Enhancing Crop Yield Prediction Using Deep Convolutional Neural Networks: A Data-Driven Approach for Agricultural Optimization

DOI

10.2991/978-94-6239-654-8_68How to use a DOI?

Keywords

Preprocessing; Overall Design; Proposed Model; deep learning; multitask learning

Abstract

Farmers have become more and more interested in data-driven methods over the past few years because they help them to see the future problems. In smart farming, they are often used to predict what kinds of plants will grow. The study’s goal was to create a useful hybrid deep learning model that could predict how much rice crops would yield by mixing a regression model with a deep learning classification model. It was possible because the layers were shared. Stats can be used to do three different types of tests. These are the Pearson correlation coefficients (PCC), the Shapley additive reasons (SHAP), and the repeated feature elimination with cross-validation (RFECV). The study’s goal is to find the most important parts of the forecast goal so that the model can be trained more quickly. Putting together all the different parts of data that were sent is the first thing that needs to be done to get the data ready. The features are then turned into a three-dimensional grid. You can figure out how much rice was grown if the R-squared value is 0.64 and the RMSE value is 344.56. AI networks (RMSE = 550.03, R-squared = 0.09) and multi-parametric deep neural networks (MDNNs) (RMSE = 370.80, R-squared = 0.59) are not as good as the model that was suggested. The F1 test gave it a score of 94%. It’s now much better at telling the difference between high yield and low yield.

Copyright

© 2026 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.

Download article (PDF)

TY  - CONF
AU  - K. Sathiya Priya
AU  - C. Rajabhushanam
PY  - 2026
DA  - 2026/04/24
TI  - Enhancing Crop Yield Prediction Using Deep Convolutional Neural Networks: A Data-Driven Approach for Agricultural Optimization
BT  - Proceedings of the Global Conference on Sustainable Energy Systems, Smart Electronics and Intelligent Computing (GCSESEIC 2025)
PB  - Atlantis Press
SP  - 875
EP  - 885
SN  - 2352-5401
UR  - https://doi.org/10.2991/978-94-6239-654-8_68
DO  - 10.2991/978-94-6239-654-8_68
ID  - Priya2026
ER  -