Multi Disease Identification in Tomato Plant using CNN and SVM
subject: Convolutional Neural Network (CNN); Machine Learning; Support Vector Machine (SVM); Tomato Plant Leaf Disease
description: Tomato is a major trade crop; it is among the most widely consumed crops in daily life. Crop diseases reduce not only the quality of the crops but also their amount of production, thus, detection and identification of the specific diseases is of great importance. Diseases like the Mosaic virus, Bacterial Spot, and Yellow Leaf Curl Virus infect the tomato plant. The advanced detection and classification techniques are mainly employed in the diagnosis of these diseases. This helps in informing the farmers about the types of diseases that attack their crops. In this study, independent CNN and SVM classifiers built to classify the diseases. The CNN model extracts feature such as color and leaf edges from input images- then, it proceeds to classification. For SVM, PCA is applied for feature reduction in order to enhance performance and accuracy before classification. A dataset sourced from plant village has been utilized to train the network CNN and SVM. The proposed neural network model has been applied to categorize 4 types of tomato leaf conditions: one healthy and three diseased types of tomato leaves. The results show that the SVM approach achieves a classification accuracy of 94.33%, whereas the CNN model has slightly higher accuracy of 95.17%. 2025 IEEE.
creator: Nandan, Shiva; Srivastava, Shilpa; Mehndiratta, Vandana; Agarwal, Ritu; Singh, Meenu
source: 2025 5th International Conference on Advancement in Electronics and Communication Engineering, AECE 2025;pp.43-49
publisher: Institute of Electrical and Electronics Engineers Inc.
date: 01-01-2025
rights: Restricted Access; Hardcopy may be available in the library
relation: ISBN: 979-833155296-1;
language: English
type: Conference paper
coverage: Nandan S., Christ University, Department of Computer Science, Bengaluru, India; Srivastava S., Christ University, Department of Computer Science, Bengaluru, India; Mehndiratta V., Christ University, Department of Computer Science, Bengaluru, India; Agarwal R., Raj Kumar Goel Institute of Technology, Ghaziabad, India; Singh M., Vsb - Technical University of Ostrava, Ostrava, Czech Republic