Deep Learning Based Multi Crop Disease Detection System

Gul Munir; Muhammad Ahsan Ansari; Sammer Zai; Irfan Bhacho

Authors

Gul Munir Department of Computer Systems Engineering, Mehran UET, Jamshoro, Pakistan
Muhammad Ahsan Ansari Department of Computer Systems Engineering, Mehran UET, Jamshoro, Pakistan
Sammer Zai Department of Computer Systems Engineering, Mehran UET, Jamshoro, Pakistan
Irfan Bhacho Department of Computer Systems Engineering, Mehran UET, Jamshoro, Pakistan

Keywords:

Deep learning, Precision architecture, Computer Vision, Crop disease detection

Abstract

This research explores the integration of deep learning, computer vision, and edge computing to revolutionize crop disease detection. In response to the pressing need for prompt and accurate disease identification, this work leverages the capabilities of edge computing devices deployed within agricultural fields. Real-time data processing at the edge facilitates quick disease classification across various crops, enabling timely interventions. At the heart of the methodology lies a fine-tuned ResNet50 deep learning model, specifically chosen for its proficiency in handling complex visual data. Trained on a specialized dataset derived from the ImageNet database, the model exhibits promising accuracy rates in preliminary testing. Integrating edge computing into precision agriculture, this research presents a significant advancement toward sustainable agricultural practices. By empowering farmers with early detection and timely interventions, this endeavor equips agricultural communities with the knowledge and tools necessary to safeguard their crops, ensuring both food security and economic stability.

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