Automated Crop Disease Detection And Classification

Information Science and Engineering

This project proposes an innovative approach to plant disease surveillance and control by integrating artificial intelligence (AI) with precision agriculture techniques. Leveraging advanced data collection methods such as satellite imagery, drones, and IoT sensors, coupled with AI algorithms, the system aims to continuously monitor crop health and detect early signs of diseases, pests, and other stressors. By analyzing visual data and environmental parameters, AI models can accurately identify and classify potential threats to crops, enabling farmers to take timely actions to mitigate risks and optimize yields. The project also emphasizes precision treatment strategies, where AI-guided decision support systems recommend targeted interventions tailored to specific crop and field conditions, minimizing chemical usage and environmental impact. Additionally, predictive analytics capabilities enable proactive disease management by forecasting outbreaks based on historical data and current trends. Through the integration of AI-driven surveillance and control systems with farm management software, this project aims to empower farmers with actionable insights for sustainable and efficient crop production. Ultimately, this innovative approach holds the potential to enhance agricultural productivity, reduce resource consumption, and contribute to global food security in a changing climate.

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