Currently, the detection and localization of tea buds within the unstructured tea plantation environment are greatly challenged due to their small size, significant morphological and growth height variations, and dense spatial distribution. To solve this problem, this study applies an enhanced version of the YOLOv5 algorithm for tea bud detection in a wide field of view. Also, small-size tea bud localization based on 3D point cloud technology is used to facilitate the detection of tea buds and the identification of picking points for a renowned tea-picking robot. To enhance the YOLOv5 network, the Efficient Channel Attention Network (ECANet) module and Bi-directional Feature Pyramid Network (BiFPN) are incorporated. After acquiring the 3D point cloud for the region of interest in the detection results, the 3D point cloud of the tea bud is extracted using the DBSCAN clustering algorithm to determine the 3D coordinates of the tea bud picking points. Principal component analysis is then utilized to fit the minimum outer cuboid to the 3D point cloud of tea buds, thereby solving for the 3D coordinates of the picking points. To evaluate the effectiveness of the proposed algorithm, an experiment is conducted using a collected tea image test set, resulting in a detection precision of 94.4% and a recall rate of 90.38%. Additionally, a field experiment is conducted in a tea experimental field to assess localization accuracy, with mean absolute errors of 3.159 mm, 6.918 mm, and 7.185 mm observed in the x, y, and z directions, respectively. The average time consumed for detection and localization is 0.129 s, which fulfills the requirements of well-known tea plucking robots in outdoor tea gardens for quick identification and exact placement of small-sized tea shoots with a wide field of view.

Cassava is one of the world’s staple crops and an important economic and food crop for one billion people. It holds significant value in food, feed, and industrial raw materials. However, cassava production is faced with the following problems: agricultural machinery and agronomy are not integrated, work efficiency is not high, it is not suitable for large-scale planting plots, and there is a lack of large-scale equipment, automation, and intelligent cassava production equipment. This study reviews research on cassava planting agronomy and mechanized production technologies and equipment, including planting, stem–tuber separation, and harvesting, both globally and in China. The shortcomings are analyzed, and recommendations are provided for current mechanized cassava production to accelerate the development of intelligent equipment and strengthen the shortcomings. Meanwhile, we used typical and extensive Chinese planting and harvesting machines as case studies. The future trends in cassava production are forecasted, underscoring the importance of advancing global mechanized technology and equipment for cassava production to enhance its efficiency, competitiveness, and modernization.

The content of impurities in mechanically harvested sugarcane is a critical factor for evaluating harvest quality and determining market price. To enable intelligent detection of impurities in mechanically harvested sugarcane, this study proposes an impurity detection method based on an improved YOLOv11n-seg model. The method integrates four enhancement modules into the original YOLOv11n-seg architecture. Firstly, a lightweight C2_Ghost module is introduced into the high-channel feature extraction stages of both the backbone and neck, thereby reducing computational complexity and feature redundancy. Subsequently, a C2_FSAS module is designed to perform frequency-domain relationship modelling, enhancing long-range semantic dependency representation. An Efficient Channel Attention (ECA) mechanism is then applied to deep high-level semantic features to adaptively reweight salient feature channels. Finally, the traditional fixed interpolation-based upsampling operation is replaced with a dynamic DySample upsampling strategy to recover fine-grained edge features. Experimental results indicate that Improved YOLOv11n-seg achieves segmentation performance of 97.0%, 98.1%, 99.2%, and 82.9% in terms of P, R, mAP _0.5 , and mAP _0.5:0.95 , respectively. Compared with the original YOLOv11n-seg, the proposed model achieves a 1.8% improvement in mAP0.5:0.95, a 10.2% reduction in parameter count, and maintains a real-time inference speed of 34.8 FPS on the Jetson Xavier NX under TensorRT acceleration. Ablation studies validate the effectiveness of the four-module synergistic design, with C2_FSAS and DySample contributing most significantly to the improvement in mAP. Moreover, the model exhibits enhanced edge delineation accuracy and inter-class discrimination capability. In summary, the Improved YOLOv11n-seg achieves a favourable balance between segmentation accuracy and real-time performance, enabling precise segmentation of sugarcane segments and diverse impurity types. The proposed method provides reliable technical support for intelligent impurity rate detection in mechanically harvested sugarcane and practical deployment on edge computing platforms.

In view of the lack of research on the extrusion of pineapple caused by the overall stress response of pineapple at the present stage of pineapple automatic harvesting, the finite element model of pineapples can be studied by constructing such a model. At present, there is still a lack of research on the mechanical properties of the pineapple stem. In this research, the mechanical properties of a pineapple stem were determined by a three-point bending test, a compression test, and a theoretical calculation. Based on the cohesive zone model (CZM), the relevant parameters of the pineapple fruit–stem junction were determined by the fracture test. The overall finite element model of pineapple was established, and then the verification test of the finite element model was carried out. In the validation test, the correlation between sample size parameters and results was analyzed, and the validity of the test sample selection was demonstrated. By substituting the simulation results into the derivation formula, the maximum traction strength and maximum displacement error values were calculated to be 4.3% and 2.9%, respectively, which verified the accuracy of the cohesive zone model parameters. By comparing the maximum load force and load displacement of the load point in the test and simulation, the error of the load force at the fracture point relative to the average value was 3.6%, and the error of the load displacement relative to the average value was 1.4%. The numerical results showed that the model reflected the accuracy of the process of pineapple plant fracture. This study provides a reliable finite element model for future research on pineapple automatic harvesting.

China is the world’s third-largest sugarcane producer. When mechanically harvested sugarcane enters the sugar mill, impurity rate detection is required. However, due to the piling up of sugarcane, significant errors may occur in the detection results. Therefore, this research addresses the issue of low accuracy in machine vision detection due to the dense stacking of sugarcane. An innovative graded device was developed, featuring a three-stage progressive geometric constraint system with roller-belt gaps of 100 mm, 45 mm, and 33 mm, alongside differential traction with speed ratios of 3:1, 4:1, and 5:1. Utilizing the normal distribution characteristic for the diameter of 500 sugarcane stalks, the gap parameters were refined through a dynamic stiffness model. Through power validation and multi-factor orthogonal experiments, the study uncovered the interactive influences of sugarcane weight, primary conveyor belt speed, and speed ratio on the single-layer rate and area ratio. Our findings indicate that sugarcane weight is the primary determinant of the material’s single-layer rate, while the speed ratio is crucial for managing sugarcane distribution density, more so than the primary conveyor belt speed. Notably, increasing the speed ratio from 3:1 to 5:1 results in a decrease in area ratio from 26.8% to 22.0%. After utilizing the graded differential device, the average accuracy of machine vision detection achieved 94.90%, with only two misidentifications on average. In comparison to the control group, detection accuracy improved by 26.93%, misidentifications dropped by about 81.80%, and detection speed was recorded at 55.5 ms. These outcomes confirm that the device not only enhances detection accuracy but also significantly lowers the misidentification rate, thereby creating a stable, clear, and efficient detection environment.

To address the challenges of inadequate adaptability, insufficient power, high ground clearance, and limited functionality in existing pineapple field machinery, this study proposes a height-adjustable pineapple field management platform based on previously established cultivation patterns and agronomic requirements. The structural configuration and operational principles of the platform’s power chassis are elucidated, with specific emphasis on the development of the traction power system and modular operational systems. Theoretical and experimental analyses of steering parameters, stability, and field performance were conducted. Finite element simulation analysis of the frame revealed that under full-load conditions, the equivalent elastic strains during descent and ascent phases were 0.000317 and 0.00125, respectively. Maximum equivalent stresses (48.27 MPa and 231.6 MPa for descent and ascent, respectively) were localized at the beam–plate junctions, while peak deformations of 1.14 mm (descent) and 4.31 mm (ascent) occurred at mid-frame and posterior–mid regions, respectively. Field validation demonstrated operational velocities of 0.16–1.77 m/s (forward) and 0.11–0.28 m/s (reverse), with a maximum gradability of 20°. The platform exhibited multifunctional capabilities including weeding, spraying, fertilization, flower induction, harvesting, and transportation, demonstrating its potential to fulfill the operational requirements for pineapple field management. Simultaneously, the overall work efficiency is increased by more than 50%, compared to manual labor.