Use of White Shark Optimization for Improving the Performance of Convolution Neural Network in Classification of Infected Citrus

Abstract

Citrus plant diseases are major causes of reduction in the production of citrus fruits and their usage. Early detection of the
onset of the diseases is very important to curb and reduce its spread. A number of researches have been done on the detection
and classification of the diseases, most of which have identified poor labelling of symptoms which result into improper
classification. Some researchers have also experimented on the effectiveness of convolution neural network and other deep
learning techniques, most of which results into a faster convergence but suffers from low accuracy, computational overhead
and overfitting as fundamental issues. To reduce the effects of overfitting, this research developed a White Shark
Optimization-Convolution Neural Network (WSO-CNN) technique to address the aforementioned problem by introducing a
regularization strategy via feature selection which selects more useful and distinguishing features for classification. As a
result, the developed technique was able to detect and classify various types of citrus fruit diseases and label them
accordingly with low false positive rate, high sensitivity, specificity, increased accuracy and reduced recognition time, based
on all the experiments performed with the dataset used in the research. Hence WSOCNN performed better than CNN in
classifying citrus plant disease having a reduced FPR of 3.57%, 8.34%, 3.89% and 9.00% for black spot, greasy spot, canker
and healthy/non healthy dataset respectively.