ItemConference Paper

A Novel Decision Tree and LSTM Powered Intelligent Agent System for Early Detection of Vegetable Plant Diseases

Title
A Novel Decision Tree and LSTM Powered Intelligent Agent System for Early Detection of Vegetable Plant Diseases
Creator
Sreelatha, Bolisetty; Rajamohanan, Rajasree; Kalaiarasi, G.; Praveen, Rvs; Bugge, Bhagya Prasad; John, Tegil J
Description
The early and precise diagnosis of vegetable plant diseases is crucial for sustainable agriculture since these diseases have a major impact on crop output and quality. Disease identification performance is examined in this work through a robust detection pipeline that examines the effects of several preprocessing techniques, class imbalance handling strategies, and deep learning classifiers. To better represent data and increase model knowledge of illness characteristics, the GLCM was used to extract texture information. By combining XGBoost with LSTM networks, a new hybrid model was created. While XGBoost is great at classifying structured data, the LSTM component is great at evaluating sequential data, which allows it to capture patterns and trends in the evolution of plant diseases over time. Better and more meaningful forecasts are made possible by this supplementary integration. By surpassing more conventional methods of illness classification, the suggested LSTM-XG model attained a remarkable prediction accuracy of 99.34%. An important factor in achieving this outcome was the use of hybrid modeling in conjunction with thorough preprocessing and correction of class imbalance. Finally, the LSTM-XG model shows great promise for practical use in precision farming. Its precision and efficiency in identifying illnesses in vegetable plants might facilitate prompt action, lessen crop loss, and encourage better farming methods. 2025 IEEE.
Source
Proceedings of the 6th International Conference on Electronics and Sustainable Communication Systems, ICESC 2025;pp.1617-1622
Date
01-01-2025
Publisher
Institute of Electrical and Electronics Engineers Inc.
Subject
Extreme Gradient Boosting (XGBoost); Gray-Level Co-occurrence Matrix (GLCM); the Long Short-Term Memory (LSTM)
Coverage
Sreelatha B., Geethanjali College of Engineering and Technology, Department of Electronics and Communication Engineering, Telangana, Hyderabad, India; Rajamohanan R., SP Jain School of Global Management, Department of Artificial Intelligence and Data Science, Dubai, United Arab Emirates; Kalaiarasi G., Dhanalakshmi Srinivasan College of Engineering, Department of Food Technology, Coimbatore, India; Praveen R., Director - Utilities Americas LTIMindtree, Houston, TX, United States; Bugge B.P., S.R.K.R Engineering College, Department of Electronics and Communication Engineering, Andhra Pradesh, India; John T.J., Christ University Dharmaram College Hosur, Department of Computer Science, Karnataka, Bengaluru, India
Rights
Restricted Access; Hardcopy may be available in the library
Relation
ISBN: 979-833155503-0;
Format
online
Language
English
Type
Conference paper

Citation

Sreelatha, Bolisetty; Rajamohanan, Rajasree; Kalaiarasi, G.; Praveen, Rvs; Bugge, Bhagya Prasad; John, Tegil J, “A Novel Decision Tree and LSTM Powered Intelligent Agent System for Early Detection of Vegetable Plant Diseases,” CHRIST (Deemed To Be University) Institutional Repository, accessed June 19, 2026, https://archives.christuniversity.in/items/show/25997.

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