Crop Sense: An Integrated WebApp for Crop Health Monitoring and Disease Detection
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Date
2025
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Volume Title
Publisher
NHCE
Abstract
The increasing demand for sustainable agriculture necessitates innovative solutions to enhance crop health monitoring and disease detection. This project integrates advanced technologies, including satellite imagery, loT sensors, machine learning, and cloud computing, to address the limitations of traditional farming practices. The proposed system leverages high-resolution satellite data and real-time IoT sensor inputs to monitor environmental conditions and detect early signs of crop diseases. Machine learning models, such as Convolutional Neural Networks (CNNs) and Random Forest classifiers, process these datasets to provide accurate diagnostics and actionable insights. A user-friendly web-based interface ensures that farmers can access real-time data and receive tailored recommendations for efficient resource management.
This system offers several key benefits, including improved disease detection accuracy, reduced reliance on chemical inputs, and enhanced agricultural productivity. By enabling precision farming, the system not only optimises resource utilisation but also promotes environmental sustainability. The project represents a significant step towards addressing the challenges of modern agriculture, contributing to food security and the adoption of smart farming practices globally.