Agri Data Fusion Framework With Pease Identification With Disease Detection For Paddy Crop Monitoring
DOI:
https://doi.org/10.29284/fjwmqf54Keywords:
AgriDataFusion, Paddy Crop Monitoring, Pest Identification, Disease Detection, Deep Learning, Precision Agriculture.Abstract
Paddy cultivation plays a vital role in global food security, yet it is highly vulnerable to pest infestations and disease outbreaks that significantly reduce yield and quality. Traditional monitoring approaches rely on manual field inspections, which are labor-intensive, subjective, and inefficient for large-scale farms. To address these limitations, this research proposes an integrated AgriDataFusion Framework that combines pest identification and disease detection for comprehensive paddy crop monitoring. The framework leverages deep learning-based image analysis along with multi-modal data fusion to enhance detection accuracy and robustness under real-field conditions. A lightweight convolutional neural network is employed for disease classification, while an object detection model identifies pest species present in paddy fields. Environmental parameters such as temperature, humidity, and rainfall are incorporated through a data fusion mechanism to improve contextual awareness. The integrated system performs severity assessment and generates actionable recommendations for farmers, enabling early intervention and optimized resource utilization. Experimental evaluation demonstrates improved accuracy, precision, recall, and F1-score compared to standalone pest or disease detection models. The framework is computationally efficient and suitable for deployment on edge devices and IoT-enabled smart agriculture platforms. Overall, the proposed AgriDataFusion Framework offers a scalable, reliable, and real-time solution for paddy crop health monitoring, contributing to sustainable farming practices and enhanced agricultural productivity.
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This work is licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
