A multi-temporal multi-spectral attention-augmented deep convolution neural network with contrastive learning for crop yield prediction

• URL: http://arxiv.org/abs/2509.15966v1
• Date: Fri, 19 Sep 2025 13:24:33 GMT
• Title: A multi-temporal multi-spectral attention-augmented deep convolution neural network with contrastive learning for crop yield prediction
• Authors: Shalini Dangi, Surya Karthikeya Mullapudi, Chandravardhan Singh Raghaw, Shahid Shafi Dar, Mohammad Zia Ur Rehman, Nagendra Kumar,
• Abstract summary: We propose a novel Multi-Temporal Multi-temporal Yield Prediction Network, MTMS- YieldNet.<n>It integrates spectral data with multi-temporal information to capture the correlations and dependencies between them.<n>The outstanding performance of MTMS- YieldNet improves yield predictions and provides valuable insights that can assist in making better decisions.
• Score: 4.880724009067774
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Precise yield prediction is essential for agricultural sustainability and food security. However, climate change complicates accurate yield prediction by affecting major factors such as weather conditions, soil fertility, and farm management systems. Advances in technology have played an essential role in overcoming these challenges by leveraging satellite monitoring and data analysis for precise yield estimation. Current methods rely on spatio-temporal data for predicting crop yield, but they often struggle with multi-spectral data, which is crucial for evaluating crop health and growth patterns. To resolve this challenge, we propose a novel Multi-Temporal Multi-Spectral Yield Prediction Network, MTMS-YieldNet, that integrates spectral data with spatio-temporal information to effectively capture the correlations and dependencies between them. While existing methods that rely on pre-trained models trained on general visual data, MTMS-YieldNet utilizes contrastive learning for feature discrimination during pre-training, focusing on capturing spatial-spectral patterns and spatio-temporal dependencies from remote sensing data. Both quantitative and qualitative assessments highlight the excellence of the proposed MTMS-YieldNet over seven existing state-of-the-art methods. MTMS-YieldNet achieves MAPE scores of 0.336 on Sentinel-1, 0.353 on Landsat-8, and an outstanding 0.331 on Sentinel-2, demonstrating effective yield prediction performance across diverse climatic and seasonal conditions. The outstanding performance of MTMS-YieldNet improves yield predictions and provides valuable insights that can assist farmers in making better decisions, potentially improving crop yields.

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