International Journal For Multidisciplinary Research

E-ISSN: 2582-2160   •   Impact Factor: 9.24

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Volume 7 Issue 1
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Plant Diseases Classification

Author(s) Sahana R, Radhika Relekar, Srustisingh DT, S.Firoze Ahamed, Prasad PS
Country India
Abstract This project aims to create an effective deep learning model for classifying plant diseases using image data from the PlantVillage dataset. The primary objective is to accurately detect different plant diseases and identify healthy plants, aiding in agricultural monitoring and decision-making. The dataset comprises labeled images of plants with diseases affecting crops such as tomatoes, peppers, and
potatoes.
A Convolutional Neural Network (CNN) serves as the foundation for this image classification task, implemented using the TensorFlow and Keras libraries. The workflow involves preprocessing the data,dividing it into training and testing subsets, and training the model using categorical cross-entropy as the loss function. The model achieves a high accuracy of 96% when tested on a dataset of 2,065 images. Additional evaluation metrics, including precision, recall, and F1-score, highlight the model's reliability, particularly in detecting diseases This classification system provides a practical solution for early disease detection, contributing to reduced crop losses and improved agricultural outcomes. Future work could involve leveraging transfer learning, deploying the model in real-world scenarios, and extending its application to other crops and
diseases.
Field Biology > Agriculture / Botany
Published In Volume 7, Issue 1, January-February 2025
Published On 2025-01-20
Cite This Plant Diseases Classification - Sahana R, Radhika Relekar, Srustisingh DT, S.Firoze Ahamed, Prasad PS - IJFMR Volume 7, Issue 1, January-February 2025. DOI 10.36948/ijfmr.2025.v07i01.34045
DOI https://doi.org/10.36948/ijfmr.2025.v07i01.34045
Short DOI https://doi.org/g82hj9
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