Recognition of Diseases on Horticultural Leaves Using Machine Learning Approach

In today's environment, Machine Learning has the potential to make huge strides toward making life better for everyone in the horticulture sector. Disease identification and prediction at the early stage of disease diagnosis will help the farmers get more yields and have a strong economic background. The proposed system uses a neural network algorithm to get insight into using machine learning technology for improving the nation's horticulture in areas like production, crop protection, and day-to-day field monitoring. Our work will also notify us about how various areas of machine learning may be brought together to create a unified result that will aid in the growth of the horticulture industry and, by extension, the nation's overall productivity. Our goal throughout this research was to provide a standardized method for evaluating the development and quality of produce. Because computer vision has so much promise to address issues in rural areas, this research drew on several prior publications for background material.