TSSC is one of the most important factors affecting loquat flavor, consumer satisfaction, and market competitiveness. To improve the ability to assess the TSSC of loquats, a method leveraging near-infrared spectroscopy and explainable artificial intelligence was proposed. The 900–1700 nm near-infrared spectroscopy of 156 fresh loquat samples was collected and preprocessed using seven preprocessing techniques, significant wavelength extraction utilizing six feature methods to eliminate data redundancy. Linear and nonlinear models were employed to establish the relationship between the feature spectrum and TSSC, with a focus on comparing and analyzing prediction performance. The findings reveal that the combination of 26 spectral bands selected by SPA and the PLSR model yielded the best prediction outcomes (R = 0.9031, RMSEP = 0.6171, RPD = 2.2803). The contribution of key wavelengths can be obtained by SHAP, which explains differences in model prediction accuracy and provides a reference for the application of loquat TSSC determination.

Saw rotational speed critically influences cutting force and surface quality yet is often destabilized by variable cutting resistance. The sensorless detection method for calculating rotational speed based on current ripple can prevent the contact of wood chips and dust with Hall sensors. This paper introduces a speed control system for brushed DC motors that capitalizes on the linear relationship between current ripple frequency and rotational speed. The system achieves speed regulation through indirect speed measurement and PID control. It utilizes an H-bridge circuit controlled by the EG2014S driver chip to regulate the motor direction and braking. Current ripple detection is accomplished through a 0.02 Ω sampling resistor and AMC1200SDUBR signal amplifier, followed by a wavelet transform and Savitzky–Golay filtering for refined signal extraction. Experimental results indicate that the system maintains stable speeds across the 2000–6000 RPM range, with a maximum error of 2.32% at 6000 RPM. The improved ripple detection algorithm effectively preserves critical signals while reducing noise. This enables the motor to quickly regain speed when resistance is encountered, ensuring a smooth cutting surface. Compared to traditional Hall sensor systems, this sensorless design enhances adaptability in agricultural applications.

Instance segmentation in agricultural robotics requires a balance between real-time performance and accuracy. This study proposes a lightweight pomelo image segmentation method based on the YOLOv8n-seg model integrated with the RepGhost module. A pomelo dataset consisting of 5076 samples was constructed through systematic image acquisition, annotation, and data augmentation. The RepGhost architecture was incorporated into the C2f module of the YOLOv8-seg backbone network to enhance feature reuse capabilities while reducing computational complexity. Experimental results demonstrate that the YOLOv8-seg-RepGhost model enhances efficiency without compromising accuracy: parameter count is reduced by 16.5% (from 3.41 M to 2.84 M), computational load decreases by 14.8% (from 12.8 GFLOPs to 10.9 GFLOPs), and inference time is shortened by 6.3% (to 15 ms). The model maintains excellent detection performance with bounding box mAP50 at 97.75% and mask mAP50 at 97.51%. The research achieves both high segmentation efficiency and detection accuracy, offering core support for developing visual systems in harvesting robots and providing an effective solution for deep learning-based fruit target recognition and automated harvesting applications.

In modern intelligent food processing, the unpredictable variability in pomelo orientation on high-speed conveyors poses a significant challenge to automated grading and precision peeling operations. To address this, a deep learning-based method is proposed for the real-time detection of pomelo posture. Firstly, a pomelo posture dataset was constructed to support model training and validation. Secondly, to balance the extraction of posture features from uniform fruits with the low-power constraints of edge deployment, a domain-specific architectural optimization is presented. Building on the YOLOv8n framework, the proposed model synergistically integrates specialized modules. A lightweight GhostHGNetV2 foundation is utilized to significantly reduce computational redundancy while maintaining the resolution required to detect key anatomical landmarks. To overcome spatial confusion and capture multi-scale global appearance information, a multi-path coordinate attention (MPCA) module is introduced. Furthermore, the SlimNeck architecture and VoVGSCSP module streamline multi-scale feature fusion via one-time aggregation, effectively preventing computational bottlenecks. This design optimizes the computational efficiency of the model while maintaining detection accuracy. Experimental results demonstrate that compared with the baseline YOLOv8n model, the proposed method increased the mAP50 accuracy by 3.67% while reducing parameter count and computational load by 17.5% and 23.3%, respectively. Additionally, it achieved a processing speed of 19.3 FPS on the Jetson Orin Nano 6G edge platform. This research provides a critical technical foundation for the recognition of pomelo posture, enabling subsequent orientation rectification and fostering the development of streamlined, automated pomelo processing lines.