Detection of Fungi Diseases in Tomato Leaf Using ResNet-18 Approach

Title

Detection of Fungi Diseases in Tomato Leaf Using ResNet-18 Approach

Creator

Asha, M.S.; Yogish, H.K.; Deepa, Y.

Description

Agriculture is the largest and most vital sector of our economy, providing employment to over 70% of the Indian population. In an agricultural field identification and classification of disease in a tomato plant plays a vital role. If proper care is not taken in advance with this, it causes the serious effect on the tomato plants. The plants which are affected by the disease reflect on the quality and quantity of the production as well as the economy of the country. Tomato is one of the main vegetables in India which plays an important role in providing food for public needs and its a very vulnerable plant to insects and diseases caused by different pathogens like bacteria, virus, and fungi. This paper proposes a convolutional neural network (CNN) for automatic identification of plant disease caused by fungi. The proposed model attains the results of F1 score is 91% to differentiate tomato leaves in both healthy and unhealthy conditions. Measures like a high Area Under Curve (AUC) score on the Receiver Operating Characteristic (ROC) curve, accuracy, sensitivity, and specificity show the models remarkable performance. The practical uses of this discovery for early illness detection in agriculture make it significant. With its exceptional specificity and precision, ResNet-18 serves as a potential tool for agriculture. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

Source

Lecture Notes in Networks and Systems;Volume;1276;pp.85-96

Date

01-01-2025

Publisher

Springer Science and Business Media Deutschland GmbH

Subject

Classification; Convolutional neural network; Disease detection; Tomato leaves

Coverage

Asha M.S., CSE Department, Christ University, Bengaluru, India; Yogish H.K., ISE Department, MSRIT, Bengaluru, India; Deepa Y., CSE Department, Christ University, Bengaluru, India

Rights

Restricted Access; Hardcopy may be available in the library

Relation

ISSN: 23673370; ISBN: 978-981962696-0;

Format

online

Language

English

Type

Conference paper

Identifier

https://doi.org/10.1007/978-981-96-2697-7_6

https://www.scopus.com/pages/publications/105008663075?origin=resultslist