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

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.

Keywords

• Mango Leaf Disease, Image Classification, ResNet, Transfer Learning, Fast.ai.