JOURNAL OF ADVANCES IN SCIENCE, ENGINEERING AND TECHNOLOGY

Vol.1, Issue.1, Jan-Mar 2025

Standard Journal Issues

 

Machine Learning and Plant Disease Detection: A Brief Review
Swarajya Lakshmi .P    

​Research Scholar, Department of Computer Science and Engineering Manas Global University, Bhopal, Madhya Pradesh, India

Asst.Professor, Department of Computer Science and Engineering, Anurag Universiy, Hyderabad, Telangana, India

 

Abstract

 

The global food security and agricultural sustainability face a major threat from plant diseases. Effective management requires both early and accurate identification of these diseases. The development of Artificial Intelligence (AI) has led Machine Learning (ML) techniques to become promising tools for automated disease detection and classification. The paper presents an extensive evaluation of current progress in employing ML methods for plant disease detection. The role of image processing combined with deep learning and traditional ML classifiers in disease identification is discussed in detail. A detailed literature survey is provided followed by a method- dataset-performance metrics comparison. The paper also discusses future research directions and challenges.

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​Key words: Machine learning, Plant disease detection, Deep learning, Image processing

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References

1. S. Sladojevic et al., “Deep neural networks based recognition of plant diseases by leaf image classification,” Computational Intelligence and Neuroscience, 2016.

2. S. Mohanty et al., “Using deep learning for image-based plant disease detection,” Frontiers in Plant Science, vol. 7, p. 1419, 2016.

3. K. Ferentinos, “Deep learning models for plant disease detection and diagnosis,” Computers and Electronics in Agriculture, vol. 145, pp. 311–318, 2018.

4. K. Zhang et al., “Attention-based CNN for leaf disease classification,” IEEE Access, vol. 7, pp. 42817–42826, 2019.

5. A. Amara et al., “A deep learning-based approach for banana leaf diseases classification,” Datenbank-Spektrum, vol. 17, no. 3, pp. 245–254, 2017.

6. A. Fuentes et al., “A robust deep-learning-based detector for real-time tomato plant diseases and pests recognition,” Sensors, vol. 17, no. 9, pp. 2022, 2017.

7. E. Too et al., “A comparative study of fine-tuning deep learning models for plant disease identification,” Computers and Electronics in Agriculture, vol. 161, pp. 272 279, 2019.

8. Y. Zhao et al., “CNN-SVM: A hybrid image classification method for plant diseases,” IEEE Access, vol. 8, pp. 134395–134406, 2020.

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