The Solanum muricatum detection algorithm based on frequency domain multi-scale fusion

Abstract With the advancement of smart agriculture, the accuracy of fruit recognition and peduncle localization has become a key factor constraining the efficiency of automated harvesting. Precise fruit recognition determines the effective selection of operation targets by automated harvesting systems, while accurate localization and measurement of fruit peduncles directly affect the choice of cutting positions during harvesting and the overall harvesting efficiency. To address the challenges in open-field cultivation of Solanum muricatum , including dense fruit distribution, severe peduncle occlusion, and difficulty in accurately distinguishing fruit maturity, this paper proposes a fruit and peduncle detection algorithm based on frequency-domain multi-scale fusion, termed YOLO11-CCM. This algorithm introduces the CSP 2-convolution wavelet transform module to jointly model spatial- and frequency-domain features, thereby enhancing the representation of fine-grained information such as color and texture. In addition, a slice convolution downsampling module is incorporated to strengthen the extraction of local salient features while preserving feature resolution, effectively improving detection accuracy in dense scenes. Furthermore, to address the difficulty of detecting small-scale targets such as occluded pedicels, a multi-scale hybrid attention module is designed to enhance cross-scale feature interaction, thereby improving small-object detection performance. This module combines multi-scale convolution with a dual attention mechanism, effectively enhancing the model’s ability to perceive small targets. Experimental results show that YOLO11-CCM significantly outperforms the original YOLOv11s model in the detection of Solanum muricatum fruits and peduncles, with precision increased by 5.3%, recall increased by 2.8%, and mAP 50 increased by 3.4%. Furthermore, the model achieves efficient real-time inference on both PC platforms and the NVIDIA Jetson Orin Nano platform. The findings provide strong technical support for the visual perception system of smart Solanum muricatum harvesting equipment and are of great significance for promoting the automated harvesting of specialized fruits and vegetables.