ConvLSTM–ViT: A Deep Neural Network for Crop Yield Prediction Using Earth Observations and Remotely Sensed Data

This article introduces an approach for soybean yield prediction by integrating convolutional long short-term memory (ConvLSTM), three-dimensional convolutional neural network (3D-CNN), and vision transformer (ViT). By utilizing multispectral remote sensing data, our model leverages the spatial hierarchy of 3D-CNNs, the temporal sequencing capabilities of ConvLSTM, and the global context analysis of ViTs to capture complex patterns in agricultural datasets. The integration of these advanced methodologies allows for a comprehensive analysis of both spatial and temporal aspects of crop growth, enabling more accurate and robust predictions. Our experimental results demonstrate that the proposed model…