Attention-based Multi-modal Deep Learning Model of Spatio-temporal Crop Yield Prediction with Satellite, Soil and Climate Data

TL;DR

This paper introduces an attention-based multi-modal deep learning framework that combines satellite, soil, and climate data for highly accurate spatio-temporal crop yield prediction, outperforming traditional models.

Contribution

The novel ABMMDLF model integrates multi-source data and temporal attention mechanisms to improve crop yield prediction accuracy over existing methods.

Findings

Achieved an R^2 score of 0.89, significantly better than baseline models.

Effectively combines satellite imagery, meteorological data, and soil properties.

Uses CNNs and temporal attention to adaptively focus on important phenological periods.

Abstract

Crop yield prediction is one of the most important challenge, which is crucial to world food security and policy-making decisions. The conventional forecasting techniques are limited in their accuracy with reference to the fact that they utilize static data sources that do not reflect the dynamic and intricate relationships that exist between the variables of the environment over time [5,13]. This paper presents Attention-Based Multi-Modal Deep Learning Framework (ABMMDLF), which is suggested to be used in high-accuracy spatio-temporal crop yield prediction. The model we use combines multi-year satellite imagery, high-resolution time-series of meteorological data and initial soil properties as opposed to the traditional models which use only one of the aforementioned factors [12, 21]. The main architecture involves the use of Convolutional Neural Networks (CNN) to extract spatial features and a Temporal Attention Mechanism to adaptively weight important phenological periods targeted by the algorithm to change over time and condition on spatial features of images and video sequences. As can be experimentally seen, the proposed research work provides an R^2 score of 0.89, which is far better than the baseline models do.