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With the emergence of the new generation vision architecture Vmamba and the further demand for agricultural yield and efficiency, we propose an efficient and high-accuracy target detection network for automated pear picking tasks based on Vmamba, aiming to address the issue of low efficiency in current Transformer architectures. The proposed network, named SRSMamba, employs a Reward and Punishment Mechanism (RPM) to focus on important information while minimizing redundancy interference. It utilizes 3D Selective Scan (SS3D) to extend scanning dimensions and integrates global information across channel dimensions, thereby enhancing the model's robustness in complex agricultural environments and effectively adapting to the extraction of complex features in pear orchards and farmlands. Additionally, a Stacked Feature Pyramid Network (SFPN) is introduced to enhance semantic information during the feature fusion stage, particularly improving the detection capability for small targets. Experimental results show that SRSMamba has a low parameter count of 21.1 M, GFLOPs of 50.4, mAP of 72.0%, mAP50 reaching 94.8%, mAP75 at 68.1%, and FPS at 26.9. Compared with other state-of-the-art (SOTA) object detection methods, it achieves a good trade-off between model efficiency and detection accuracy. [ABSTRACT FROM AUTHOR]

The adulteration of camellia seed oil with different processes will seriously violate the rights and interests of consumers. The accurate identification of camellia seed oil processes is of great significance to reduce such illegal activities. However, the fatty acid composition of camellia seed oil is complex and the content varies greatly in the same process, while the difference is small in different processes. This multivariate data are easy to lead to the fuzzy characteristics of camellia seed oil, which increases the difficulty of identifying camellia seed oil quality. To solve these problems, we propose a multi-scale interactive attention network (MIANet) for the accurate identification of camellia seed oil. Firstly, a one-dimensional multi-scale convolutional feature extraction method (OMCM) was proposed, which was used to reduce the difference from multivariate fuzzy features and better solve the problem of fuzzy features of camellia seed oil fatty acids with the same process. Secondly, the interactive attention mechanism (IA) was proposed to enhance the deep characteristics of multivariate fatty acids from the fusion of two dimensions, so that the model paid more attention to the subtle differences between different processes, and effectively solved the problem of fuzzy fatty acid characteristics of camellia seed oil in different processes. Finally, in order to verify the effectiveness of MIANet, MIANet is compared with classical machine learning methods such as SVM, KNN, LR, LDA, QDA, classical deep learning method AlexNet, and the most advanced deep learning methods such as DMCNN and HCA-MFFNet. The accuracy of MIANet reached 94.10%, which was better than the eight methods. The experimental results show that MIANet is an effective method for the accurate identification of camellia seed oil data with fuzzy characteristics. [ABSTRACT FROM AUTHOR]

Accurate pear sorting plays a crucial role in ensuring the quality of pears and increasing the sales of them. In the domain of intelligent pear sorting, precise target detection of pears is imperative. However, practical implementation faces challenges in achieving adequate accuracy in pear target detection due to the limitations of computational resources in embedded devices and the occurrence of occlusion among pears. To solve this problem, we built an image acquisition system based on pear sorting equipment and created a pear dataset containing 34,598 pear images under laboratory conditions. The dataset was meticulously annotated using the LabelImg software, resulting in a total of 154,688 precise annotations for pears, pear stems, pear calyxes, and pear defects. Furthermore, we propose an Extremely Compressed Lightweight Model for Pear Object Detection (ECLPOD) based on YOLOv7's pipeline to assist in the pear sorting task. Firstly, the Hierarchical Interactive Shrinking Network (HISNet) was proposed, which contributed to efficient feature extraction with a limited amount of computation and parameters. The Bulk Feature Pyramid (BFP) module was then proposed to enhance pear contour information extraction during feature fusion. Finally, the Accuracy Compensation Strategy (ACS) was proposed to improve the detection capability of the model, especially for identification of the calyces and stalks of pears. The experimental results indicate that the ECLPOD achieves 90.1% precision (P) and 85.52% mAP50 with only 0.58 million parameters and 1.3 GFLOPs of computation in the homemade pear dataset in this paper. Compared with YOLOv7, the number of parameters and the amount of computation for the ECLPOD are compressed to 1.5% and 1.3%, respectively. Compared with other mainstream methods, the ECLPOD achieves an optimal trade-off between accuracy and complexity. This suggests that the ECLPOD is superior to these existing approaches in the field of object detection for assisting pear sorting tasks with good potential for embedded device deployment. [ABSTRACT FROM AUTHOR]