Plant Leaf Disease Detection Using Resnet-50 Based on Deep Learning

DOI

10.2991/978-94-6463-433-4_12How to use a DOI?

Keywords

Plant leaf; Machine Learning; CRE; Gaussian and Wiener filters; MRDS; ROCNN; classification; accuracy; Recall; F-measure

Abstract

India’s agriculture permits the world food chain by producing various crops and boosting the country’s economy. Diseases pose a significant challenge to agricultural production. It causes crop disruption, lowers output, and makes it extremely hard for farmers to compensate for planting damage. Early disease detection and rapid action are essential to preventing productivity loss. Currently, several methods for analyzing illness characteristics and figuring out the stage of progression use Machine Learning (ML) for image processing. However, because disease features vary, it is challenging to determine the regional segments. Unbalanced traits can complicate the detection of diseases. To resolve this problem, initially, we collected the plant image dataset from Kaggle. We applied preprocessing steps, including Gaussian and Wiener filters, to normalize plant leaves. Furthermore, plant leaf features can be selected using the Canny Region Extraction (CRE) technique for non-edge and smoothing. Moreover, the Multilevel Threshold Segmentation (MRDS) method can identify pixel groups and classify the optimal values. Finally, the proposed ResNet50 Optimal Convolutional Neural Network (ROCNN) method can categories the results to obtain binary plant classification. As a result, accuracy for plant leaf diseases can be obtained using high false rates, imprecise recognition, high precision, F-measure and low recall efficiency.

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.

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TY  - CONF
AU  - S. Jayashree
AU  - V. Sumalatha
PY  - 2024
DA  - 2024/06/03
TI  - Plant Leaf Disease Detection Using Resnet-50 Based on Deep Learning
BT  - Proceedings of the International Conference on Digital Transformation in Business: Navigating the New Frontiers Beyond Boundaries (DTBNNF 2024)
PB  - Atlantis Press
SP  - 150
EP  - 166
SN  - 2352-5428
UR  - https://doi.org/10.2991/978-94-6463-433-4_12
DO  - 10.2991/978-94-6463-433-4_12
ID  - Jayashree2024
ER  -