In response to the growing need for efficient, stable, and non-destructive gripping in apple harvesting robots, this study proposes a novel rigid–flexible coupled end-effector. The design integrates an underactuated mechanism with a real-time force feedback control system. First, compression tests on ‘Red Fuji’ apples determined the minimum damage threshold to be 24.33 N. A genetic algorithm (GA) was employed to optimize the geometric parameters of the finger mechanism for uniform force distribution. Subsequently, a rigid–flexible coupled multibody dynamics model was established to simulate the grasping of small (70 mm), medium (80 mm), and large (90 mm) apples. Additionally, a harvesting experimental platform was constructed to verify the performance. Results demonstrated that by limiting the contact force of the distal phalange region silicone (DPRS) to 24 N via active feedback, the peak contact forces on the proximal phalange region silicone (PPRS) and middle phalange region silicone (MPRS) were effectively maintained below the damage threshold across all three sizes. The maximum equivalent stress remained significantly below the fruit’s yield limit, ensuring no mechanical damage occurred, with an average enveloping time of approximately 1.30 s. The experimental data showed strong agreement with the simulation, with a mean absolute percentage error (MAPE) of 5.98% for contact force and 5.40% for enveloping time. These results confirm that the proposed end-effector successfully achieves high adaptability and reliability in non-destructive harvesting, offering a valuable reference for agricultural robotics.

To address the issues of high stalk breakage rate and the mismatch between extraction force and operational speed in current horizontal shaft counter-rolling cotton stalk pullers, this study presents a novel clamping mechanism. The mechanism enables precise adjustment of the rollers’ rotational speed, inter-roller gap, and surface topography. The objective is to systematically investigate the effects of these key parameters on the peak extraction force and its timing during the stalk pulling process. Initially, pre-compressed cotton stalks were employed as test specimens. Their tensile properties post-compression were investigated by simulating the extraction forces using a universal testing machine. Subsequently, the structural design of the critical components for the test rig was created based on these experimental findings. Theoretical analysis identified the surface texture of the clamping rollers, their rotational speed, and the clamping gap as the primary experimental factors. The effects of these factors on the peak extraction force and its timing were analyzed using Response Surface Methodology (RSM). The results indicated that the optimal combination—striped surface texture for both rollers, a speed of 220 rpm, and a zero gap—yielded a time to peak force of 0.05 s and a peak force of 710.77 N, which is significantly below the measured tensile strength limit of 994.60 N for compressed stalks. This indicates that the designed clamping device for the horizontal shaft counter-rolling cotton stalk extraction machine achieves faster extraction speed while ensuring stalk integrity, and the research results can provide theoretical foundation and design guidance for the development of horizontal shaft counter-rolling cotton stalk extraction machinery.

To address the issues of low efficiency and repeated soil compaction caused by segregated pre-sowing operations for residual film recovery and soil preparation in Xinjiang’s long-term film-mulched cotton fields, this study developed a sowing-layer residual film recovery machine integrated with soil preparation functionality. The modular machine sequentially performs harrowing, film-pickup, removal, collection, soil crushing, and leveling operations. An orthogonal experiment focusing on film-pickup rate and film-removal rate was conducted using forward speed, roller speed, and working depth as experimental factors to evaluate the residual film recovery performance. Simultaneously, the effectiveness of the soil preparation operation was quantitatively validated. The results indicated that the order of factor influence significance on the film-pickup rate was forward speed > working depth > rotational speed of the film-removal roller, while the film-removal rate was primarily affected by the rotational speed of the film-removal roller. The optimal parameter combination was identified as a forward speed of 4 km/h, a film-removal roller speed at 300 r/min, and a working depth of 120 mm. Validation tests under these conditions yielded a pickup rate of 71.23% and a removal rate of 95.06%. Regarding soil preparation, the surface evenness was maintained at 1.23 cm after operation, demonstrating significant performance improvement over previous machine prototypes. This study promises to deliver crucial advancements for combined pre-sowing operations, offering support for future agricultural machinery innovation.

Under the dwarf-rootstock dense planting method, existing apple detection models for intelligent harvesting suffer from excessive parameter counts that hinder deployment on resource-constrained devices, while lightweight alternatives often sacrifice detection accuracy. To address this dilemma, this paper proposes YOLO-SPM, a lightweight apple detection model based on the YOLOv12n architecture, specifically designed for complex orchard environments. The core innovation lies in a problem-driven, three-stage collaborative optimization strategy: first, PConv is introduced to replace standard convolutions in the A2C2f module, reducing computational redundancy by exploiting channel-wise feature similarity of apple targets; second, the parameter-free SimAM attention mechanism is embedded in the neck network to enhance the model’s focus on occluded fruit features without increasing model size, while MBConv is integrated into the detection head to further reduce computational cost; third, WIoU v3 is adopted as the loss function to compensate for the accuracy loss incurred by lightweight design through its dynamic focusing mechanism on difficult samples. This complementary design ensures that each module addresses a distinct bottleneck of the native YOLOv12n in orchard scenarios, achieving a balance between efficiency and accuracy rather than simple module stacking. Experimental results demonstrate that YOLO-SPM achieves a precision of 92.8% and mAP@0.5 of 93.1%, outperforming the baseline by 4.8 and 5.3 percentage points, respectively, while reducing parameter count, FLOPs, and memory footprint by 40.2%, 35.4%, and 41.8%. This study provides a feasible solution for high-precision apple identification in dwarf-rootstock dense planting orchard environments, with the potential for integration into automated harvesting systems upon future on-device validation.

Reliable perception of young apple fruits in natural orchards is a prerequisite for automated thinning and intelligent orchard management, yet remains difficult in real field conditions due to small fruit size, dense distribution, branch–leaf occlusion, background similarity, and severe illumination degradation at night. This study presents FruitDet, a lightweight multi-module detector designed for robust day–night young apple fruit detection in complex orchard environments. A field dataset was established in a high-density apple orchard in Aksu, Xinjiang, covering daylight and low-light night-time scenes with diverse occlusion, scale, and illumination variations. To improve detection robustness without sacrificing computational efficiency, FruitDet combines three complementary mechanisms: an inverted-bottleneck-based multi-scale feature enhancement module for preserving small-fruit details, a channel–spatial attention module for suppressing foliage and illumination interference, and a lightweight Transformer-based context module for modeling long-range dependencies between fruits and surrounding orchard structures. In daytime scenes, FruitDet achieved 91.904% precision, 77.557% recall, 83.254% mAP50, and 66.427% mAP50–95; in night-time scenes, it maintained 90.107% precision, 75.135% recall, 80.544% mAP50, and 64.719% mAP50–95. Compared with mainstream detectors including YOLOv5n, YOLOv8n, YOLO11n, YOLO26n, Faster R-CNN, RT-DETR, and RT-DETRv2, FruitDet consistently delivered higher accuracy across lighting conditions. Ablation, visualization, public-dataset testing, and edge-deployment experiments verified that the proposed modules jointly improve small-object representation, background discrimination, low-light robustness, and real-time applicability. With 2.960 M parameters, 3.726 G FLOPs, and approximately 180 FPS, FruitDet offers a practical and efficient visual perception approach for Young fruit monitoring was conducted under both daytime and night-time orchard conditions covered in this study. All-weather orchard monitoring and robotic young-fruit thinning. The shareable data are available