Image Features Based Intelligent Apple Disease Prediction System

Mahvish Jan,Hazik Ahmad

doi:10.4018/ijaeis.2020070103

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%.

Highlights

Kashmir is famous for its specialty of apples all over India
The authors have computed the accuracy of different kernel function and it is concluded that Support vector machine (SVM) identification method with RBF kernel function gives more accurate results (Zhang et al, 2010)
As shown in the Table, out of the 106 instances leaves belongs to Apple Scab disease, belongs to Alternaria leaf blight and remaining instances belongs to healthy tree

Summary

INTRODUCTION

Kashmir is famous for its specialty of apples all over India. India contributes 65% of total production of apples in the world. Apple (Malus pumila) is attacked by a number of diseases such as apple scab and Alternaria leaf spot/blight. Apple Scab disease is caused by the fungus Venturia inaequalis It infects leaves, shoots, buds, blossoms and fruit. Alternaria leaf spot/blight is caused by a fungal pathogen Alternaria Mali The lesions of this disease in apple first appears on leaves in late spring and early summer as round, small, purplish or blackish spots. The main objective of this research work is to predict the apple disease by using the low-level image features of the leaves of an apple tree. The contribution of the work is that it achieves better prediction accuracy for apple disease by using only low-level texture or shape features of apple leaf images. The last section provides a comparison of the proposed technique with the existing similar techniques with brief conclusion

BACKGROUND

Backward pass

EXPERIMENTAL SETUP

Findings

CONCLUSION AND FUTURE SCOPE