# YOLO-PGC: A Tomato Maturity Detection Algorithm Based on Improved YOLOv11
###### Abstract  
Accurate tomato maturity detection represents a critical challenge in precision agriculture. A YOLOv11-based algorithm named YOLO-PGC is proposed in this study for tomato maturity detection. Its three innovative components are denoted by \"PGC\", respectively representing the Polarization State Space Strategy with Dynamic Weight Allocation, the Global Horizontal-Vertical Context Module, and the Convolutional-Inductive Feature Fusion Module. The Polarization Strategy enhances robustness against occlusion through adaptive feature importance modulation, he Global Context Module integrates cross-dimensional attention mechanisms with hierarchical feature extraction, and the Convolutional-Inductive Feature Fusion Module employs multimodal integration for improved object discrimination in complex scenes. Experimental results demonstrate that YOLO-PGC achieves superior precision and mean average precision compared to state-of-the-art methods. Validation on the COCO benchmark confirms the framework's generalization capabilities, maintaining computational efficiency for real-time deployment. YOLO-PGC establishes new performance standards for agricultural object detection with potential applications in similar computer vision challenges. Overall, these components and strategies are integrated into YOLO-PGC to achieve robust object detection in complex scenarios. agricultural automation; tomato ripening detection; YOLO; complex context +
Footnote †: journal: Applied sciences  
0000-0002-0000-0000-0000-0000-0000-0000-0000-0000-0000-0000-0000-0000-0000-00000-0000-00000-00000-00000-00000-00000-00000-00000-000000-00000-000000-000000-000000-0000000000000000000000000000000000000000000000 Early-stage fruit detection methodologies have primarily employed classical computer vision paradigms, establishing foundational frameworks through three principal technical routes. Representative work includes that of [PERSON] et al. [3], who pioneered an apple yield estimation system combining multiscale image feature engineering with multivariate linear regression, demonstrating a 12.7% improvement over empirical agricultural forecasts. Concurrently, [PERSON] et al. [4] developed an HSV color space-optimized watershed algorithm for fruit quantification, attaining 93.6% counting accuracy with statistical parity to human experts (\(p<0.05\)). However, these conventional systems reveal critical deficiencies in three operational dimensions: inadequate robustness against aggregated fruit clusters and heavy occlusions (error rate > 32% under >60% occlusion density), limited sensitivity to sub-canopy targets (<15 cm detection range), and computational inefficiency (average processing latency > 2.3 s/image). These deficiencies collectively restrict their deployment in precision agriculture scenarios that require real-time field adaptability [5]. The advent of deep learning has catalyzed transformative advances in agricultural intelligence. Detection algorithms have found widespread application in crop estimation and fruit counting tasks [6; 7]. While various architectural approaches exist, including earlier YOLO iterations (YOLOv3 [8]-YOLOv8 [9]), transformer-based RT-DETR [10], and two-stage Faster R-CNN, the recently developed YOLOv11 architecture represents a significant advancement over these predecessors [11; 12]. YOLOv11 [13] outperforms earlier models through its enhanced feature extraction backbone, optimized anchor-free detection head, and innovative loss function, resulting in superior accuracy while maintaining exceptional inference speed. When applied to agricultural contexts, YOLOv11 demonstrates markedly improved precision in fruit detection under challenging conditions such as occlusion, variable lighting, and dense clustering, all of which are common scenarios that hinder previous architectures. These improvements make YOLOv11 particularly effective for real-time detection and enumeration challenges across various crops, including tomatoes, apples, grapes, and citrus fruits [11; 12].