Advancing Strawberry Disease Detection in Agriculture: A Transfer Learning Approach with YOLOv5 Algorithm

Abstract

Strawberry Disease Detection in the Agricultural Sector is of paramount importance, as it directly impacts crop yield and quality. A multitude of methods have been explored in the literature to address this challenge, but deep learning techniques have consistently demonstrated superior accuracy in disease detection. Nevertheless, the current research challenge in deep learning-based strawberry disease detection remains the demand for consistently high accuracy rates. In this study, we propose a deep learning model based on the Yolov5 architecture to address the aforementioned research challenge effectively. Our approach involves the generation of a custom dataset tailored to strawberry disease detection and the execution of comprehensive training, validation, and testing processes to fine-tune the model. Experimental results and performance evaluations were conducted to validate our proposed method, demonstrating its ability to achieve accurate results consistently. This research contributes to the ongoing efforts to enhance strawberry disease detection methods within the agricultural sector, ultimately aiding in the early identification and mitigation of diseases to preserve crop yield and quality.