Integrating YOLOv8-agri and DeepSORT for Advanced Motion Detection in Agriculture and Fisheries

Abstract

This paper integrates the YOLOv8-agri models with the DeepSORT algorithm to advance object detection and tracking in the agricultural and fisheries sectors. We address the current limitations in object classification by adapting YOLOv8 to the unique demands of these environments, where misclassification can hinder operational efficiency. Through the strategic use of transfer learning on specialized datasets, our study refines the YOLOv8-agri models for precise recognition and categorization of diverse biological entities. Coupling these models with DeepSORT significantly enhances motion tracking, leading to more accurate and reliable monitoring systems. The research outcomes identify the YOLOv8l-agri model as the optimal solution for balancing detection accuracy with training time, making it highly suitable for precision agriculture and fisheries applications. We have publicly made our experimental datasets and trained models publicly available to foster reproducibility and further research. This initiative marks a step forward in applying sophisticated computer vision techniques to real-world agricultural and fisheries management.

Intelligent pesticide recommendation system for cocoa plant using computer vision and deep learning techniques

“…This helps in developing an accurate and automatic method for the classification of plant diseases. To overcome the limitation of Cocoa-care [22], the YOLOv8 model [26] is trained on Roboflow platform to distinguish between cocoa and non-cocoa images. This ensures that the model learns from a focused set of cocoa-specific examples, optimizing its ability to accurately detect and classify cocoa plants.…”