Classification of Leaf Disease using Image Processing and Machine Learning

A successful implementation of education, health and agriculture programs are the main concerns of developing countries like Ethiopia for sustainable development of the countries and ease life of their citizens. Farmers and agriculture experts visually carry out examination of crops. However, this evaluation process is tedious, time consuming, and less accurate, which can cause high risk of loss later. Image processing and machine learning have been widely used in various disease diagnosis approaches. It has been applied to both images captured from cameras of visible light and from equipment that captures information in invisible wavelength, assisting experts to select the right measure and treatment. In this research article, a digital camera captured image is used as input and enhanced with various preprocessing techniques followed by color-based segmentation method to separate the regions of interest then features are extracted using Gray Level Co-occurrence Matrix. Classification of the input image is performed at the final stage taking four different supervised learning algorithms to classify in to two different classes called ‘healthy’ and ‘infected’.