Year: 2025
Type: article
Abstract: Plant disease detection is crucial for ensuring agri-cultural productivity and food security. However, accurately identifying diseases across various crops remains a challenge, particularly in resource-constrained regions. This study addresses this issue by developing a single convolutional neural network (CNN) model capable of identifying diseases across multiple crop types. Using a comprehensive dataset containing labelled images of healthy and diseased leaves from various crops: including apples, potatoes, and bell peppers, the model was trained to recognize both the crop type and the specific disease.
Authors: Kuldeep Dilliwar -, Subham Mandal -, Nagesh Sahu -, Manish Kumar Shriwas -, Rajeshri Lanjewar -
Institution: Chhattisgarh Swami Vivekanand Technical University
Language: English
Topic: Smart Agriculture and AI
Subfield: Plant Science
Domain: Life Sciences
SDG: Zero hunger
Open Access status: hybrid
OpenAlex ID: W4410052029