Fruit detection and yield estimation in Camellia oleifera based on improved YOLOv8 and ByteTrack algorithm

Camellia oleifera , an essential woody oil tree species, produces camellia oil, which is highly nutritious and edible after seed pressing. The demand for camellia oil has increased in recent years, so accurate estimates of camellia oil production are key to fruit harvesting and price setting. The rapid development of computer vision and deep learning techniques has found wide applications in agriculture, demonstrating significant fruit detection capabilities. This study proposes a surface fruit detection algorithm in C.oleifera based on the improved YOLOv8 model, with lightweight RepViT as the backbone of the model, a small object detection layer with p2 layer added on the multi-scale basis of FPN and PAN, and a network structure optimized by Shape IoU loss function. Meanwhile, the improved ByteTrack algorithm based on ByteTrack is utilized for multi-scale small-target tracking. Using LightGBM and linear regression to establish the relationship between the number of surface fruits and the number of individual fruits, combined with the number of typical sample trees and the average single fruit quality in the dataset, a production estimation model for the automatic detection of C.oleifera is constructed. The mAP value of the improved YOLOv8 algorithm on the C.oleifera fruit dataset reached 86.21%, an improvement of 4.51% compared to the original model. Combined with average single-fruit mass and the number of representative sample trees, these counts yielded whole-tree production estimates.The approach produced a fruit-number prediction R ²of 0.945 and a yield- estimation R ²of 0.902 across our sample trees, demonstrating strong linear relationships and validating the pipeline for automated C. oleifera yield forecasting.