Image-based Mangifera Indica Leaf Disease Detection using Transfer Learning for Deep Learning Methods

• Published in: Electronic letters on computer vision and image analysis Vol. 22; no. 2; pp. 27 - 40
• Main Authors: Dhawan, Kshitij, Perumal, R. Srinivasa, Rk, Nadesh
• Format: Journal Article
• Language: English
• Published: Computer Vision Center Press 15.02.2024
• Subjects: Mango Leaf Disease, Image Classification, ResNet, Transfer Learning, Fast.ai
• ISSN: 1577-5097; 1577-5097
• DOI: 10.5565/rev/elcvia.1660

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.