Deep Learning Precision Farming: Grapes and Mango Leaf Disease Detection by Transfer Learning
Uchilla S. Vishal Rao, R. Swathi, V Sanjana, L. M. Arpitha, K. P. A. Chandrasekhar, Chinmayi, Pramod Kumar Naik
Abstract
In India, half the population depends on agriculture for a livelihood. Microbial diseases are a significant threat to food security, but their rapid identification remains difficult due to limited infrastructure. With AI, automatic detection of plant diseases from raw images is possible using deep learning and transfer learning. This paper aims to detect and classify Grapes and Mango leaf diseases, employing a dataset of 8,438 images of diseased and healthy leaves collected from the Plant Village dataset and acquired locally. The deep convolutional neural network (CNN) is trained to identify diseases or their absence. A pre-trained CNN architecture called AlexNet is modeled for automatic feature extraction and classification. The system is developed with MATLAB achieves an accuracy rate of the detection of 99% and 89% for Grape leaves and Mango leaves respectively. An app named "JIT CROPFIX" is developed to implement the same on an Android Smartphone.