Fungal Disease Identification of Tomato Leafs Using Deep Learning
Abstract
Tomato is a commercially significant crop, and manual identification of fungal diseases is inefficient and prone to human error, affecting export quality. We propose an automated method for detecting tomato fungal diseases using convolutional neural networks (CNN), addressing the gap in disease identification in Pakistan. To detect disease, a pre-trained CNN model was applied to a proprietary dataset of tomato leaf imagery. This approach efficiently identifies diseases like Early blight, Late blight, Septoria leaf spot, and Leaf mold. The automated method allows farmers to control these diseases, improving crop quality and boosting the country's agriculture economy through increased tomato exports. Our method offers a faster, more reliable solution than traditional manual identification.
Copyright Notice Submission of an article implies that the work described has not been published previously (except in the form of an abstract or as part of a published lecture or academic thesis), that it is not under consideration for publication elsewhere, that its publication is approved by all authors and tacitly or explicitly by the responsible authorities where the work was carried out, and that, if accepted, will not be published elsewhere in the same form, in English or in any other language, without the written consent of the Publisher. The Editors reserve the right to edit or otherwise alter all contributions, but authors will receive proofs for approval before publication. Copyrights for articles published in IJSSA journal are retained by the authors, with first publication rights granted to the journal. The journal/publisher is not responsible for subsequent uses of the work. It is the author’s responsibility to bring an infringement action if so desired by the author.
