RICE PLANT DISEASE DETECTION USING MACHINE LEARNING TECHNIQUE

Abstract

Plants and crops with insect infestations have an impact on the nation's agricultural output. Farmers and other professionals regularly keep a close eye on the plants in order to locate and identify diseases. The common drawbacks of this technique are that it is time-consuming, expensive, and imprecise. Plant diseases can be recognised by a spot on the affected plant's leaves. This research seeks to create a disease recognition model supported by leaf image classification. Using image processing and adam which is a optimzer of convolution neural network, we are detecting rice leaf detection (CNN). One of the principal crops, rice has been planted in practically all of India and many other places across the world. Plant disease diagnostics is becoming more digitalized and data-driven with the rapid growth of smart farming, providing better decision support, clever analysis, and planning. Affected plants generally have stains or lesions that are visible on their leaves, stems, blooms, or fruits. The leaves, which are often the main source for identifying plant illnesses, may show the bulk of disease signs. In this publication, we use deep learning to describe the present trends and difficulties with the diagnosis of plant leaf disease.