Welcome to the InfoSci Platform
AI-Based Plant Disease Detection and Classification Using Pretrained Models

AI-Based Plant Disease Detection and Classification Using Pretrained Models

Honey Mehta, Rajeev Kumar Gupta, Arti Jain, Santosh Kumar Bharti, Nilesh Kunhare
Copyright: © 2023 |Pages: 14
ISBN13: 9781668485163|ISBN10: 1668485168|ISBN13 Softcover: 9781668485170|EISBN13: 9781668485187
DOI: 10.4018/978-1-6684-8516-3.ch012
Cite Chapter Cite Chapter

MLA

Mehta, Honey, et al. "AI-Based Plant Disease Detection and Classification Using Pretrained Models." Artificial Intelligence Tools and Technologies for Smart Farming and Agriculture Practices, edited by Rajeev Kumar Gupta, et al., IGI Global, 2023, pp. 219-232. https://doi.org/10.4018/978-1-6684-8516-3.ch012

APA

Mehta, H., Gupta, R. K., Jain, A., Bharti, S. K., & Kunhare, N. (2023). AI-Based Plant Disease Detection and Classification Using Pretrained Models. In R. Gupta, A. Jain, J. Wang, S. Bharti, & S. Patel (Eds.), Artificial Intelligence Tools and Technologies for Smart Farming and Agriculture Practices (pp. 219-232). IGI Global. https://doi.org/10.4018/978-1-6684-8516-3.ch012

Chicago

Mehta, Honey, et al. "AI-Based Plant Disease Detection and Classification Using Pretrained Models." In Artificial Intelligence Tools and Technologies for Smart Farming and Agriculture Practices, edited by Rajeev Kumar Gupta, et al., 219-232. Hershey, PA: IGI Global, 2023. https://doi.org/10.4018/978-1-6684-8516-3.ch012

Export Reference

Mendeley
Favorite

Abstract

Plants growth is crucial to an agricultural industry, and to the economy of a nation. Consequently, taking care of plants is essential. Like humans, plants are susceptible to several bacterial, fungal, and viral diseases. To avoid the plants from being destroyed, prompt disease detection and treatment are crucial. The goal of this chapter is to introduce plant disease detection and classification using deep learning and transfer learning based pre-trained models. Using images of leaves, the models can identify several plant ailments. The models, namely- Convolutional Neural Network (CNN), MobileNet, and VGG16 are applied for plant disease identification. To train them, the PlantVillage dataset is used, and to enhance the sample size, the dataset is augmented. For experimentation purposes, images of both healthy and damaged plants are taken. Experiment results reveal that VGG16 has outperformed CNN and MobileNet models for the detection of tomato, potato, and apple plant diseases. The accuracy of the VGG16 model is 0.89, 0.92, and 0.95 for the tomato, potato, and apple plant diseases respectively.

Request Access

You do not own this content. Please login to recommend this title to your institution's librarian or purchase it from the IGI Global bookstore.