Dense strawberry maturity recognition neural network based on multichannel attention mechanism - Cao, Zhaorui; Wang, Chuwen; Xin, Xiaojie; Chen, Xinyao; Cai, Jiahui (International Journal of Agricultural and Biological Engineering, 2026)
Color is an important indicator of strawberry maturity; therefore, identifying color changes during harvesting and delivery is important. The soft skin of strawberries can easily cause mechanical damage during harvesting and manual selection. While scholars have made significant progress in using machine vision technology for crop detection, detecting strawberries that are densely placed, differently sized, and in different growth states remains challenging. This study proposes a YOLOv8s-based dense strawberry maturity recognition convolutional neural network that integrates multiscale feature attention. The proposed model addresses several issues, including the overlapping of adjacent fruit features, fuzzy maturity criteria, and difficulty distinguishing individual fruits when they are densely placed. It utilizes a multichannel attention mechanism to fuse semantic features of strawberries at different scales, enhancing the independent summarization ability of image information of individual strawberries in dense and randomly placed environments. It also introduces wavelet down sampling convolution as the backbone of network layers to enhance the ability to capture detailed features of small strawberries. Furthermore, with the integration of the weighted intersection over union loss function, it optimizes the convergence effect and inference accuracy of network training. On a custom strawberry dataset, model accuracy, recall rate, mAP@0.5, and mAP@0.5:0.95 increased by 1.1%, 1.8%, 1.2%, and 0.8%, respectively, compared to the original YOLOv8s model, showing good recognition accuracy and stability when facing dense strawberry arrays with multiple sizes and arbitrary placement. The proposed model can quickly detect the maturity of individual strawberry fruits in random environments, improve sorting efficiency, and reduce post-harvest losses. This study provides new ideas and technical references for the application of machine vision technology in the field of dense crop maturity recognition. Keywords: object detection, feature fusion, maturity testing, deep learning DOI: 10.25165/j.ijabe.20261901.9931 Citation: Cao Z R, Wang C W, Xin X J, Chen X Y, Cai J H. Dense strawberry maturity recognition neural network based on multichannel attention mechanism. Int J Agric & Biol Eng, 2026; 19(1): 295–301.