Enhanced Detection of Brinjal Diseases using YOLOv8: A High-Accuracy Real-Time Model for Precision Agriculture

The rapid and accurate identification of diseases in brinjal (eggplant) crops is essential for ensuring high agricultural productivity and reducing losses. Traditional methods of disease detection are often time-consuming, require expert knowledge, and can be prone to human error. To address these challenges, this study proposes an automatic brinjal disease identification system utilizing the YOLOv8 (You Only Look Once version 8) algorithm, which is known for its superior performance in object detection tasks.The methodology involved training the YOLOv8 model on a comprehensive dataset comprising images of brinjal leaves affected by various diseases. The dataset was annotated to ensure accurate labelling of disease symptoms. The model’s performance was evaluated on the basis of key metrics, including accuracy, precision, recall, and F1 score. The YOLOv8 algorithm demonstrated a significant improvement over previous models, achieving an impressive accuracy of $95 \%$. Comparative analysis with earlier versions of YOLO and other state-of-the-art object detection algorithms revealed that YOLOv8 not only outperformed these models in accuracy but also offered faster processing times, making it suitable for real-time applications. The results indicate that the proposed system can effectively identify and classify brinjal diseases with a high degree of accuracy, thus enabling timely interventions. This system has the potential to be integrated into precision agriculture practices, where continuous monitoring of crops is necessary to optimize yield and minimize the use of pesticides. Future research will focus on expanding the training dataset to include a wider variety of disease conditions and environmental factors and exploring the integration of this system with other agricultural technologies to increase its practical applicability.