A Genetic Algorithm based Feature Optimization method for Citrus HLB Disease Detection using Machine Learning

Agriculture was influential since ages in the development of human civilization. It is the backbone of our economic system and relies heavily on horticulture. To increase their productivity, various diseases may affect the plants that are to be handled by the farmers within time. For better yields and gains, the diseases in plants need to be controlled and eradicated to ensure the inhibition of the spread of the disease. In view of automating the process of disease detection, many researchers are developing image processing based solutions. The images captured through cameras are utilized to detect and distinguish the disease type using the features extracted from the image of the leaf or fruit. The different set of diseases are defined by the lesion properties and this enables the system to make decisions regarding the presence or absence of disease. This paper presents an approach towards optimized feature extraction and selection stage for Citrus greening disease. The resultant feature vector will train the machine learning model to distinguish between healthy and unhealthy images. The testing of different Machine learning models is done in this article for greening disease and an optimized solution is selected for the system design.