Plant disease detection using machine learning approaches

Plant health care is the science of anticipating and diagnosing the advent of life-threatening diseases in plants. The fatality rate of plants can be reduced by diagnosing them for any signs early on. The early detection of such diseases is one possibility for lowering plant mortality rates. Machine learning (ML), a type of artificial intelligence technology that allows researchers to enhance and develop without being explicitly programmed, is used in this study to build early prediction models for plant disease diagnosis. Due to the similarities of crops throughout the early phonological phases, crop classification has proved problematic. ML can be applied to a variety of tasks recognize different types of crops at low altitude platforms with the help of drones that provide high-resolution optical imagery. The drones are employed to photograph phonological stages, and these greyscale photographs are then utilized to develop grey level co-occurrence matrices-based characteristics. In this article, the proposed plant disease detection models are developed using ML approaches such as random forest-nearest neighbours, linear regression, Naive Bayes, neural networks, and support vector machine. The performance of the generated plants disease risk evaluation model is calculated using unbiased metrics such as true positive rate, true negative rate, precision, recall, and F1-score method are all factors to consider. The results revealed that the ensemble plants disease model outperforms the other proposed and developed plant disease detection models. The proposed and developed plant disease prediction models aimed to predict disease detection in the early stages, allowing for early preventive actions and predictive maintenance.