Year: 2025
Type: article
Abstract: Crop diseases significantly impact agricultural productivity and food security.Traditional disease detection methods rely on manual inspection, which is time-consuming and prone to errors.This research presents a machine learning-based approach for early crop disease detection using image classification techniques.Convolutional Neural Networks (CNNs) are utilized to classify various plant diseases based on leaf images.The proposed system integrates Flask for web deployment, allowing users to upload plant images and receive realtime disease predictions.The results demonstrate high accuracy in detecting multiple plant diseases, providing an efficient solution for farmers.Future work includes improving model generalization, expanding the dataset for broader crop coverage, integrating IoT devices for real-time monitoring
Language: English
Topic: Smart Agriculture and AI
Subfield: Plant Science
Domain: Life Sciences
SDG: Zero hunger
Open Access status: hybrid
OpenAlex ID: W4409512523