Farmer-Friendly Mobile Application for Automated Leaf Disease Detection of Real-Time Augmented Data Set using Convolution Neural Networks

Abstract

In farming, crops are prone to a wide variety of diseases. The impact of sudden climatic change has adverse effects on their growth, providing incubation to harmful viruses and bacteria. Diseases to crops imply a significant negative impact on health, economy and livelihood of the human population. According to the data from the Food and Agricultural Organization (FAO), an average of 1.3 billion tonnes of food crops succumb to such diseases annually. This paper presents an approach to prevent such diseases from propagating, by early diagnosis of such abnormalities in leaves using state of the art deep learning techniques. Convolution Neural Networks based model with additional pre-processing techniques are developed to classify the leaves into affected and healthy category. Various Deep Learning architectures and hyperparameter tuning were carried out and the resulting model produces an accuracy of up to 95% with 400 actual leaf images and up to 98% with 3600 augmented datasets. The models are trained on real-life leaf images of crops, captured from an actual agricultural field. A user-intuitive IoT Web Application is developed to capture, process and display the predicted result (disease status) from the model.