Abstract
By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Current study results on Robotics have been published. According to news reporting originating from Ya'an, People's Re public of China, by NewsRx correspondents, research stated, "Apples are suscepti ble to various types of damage during the production process. Such damage not on ly affects the appearance and edibility of the apples but also can result in the infection of healthy apples, leading to secondary economic losses." Financial supporters for this research include China Scholarship Council, Sichua n Agricultural University Double Support Project. Our news editors obtained a quote from the research from Sichuan Agricultural Un iversity, "Therefore, it is crucial to properly handle damaged apples and re-sor t them to enhance their utilization value and optimize resource use. To quickly and accurately identify apple damage and perform sorting in real time, addressin g the resource limitations of mobile devices and the difficulty of extracting de ep network image features, this study proposes a lightweight real-time apple dam age classification network, Fast Fourier ransform Channel Attention (FFTCA)-YOL Ov8n-cls. The FFTCA module focuses on the frequency domain feature information o f images in deep networks, enhancing the network's feature extraction capabiliti es. Additionally, it integrates Convolutional Block Attention Module (CBAM) and Distribution Shifting Convolution to capture channel and spatial information of images in shallow networks and accelerate network inference. Finally, FFTCA-YOLO v8n-cls is compared with typical lightweight classification networks. Experiment al results show that this network has better classification accuracy and faster inference speed. Specifically, the FFTCA-YOLOv8n-cls network is only 0.601 MB in size, achieving a classification accuracy of 96.03%, a recall of 9 6.08%, and an F1-score of 96.05%, demonstrating its fe asibility in real-time apple damage sorting."
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