-Crop Yield Prediction](https://mlsanity.com/paper/2605.23478/) (2026)

## Abstract (arXiv)

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.