Structure-Aware RGB Channel Reconstruction for YOLOv8-Based Oyster Mushroom Detection

Abstract

Oyster mushroom cultivation requires accurate object detection for automated monitoring and precision agriculture applications. This study evaluates a structure-aware three-channel input representation for YOLOv8s in which raw RGB channels are replaced by grayscale intensity, Sobel gradient magnitude, and a complementary structural channel derived from Gaussian Blur, Laplacian of Gaussian (LoG), Canny edge detection, or Gabor filtering. The dataset contains 555 RGB images and 8282 maturity-labeled mushroom instances. Controlled experiments include an RGB baseline with HSV augmentation disabled, component-wise ablations (GGG and GGradG), repeated-seed training, a chronological holdout, and a cross-architecture RT-DETR evaluation. Under the fixed random split, differences among RGB, grayscale, and structure-aware inputs were modest, and the ablations indicate that grayscale conversion accounts for most of the measured effect. Performance decreased substantially under the chronological split, and RT-DETR did not reproduce the same ordering observed with YOLOv8s. These results show that input representation can influence detector behavior, but they do not support a general claim that handcrafted structural channels consistently improve robustness across evaluation protocols or architectures.