AgriScanNet-18: A Robust Multilayer CNN for Identification of Potato Plant Diseases

This paper presents a novel approach for the early detection of potato plant diseases using deep learning techniques. The proposed method, AgriScanNet-18, is a multilayer convolutional neural network (CNN) that uses image-based analysis to identify various plant diseases. By training and evaluating the model on a potato leaf disease dataset, we achieved high accuracy of 99.30% for training and 99.28% for testing. Additionally, we developed a web app that facilitates the diagnosis of potato plant diseases by easily uploading images of leaves. In comparison with state-of-the-art models such as, VGG16, ResNet50, and VGG19, AgriScanNet-18 demonstrated improved identification accuracy of 8.66%, 3.61%, and 7.45%. In addition, Potato plant diseases can be managed and controlled using this technology to increase crop production and profitability.