Weed management has been confined to small spaces and obstructive branches in densely planted orchards, particularly in the hilly and mountainous regions. The conventional mowing equipment cannot fully perform the intra-row weeding or navigate the tight transitions. This study aimed to develop an intelligent, fully electric-driven robot on both inter-row and intra-row weeding operations with obstacle avoidance. Specifically, the modular hardware and control architecture was integrated for the spatial and terrain constraints of the closed-canopy orchard environments. Four core systems were developed: A dual-motor tracked chassis to enhance the terrain adaptability; an electric push-rod mechanism for the adjustable cutting height in response to undulating terrain; a torsion-spring passive avoidance for the intra-row weeding blades; and an isolated direct current to direct current (DC-DC) converter system for the stable and safe power distribution across high- and low-voltage subsystems. A fuzzy proportional–integral–derivative (PID) controller was implemented for the chassis drive system, in order to improve the accuracy of the motion control under unstructured field conditions. Furthermore, an improved version of the Sparrow Search Algorithm (SSA) was proposed to optimize the parameters of the controllers. This optimization was incorporated with the chaotic population initialization, adaptive dynamic step adjustment, and reverse learning strategies, in order to prevent the premature local optima for the convergence performance. Simulation tests demonstrated that the improved fuzzy PID controller exhibited significantly enhanced tracking performance and robustness. Compared with both standard SSA-tuned fuzzy and conventional PID controllers, the improved controller reduced the steady-state error and overshoot, when subjected to the step inputs, indicating superior response stability and dynamic adaptability. Field experiments were conducted to validate the performance of the robots under full-load operations in a closed-canopy hilly orchard. The better performance was achieved, with an average working speed of 0.781 1 m per second, and an average turning trajectory diameter of 984 mm. Reliable operation was also maintained on the slopes with the gradients up to 16.8°. The heading deviation remained within ±3° during navigation. In terms of agronomic effectiveness, the inter-row weeding rate reached an average of 91.97%. The success rate of obstacle avoidance reached 95.58%, indicating better performance in safely maneuvering around tree trunks and irregular obstacles. The consistency coefficient of the stubble height exceeded 85%, indicating the uniform cutting height. The cutting width utilization rate surpassed 90% for the high efficiency. All evaluated metrics fully met the requirements of the original design, indicating technical feasibility and functional robustness. The high maneuverability, terrain adaptability, and precision weeding were realized in the hilly, spatially constrained orchard environments. An optimized fuzzy PID controller and the metaheuristic tuning algorithm were integrated to enhance control performance and autonomous decision-making. This finding can offer valuable theoretical and technical support for the future development of electric-driven weeding robots targeting closed-canopy orchards. A great contribution can also be gained to advance intelligent orchard machinery in sustainable agriculture

Robotic harvesting had been constrained by the low positioning accuracy of strawberry stem picking points and the significant challenge of identifying occluded strawberries. In this study, we proposed an improved YOLOv8 model combined with Pose key-point detection for enhanced strawberry recognition and localization. The accuracy of picking point localization was also improved, especially for occluded strawberries in complex environments. To optimize the YOLOv8 model, we introduced the Bidirectional Feature Pyramid Network (BiFPN) and the Generalized Attention Module (GAM), which enhanced bidirectional information flow, dynamically allocated feature weights, and focused on extracting features of small targets and enhancing the features of occluded regions. As a result, the model's ability to accurately detect and localize strawberries in complex environments was significantly improved.Experimental results showed that the improved YOLOv8-pose model outperformed the original model in several metrics: the Precision (P) increased by 6.01 percentage points, Recall (R) by 1.98 percentage points, mean Average Precision (mAP) by 6.67 percentage points, and mean Average Precision for key points (mAP_kp) by 7.85 percentage points. The positioning accuracy for strawberry stem picking points, based on key-point detection, achieved errors of just 1.4 mm in both the x and y directions and 2.2 mm in the z direction. Additionally, the occlusion level was classified according to the overlap area of occluded strawberries, and the model's performance under varying occlusion conditions was assessed. Under these conditions, the mAP_kp of the improved YOLOv8-pose model increased by 9.78 percentage points compared to the original model. Field trials further validated the model's effectiveness, with the strawberry-picking robot achieving a 95% success rate, picking each strawberry within 10 seconds. The high success rate and short picking time demonstrated the practicality of the model in real-world agricultural settings, indicating its high efficiency and accuracy. The improved YOLOv8 model with key-point detection accurately and robustly recognized strawberries, leveraged multi-scale features with the BiFPN architecture, and focused attention on relevant regions with the GAM, especially for occluded strawberries. These advancements significantly improved overall performance in precision, recall, and average precision, particularly under occlusion scenarios.In conclusion, these advanced techniques were integrated into a more capable strawberry-picking robot system. The enhanced accuracy and efficiency achieved in recognizing and localizing strawberries, even in challenging occlusion scenarios, highlighted the system's potential for practical agricultural applications. The findings contributed significantly to automated strawberry harvesting in agricultural robotics, paving the way for more efficient and cost-effective farming solutions in sustainable production.

Facility operation is often required to alleviate the soil-borne diseases that are caused by the long-term heavy stubble in greenhouses. Particularly, the planting pattern is mostly highly intensive in Chinese greenhouses at present. The long-term heavy stubble has led to a low nutrient balance in the soil. The pathogenic bacteria and insect eggs can continue to multiply and expand, thus infecting the roots and stems during crop reduction. Therefore, it is very urgent to consider soil flame disinfection. It is expected to integrate the disinfection, fine deep tillage, and ridge forming, in order to avoid the repeated operation of machinery. In this study, an integrated operation of "straight blade crushing - scraper throwing - flame disinfection – ridge forming" was adopted in the facility greenhouse. The optimal combination of operation parameters was also clarified to improve the quality of operation. A soil disinfection and ridge-forming machine was designed using a fine rotary flame. The main components included rotary tillage and soil crushing, ridge forming, flame disinfection, and electronic lighter devices. In the rotary tillage and soil crushing device, the blade roller of soil crushing was mainly composed of straight blades and scrapers arranged on the blade shaft, according to the spiral line. The major component was utilized to facilitate into the soil. The soil fragmentation rate and the efficiency of rotary tillage were also compared with the conventional rotary tillage blade roller using simulation. The results showed that the better performance of soil crushing blade roller was achieved during soil crushing and throwing, compared with the conventional one. There was an increase in the total number of soil fracture bonds caused by the soil-crushing blade roller. The better performance of flame disinfection was also obtained to optimize the parameters of the machine. Meanwhile, the heat demand for the soil disinfection was 958.23 kJ per second, and the heat supply of the machine was 978.72 kJ per second, which was greater than the heat demand. The soil was heated from 0-10 cm to 71.8 °C and from 11-14 cm to 51 °C, corresponding to the lethal temperature requirements of pathogenic microorganisms and root-knot nematodes, respectively. Single- and multi-factor field tests were carried out to verify the performance of the machine. The evaluation indicators were taken as the qualified rate of ridge type, as well as the insecticidal and sterilization rate. The influencing factors were the blade shaft speed, machine walking speed, and tillage depth. The best combination of operating parameters was obtained after optimization. The field tests showed that the primary and secondary factors on the machine's performance were ranked in descending order of the machine's walking speed, blade shaft speed, and tillage depth. The qualified rate of ridge type was 95.2%, while the insecticidal and sterilization rate was 82.9% when the blade shaft speed was 270 r/min, the machine walking speed was 0.63 m/s, and the tillage depth was 22 cm. Among them, the qualified rate of ridge type fully met the ridge standard, while the insecticidal and sterilization rate was close to the national standard. The operating cost and CO₂ emission of the machine were compared with the dazomet mechanical soil disinfection. The operating cost was RMB 4 543 yuan per hectare, thus saving RMB 7 087 yuan per hectare, compared with the dazomet mechanical soil disinfection. The CO₂ emission was 1 341 kg per hectare, indicating the promising prospect of application. The finding can also provide guidance for the practical application of the facility greenhouse, particularly for the soil disinfection and ridge-forming machine using a fine spiral flame.