ItemArticle

3TFL-XLnet-CP: A Novel Transformer-Based Crop Yield Prediction Framework with Weighted Loss Based 3-Tier Feature Learning Model

Title
3TFL-XLnet-CP: A Novel Transformer-Based Crop Yield Prediction Framework with Weighted Loss Based 3-Tier Feature Learning Model
Creator
Anoop, G.L.; Nandini, C.; Naresh, E.
Description
The advancement of crop yield prediction through artificial intelligence (AI) has gained significant attention. However, the existing AI-based approaches for maximizing agricultural productivity, specifically in crop yield prediction, have not consistently delivered satisfactory results. In response to this challenge, we propose a novel framework named as Three Tier Feature Learning with XLnet based Crop Prediction (3TFL-XLnet-CP) that enhances agricultural productivity by accurately predicting crop yield. The 3TFL-XLnet-CP framework employs a three-tier feature learning approach in combination with the powerful XLnet transformer-based crop prediction model. The three-tier feature learning involves the integration of Spiking Neural Network (SNN), Graphical Neural Network (GNN), and Convolutional Neural Network (CNN) to extract distinct feature vectors from the preprocessed data. These feature vectors are then concatenated using Jaccard Similarity to measure their similarity score. Additionally, a weighted Loss function is introduced to optimize feature learning, further enhanced by a novel self-adaptive Spider Monkey Optimization algorithm (SASMO). The concatenated features are subsequently fed into the classification layer for making precise crop yield predictions. The proposed model is implemented using the Python platform and evaluated against existing models such as ANN, RNN, DNN, and BiLSTM. The comparison demonstrates the superiority of our proposed 3TFL-XLnet-CP framework in accurately predicting crop yield. The Author(s) 2025.
Source
SN Computer Science;Volume;6;Issue;3;Article No.;275;
Date
01-01-2025
Publisher
Springer
Subject
3TFL-XLnet-CP; Convolutional neural network; Crop yield prediction; Graphical neural network; Spiking neural network
Coverage
Anoop G.L., Department of Computer Science and Engineering, Christ University, Karnataka, Bengaluru, India; Nandini C., Department of Computer Science and Engineering, Dayananda Sagar Academy of Technology and Management, Karnataka, Bengaluru, India; Naresh E., Department of Information Technology, Manipal Institute of Technology Bengaluru, Manipal Academy of Higher Education, Manipal, India
Rights
All Open Access; Hybrid Gold Open Access
Relation
ISSN: 2662995X;
Format
online
Language
English
Type
Article

Citation

Anoop, G.L.; Nandini, C.; Naresh, E., “3TFL-XLnet-CP: A Novel Transformer-Based Crop Yield Prediction Framework with Weighted Loss Based 3-Tier Feature Learning Model,” CHRIST (Deemed To Be University) Institutional Repository, accessed June 18, 2026, https://archives.christuniversity.in/items/show/22138.

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