CropAI: Crop disease prediction and growth tracking using AI
Kuruva Sree Sowmya, S Hupesh Naga Ketan, Mohebbanaaz
Abstract
Recognizing plant diseases is essential to maintaining a nation with a high level of agricultural development. A successful agricultural industry and the reduction of financial and other resource waste depend on the timely and productive discovery of plant diseases. Many illnesses can affect crops, causing farmers to lose more money every year. By detecting diseases in plant foliage early on, deep learning systems can greatly help farmers prevent crop failures. To identify crop illnesses, we tested the effectiveness of Convolutional Neural Network(CNN), VGG16, VGG19, and ResNet50 models using the 10,101 images of the Plant Village from the huge data records. The 98.60%, 92.99%, 97.85%, and 99.29% accuracy percentages attained for Convolutional Neural Network (CNN), ResNet50, VGG16, and VGG19, in that order. With a fidelity of 98.17%, the solution shows that the ResNet50 execute great results when compared to other models. The goal is to enable early diagnosis of plant diseases and provide prompt treatment to abet the farmers in preserving resources and minimal financial losses.