MLeNet -CNN fusion model for crop yield prediction with extended normalization process
1 Research Scholar, Bir Tikendrajit University, Manipur, India and Lecturer, AISSMS Polytechnic, Pune, India.
2 Associate Professor, Bir Tikendrajit University, Manipur, India.
3 Assistant Professor, Department of Engineering Sciences, AISSMS Institute of Information Technology, Pune, India.
Research Article
Open Access Research Journal of Engineering and Technology, 2025, 09(02), 084-102.
Article DOI: 10.53022/oarjet.2025.9.2.0100
Publication history:
Received on 04 November 2025; revised on 10 December 2025; accepted on 12 December 2025
Abstract:
Crop yield prediction (CYP) at the field level plays a crucial role in evaluating agricultural commodities plans for import-export strategies, agricultural production and increasing farmer incomes. Traditional methods, which depend on historical data, weather conditions, and agronomic models, have been greatly improved through the application of machine learning technologies. This paper proposes an innovative M-Let and CNN-based Fusion model for Crop Yield Prediction (MCF-CYP) model. The input data undergoes preprocessing, where a Three-tier Data Normalization (TDN) technique is applied to standardize the values, ensuring all features are on a consistent scale for improved model effectiveness. Next, feature extraction is performed to identify the key characteristics of the data. In this case, features like Beckenstein Hawking with Deng Belief-based Renya Entropy (BHDB-RE) features, information gain, and various statistical measures are extracted to capture essential insights related to crop yield. These features are then passed into the prediction model, which utilizes two deep learning architectures: Multi-head LeNet (M-Let) and Convolutional Neural Networks (CNN). Both models analyze the complex patterns in the data and generate accurate crop yield predictions. Finally, the predicted yield is outputted, providing valuable insights for crop management and other agricultural decisions.
Keywords:
Crop Yield Prediction; Deep Learning; Renya Entropy; Three-Tier Data Normalization
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Copyright © 2025 Author(s) retain the copyright of this article. This article is published under the terms of the Creative Commons Attribution Liscense 4.0