Machine Learning and Deep Learning Algorithms for Enhanced Maize Plant Disease Diagnosis and Prognosis in Agriculture

DOI

10.2991/978-94-6463-471-6_15How to use a DOI?

Keywords

Machine Learning; Deep Learning; Transfer Learner; SVM; MLP

Abstract

Maize plant diseases can have a severe impact on agricultural productivity, making detection and control challenging for farmers. Early identification of diseases is crucial for minimizing losses. This study proposes a new approach that integrates machine learning (ML) and deep learning (DL) algorithms to improve maize disease diagnosis and prognosis. The research employs traditional machine learning algorithms, such as Support Vector Machine (SVM) and Multilayer Perceptron (MLP), along with extracted features of Transfer Learning models, such as InceptionV3, VGG19, and Dense-Net201. The objective is to develop a robust system for early disease detection in maize leaves using image analysis. Optimization techniques, such as the Adam optimizer, and activation functions, such as tanh and sigmoid, are also explored. The results indicate that the Adam optimizer MLP achieves the highest accuracy (MLP(100,100) layers PCA(300) accuracy 0.95107) as well as SVM (RBF kernel) with PCA(100) accuracy (0.95585) exceptional other classification methods. This integrated approach promotes agricultural sustainability and crop yield by enabling prompt disease management.

Copyright

© 2024 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  - Prasanthi Potnuru
AU  - Panduranga Vital Terlapu
AU  - Potnuru Harika
AU  - Kavya Metturu
AU  - Jami Manasa
AU  - Pasupureddi Lakshmideepak
PY  - 2024
DA  - 2024/07/30
TI  - Machine Learning and Deep Learning Algorithms for Enhanced Maize Plant Disease Diagnosis and Prognosis in Agriculture
BT  - Proceedings of the International Conference on Computational Innovations and Emerging Trends (ICCIET- 2024)
PB  - Atlantis Press
SP  - 149
EP  - 159
SN  - 2352-538X
UR  - https://doi.org/10.2991/978-94-6463-471-6_15
DO  - 10.2991/978-94-6463-471-6_15
ID  - Potnuru2024
ER  -