Crop Disease Detection Using 2D CNN Based Deep Learning Architecture - Kumar, P. Dharmendra; Suhasini , A.; Anand, D. (International Journal of Intelligent Systems and Applications in Engineering, 2023)
Growth in the economy of the nation is mainly based on agricultural production. Harmful plant areas are recognized as the major reason for crop productivity. Huge time, very hard work, and monitoring the farm continuously are required for the detection of disease and classification in the previous conventional approach. In recent years, researchers and technology advancement focuses on this region making it probable to acquire a solution optimized for it. For the identification and detection of disease in agricultural products, several well-known approaches of neural networks, machine learning, and image processing are used. The crop disease detection based on preprocessing and segmentation process using filtering and neural network technique is proposed. The dataset here has been collected based on the pre-historic cultivation data and disease-affected data of the crop and live images from the field have been collected and the dataset has been created. This data has been initially processed using a pre-processing technique based on convoluted Gaussian filtering. Then the processed image has been segmented using an active contour neural network (ACNN) to formulate new loss functions which incorporate the region and information about size in the disease detection while training. By using 2D CNN the processed and segmented image has been classified for detecting the crop disease. From the results of the experiment, the proposed method is a vigorous method for crop disease detection and also segmented main diseases of plant leaves like Cercospora Leaf Spot and Bacterial Blight, Powdery Mildew and Rust.