Image-based Mangifera Indica Leaf Disease Detection using Transfer Learning for Deep Learning Methods
Dhawan, Kshitij (Vellore Institute of Technology School of Information Technology and Engineering (Índia))
Ramalingam, Srinivasa Perumal (Vellore Institute of Technology School of Information Technology and Engineering (Índia))
Ramu Krishnansh, Nadesh (Vellore Institute of Technology School of Information Technology and Engineering (Índia))

Date:	2023
Abstract:	Mangifera Indica, ordinarily known as mango, comes from a large tree. The leaf of the mango tree has human health benefits; the mango leaf extract is used for curing various diseases, including patients with cancer and diabetes. It also has an anti-oxidant and anti-microbial biological activity. Leaf disease, including fungal disease, is a severe security threat to nourishment and food paramours. Sometimes, it leads to decreased productivity and a huge loss for the farmers. Observing and determining whether a leaf is infected through the naked eye is unreliable and inconsistent. Technology advancement has helped agriculture people in several ways, and deep learning methods are a promising approach to spotting leaf diseases with the best accuracy. A mango leaf disease detection model is developed with the pre-trained model of ResNet18, which is used in transfer learning along with the Fast. ai framework. Around 2000 images were used, including images of healthy and infected leaves. The trained model achieved an accuracy of 99. 88% and performed well compared to the existing state-of-the-art methods.
Abstract:	Mangifera Indica, ordinarily known as mango, comes from a large tree. The leaf of the mango treehas human health benefits; the mango leaf extract is used for curing various diseases, including patientswith cancer and diabetes. It also has an anti-oxidant and anti-microbial biological activity. Leaf disease,including fungal disease, is a severe security threat to nourishment and food paramours. Sometimes, itleads to decreased productivity and a huge loss for the farmers. Observing and determining whether aleaf is infected through the naked eye is unreliable and inconsistent. Technology advancement has helpedagriculture people in several ways, and deep learning methods are a promising approach to spotting leafdiseases with the best accuracy. A mango leaf disease detection model is developed with the pre-trainedmodel of ResNet18, which is used in transfer learning along with the Fast. ai framework. Around 2000images were used, including images of healthy and infected leaves. The trained model achieved an accuracyof 99. 88% and performed well compared to the existing state-of-the-art methods.
Rights:	Aquest document està subjecte a una llicència d'ús Creative Commons. Es permet la reproducció total o parcial, la distribució, i la comunicació pública de l'obra, sempre que no sigui amb finalitats comercials, i sempre que es reconegui l'autoria de l'obra original. No es permet la creació d'obres derivades.
Language:	Anglès
Document:	Article ; recerca ; Versió publicada
Subject:	Mango leaf disease ; Image classification ; Resnet ; Transfer learning ; Fast.ai
Published in:	ELCVIA : Electronic Letters on Computer Vision and Image Analysis, Vol. 22 Núm. 2 (2023) , p. 27-40 (Regular Issue) , ISSN 1577-5097

Adreça original: https://elcvia.cvc.uab.cat/article/view/1660
DOI: 10.5565/rev/elcvia.1660

14 p, 1.3 MB

The record appears in these collections:
Articles > Published articles > ELCVIA
Articles > Research articles