An Efficient Method for Leaf Diseases Detection Using Deep Learning Technique

Leaf diseases identification and sorting are essential responsibilities in crops, and they pose a significant challenge in today's agriculture and food processing environments since they affect crop inputs and yields. A more accurate and timelier prognosis of agricultural leaf diseases could aid in the creation of an immediate corrective method, greatly reducing economic and production losses. Deep learning technical developments have enabled investigators to significantly improve the performance and accuracy of object identification and recognition systems. The primary objective is to establish how to come up with a more applicable neural network framework for the stated duties. In the present article, we put forward the use of a convolutional neural network (CNN) approach for detecting crop leaf diseases in an assortment of plants employing images taken from plant leaves. As a result, it is able to appropriately assess the detection and classification processes. The proposed crop leaf disease forecasts include four stages: preprocessing to enhance and shrink images using the contrast stretching approach; segmentation using k-means clustering technique; feature extraction and classification using convolutional neural networks. Following that, we detected and displayed in the conversation box several segmentation features, classifier performance metrics, and statistical and structural features of the anticipated disease type. Finally, the acquired findings were compared to existing methodologies, and it was determined that the proposed plant disease prediction method is both accurate and efficient.