Machine learning techniques implementation for detection of grape leaf disease

Grape leaf diseases like Black Rot, Eska measles, Leaf Spot and Healthy are among the most common disease types of the grape crop. Accurate detection of grape leaf diseases in the initial stages can control the disease spread significantly and guarantee progressive development of the grape crop industry. The existing research provides several complex image processing algorithms and cannot assure high classification accuracy. Therefore, machine learning techniques are presented in this article to enhance leaf disease classification accuracy for efficiently detecting grape leaf diseases. Moreover, two classification models are introduced in which the simple Convolutional Neural Network based Classification (CNNC) Model is detailed. Then the improvised K-Nearest Neighbour (IKNN) model for precisely detecting grape leaf diseases is detailed. Moreover, pixel encoding methods are presented to obtain a histogram representation of extracted features. Training of simple CNNC and the proposed IKNN model is conducted on Plant-Village Dataset. Additionally, mathematical modeling is presented to formulate the problem in the feature extraction process. Moreover, Confined Intensity Directional Order Relation (CIDOR) operation ensures low dimensionality of histogram representation in the multiscale domain. Furthermore, Global Pixel Order Relation (GPOR) focuses on setting up a communication with long-reach pixels of an image outside of the central pixel neighborhood. Compared to the simple CNNC, the proposed IKNN model outperforms all the traditional leaf disease classification algorithms in terms of classification accuracy. However, the IKNN model provides superior results than CNNC comparatively in terms of classification accuracy.