Enhancing Precision Agriculture with YOLOv8: A Deep Learning Approach to Potato Disease Identification

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Abstract
Timely and precise identification of potato leaf diseases plays a critical role in improving crop productivity and reducing the impact of plant pathogens. Conventional detection techniques are often labor-intensive, dependent on expert anal-ysis, and may not be practical for widespread agricultural use. This paper introduces an automated detection system based on YOLOv8, a cutting-edge deep learning framework specialized in object detection, to accurately recognize multiple potato leaf diseases. The proposed model is trained on a carefully prepared dataset that includes both healthy and infected leaves, utilizing robust feature learning to distinguish between different disease types. Our experimental evaluation reveals that the YOLOv8-based method achieves superior performance in terms of accuracy and processing speed when compared to traditional approaches. This work contributes to the ongoing transformation of agriculture through smart technologies by offering an AI-powered tool that facilitates real-time crop monitoring. Future research may focus on deploying this solution on edge devices, such as smartphones or drones, to enable scalable, on-field disease diagnostics. Ultimately, this study supports the vision of sustainable agriculture by integrating intelligent systems into everyday farming operations.

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