Image features based intelligent apple disease prediction system: Machine learning based apple disease prediction system

Abstract

A pattern classifier (PC) is used to solve a variety of non-separable and complex computing problems. One of the key problems is to efficiently predict a type of disease in a typical fruit tree. The timely and accurately predicted disease in an apple tree may help a farmer to take appropriate preventive measures in advance. In this article, an apple disease diagnosis system is developed to predict the apple scab and leaf/spot blight diseases. In this article, low level and shape-based features are used for the development of an intelligent apple disease prediction system. First, the key image features like entropy, energy, inverse difference moment (IDM), mean, standard deviation (SD), perimeter, etc., are extracted from the apple leaf images. The model for the proposed system is trained by using multi-layer perceptron (MLP) pattern classifier and eleven apple leaves image features. The Gradient descent back-propagation algorithm is used for building the intelligent system to carry out the pattern classification. The proposed system is tested using some random samples and exhibits excellent diagnosis accuracy of 99.1%. The sensitivity of the proposed prediction model is 98.1% and specificity of ~99.9%.