Multispectral Crop Yield Prediction Using Neural Network

DOI

10.2991/978-94-6463-866-0_74How to use a DOI?

Keywords

Crop Yield Prediction; Deep Learning; Multispectral Data; 3D Convolutional Neural Network; Attention-based ConvLSTM; Precision Agriculture; Spatiotemporal Modeling

Abstract

Accurate crop yield prediction is vital for enhancing food security and supporting data-driven agricultural planning. Existing models often fail to capture the intricate relationships between environmental conditions, farming practices, and crop responses. This study presents a novel deep learning-based decision support system that integrates multispectral satellite imagery, weather patterns, soil characteristics, and farm management data to forecast crop yields with greater precision.

The proposed architecture combines 3-D Convolutional Neural Networks (3D-CNN) and Attention Based Convolutional LSTM (ConvLSTM) to effectively capture spatiotemporal dependencies across crop growth cycles. An attention mechanism enhances interpretability by highlighting critical features influencing yield variation at different growth stages.

Extensive experiments conducted on diverse agricultural datasets demonstrate that our model achieves higher predictive accuracy than existing approaches such as CNN-LSTM and DeepYield, with a 12.5% reduction in RMSE and a 10% improvement in MAE. These findings highlight the model’s robustness and adaptability across varied agro-climatic zones. This research contributes an interpretable and scalable framework for precision agriculture, supporting informed decision-making by farmers and policymakers.

Copyright

© 2025 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - S. Hariharan
AU  - T. Hemanathan
AU  - V. Akashkarthi
AU  - D. Punitha
PY  - 2025
DA  - 2025/10/31
TI  - Multispectral Crop Yield Prediction Using Neural Network
BT  - Proceedings of the International Conference on Intelligent Systems and Digital Transformation (ICISD 2025)
PB  - Atlantis Press
SP  - 913
EP  - 924
SN  - 2589-4919
UR  - https://doi.org/10.2991/978-94-6463-866-0_74
DO  - 10.2991/978-94-6463-866-0_74
ID  - Hariharan2025
ER  -