Detection of Plant Diseases Using Leaf Images and Machine Learning

Abstract

Prevention and early detection of plant diseases is one of the main issues and challenges in agriculture. Farmers spend a lot of time observing and detecting diseased plants, often by looking at and analyzing plant leaves. Inadequate handling of plant disease such as late detection or the use of wrong pesticides often causes damage to crops, which causes a deterioration in the quality of food. This problem could be addressed using artificial intelligence and machine learning to detect plant diseases by processing digital images of leaves. As the leaf is the best indicator of whether the plant is healthy or not, by applying machine learning we can create predication models to detect the condition of the leaf in a shorter period of time and possibly prevent or reduce the losses. This paper describes experimenting with Detectron2 software library and Faster R-CNN neural network in order to detect the condition of the leaf. A dataset containing 6407 images was used to train the model. The original dataset has been extended by augmenting images using the RoboFlow tool. The experimentation and implementation was done using Google Colab, environment designed for cloud computing and machine learning development.