To enable efficient and low-cost automated apple harvesting, this study presented a multi-class instance segmentation model, SCAL (Star-CAA-LADH), which utilizes a single RGB sensor for image acquisition. The model achieves accurate segmentation of fruits, fruit-bearing branches, and main branches using only a single RGB image, providing comprehensive visual inputs for robotic harvesting. A Star-CAA module was proposed by integrating Star operation with a Context-Anchored Attention mechanism (CAA), enhancing directional sensitivity and multi-scale feature perception. The Backbone and Neck networks were equipped with hierarchically structured SCA-T/F modules to improve the fusion of high- and low-level features, resulting in more continuous masks and sharper boundaries. In the Head network, a Segment_LADH module was employed to optimize classification, bounding box regression, and mask generation, thereby improving segmentation accuracy for small and adherent targets. To enhance robustness in adverse weather conditions, a Chain-of-Thought Prompted Adaptive Enhancer (CPA) module was integrated, thereby increasing model resilience in degraded environments. Experimental results demonstrate that SCAL achieves 94.9% AP_M and 95.1% mAP_M, outperforming YOLOv11s by 6.6% and 4.6%, respectively. Under multi-weather testing conditions, the CPA-SCAL variant consistently outperforms other comparison models in accuracy. After INT8 quantization, the model size was reduced to 14.5 MB, with an inference speed of 47.2 frames per second (fps) on the NVIDIA Jetson AGX Xavier. Experiments conducted in simulated orchard environments validate the effectiveness and generalization capabilities of the SCAL model, demonstrating its suitability as an efficient and comprehensive visual solution for intelligent harvesting in complex agricultural settings.

Safflower is one of the most important cash crops in China. Its production area is concentrated in Xinjiang, Gansu and Ningxia. However, the harvesting of safflower relies mainly on manual labour at present. Particularly, the operating environment is easily affected by weather factors. Fortunately, intelligent harvesting can be expected to improve the efficiency of safflower harvesting with labour cost savings. Previous research often focused on pneumatic, pulling, combing, and cutting harvesting. However, it is still required for manual work during harvesting. The autonomous operation can be realized by combining target detection and navigation in the harvesting robots. However, the complex working environment in the field has limited the accurate recognition and localization in the harvesting process. This study aims to promote the performance of safflower recognition under the complex environment in the field during intelligent harvesting. A lightweight safflower recognition was also proposed using an improved YOLOv8n. The computational resources of the device were then deployed to the model on the mobile for detection. A dataset of 2309 images was created to categorize into two classes: picked and no picked. The safflower blooming was categorized into four stages, namely the bud, first flowering, prime bloom, and septum stage. The prime bloom stage was the best picking time in the most economically beneficial period of safflower. Therefore, the safflower only in the prime bloom stage was picked rather than the bud, first flowering, and septum stage. The improvement procedures were as follows. Firstly, the Vanillanet lightweight network structure was applied to substitute for the Backbone of YOLOv8n, in order to reduce the complex structure of the model. Secondly, the large separable kernel attention (LSKA) module was introduced into the Neck, in order to reduce the amount of storage and computational resource consumption. Thirdly, The YOLOv8n's loss function was revised from the center intersection of union (CIoU) to the wise intersection of union (WIoU), in order to improve the overall performance of the detector. Finally, the stochastic gradient descent (SGD) was chosen to train the model for robustness. The experimental results showed that the frames per second (FPS) of the improved lightweight model increased by 7.41%, while the weight file was only 50.17% of the original one. The precision (P) and the mean average precision (mAP) values reached 93.10% and 96.40%, respectively. Furthermore, the FPS was improved by 25.93% and 19.76%, the weight file was reduced by 21.90% and 25.86%, respectively, compared with the YOLOv5s and YOLOv7-tiny models. Meanwhile, better robustness was achieved in the improved model. The Jetson Orin NX flatform was then selected to deploy for testing. The single-image detection time of YOLOv8n and YOLOv8n-VLWS was 0.38s and 0.27s, which was 28.95% shorter than the original model. The high precision and lightweight of real-time detection was realized for the safflower in the field. The findings can provide the technical support to develop intelligent harvesting equipment for safflower.

To explore the influence of the lateral sloshing and the time-varying mass of the liquid in the tank on the ride comfort of the high-clearance sprayer, a spring-mass-damping equivalent mechanics that can describe the lateral sloshing of the liquid under different filling ratios was constructed based on the equivalent criterion. The Fluent was used to simulate the moment acting on the wall of the tank by the lateral sloshing of the liquid, and then the parameters of the equivalent mechanical model are obtained by fitting and solving. Comparative analysis of Fluent simulation and bench test on lateral sloshing of tank liquid under different filling ratios. The results show that the lateral sloshing trend of the tank liquid level obtained from the Fluent simulation and the bench test was consistent, which proved the accuracy of the Fluent fluid simulation process and the correctness of the required equivalent mechanical model parameters. Incorporating a liquid sloshing equivalent model, a four-degree-of-freedom vertical dynamic model of the sprayer half-car was established. Subsequently, the performance of the sprayer was systematically analyzed and compared under the excitation of a bump road and a random E-level road. This investigation took into account varying liquid filling ratios of 10%, 50%, and 90%. The focus lay on evaluating the vertical acceleration of the sprayer body, dynamic deflection of the suspension, and dynamic load on the tires in response to these road conditions. This analysis is conducted independently of the liquid sloshing factor. The results show that the lateral sloshing of the liquid medicine significantly reduces the ride smoothness of the machine, and makes the vibration response of the machine produce a certain hysteresis effect. With the reduction of the quality of the liquid medicine in the spray tank, the vibration amplitude of the sprayer body gradually decreases, the hysteresis effect is also gradually weakened. The results presented in this study offer a theoretical foundation for the analysis of ride comfort and the optimization of chassis structure in high-clearance sprayers.

Over the past 30 years, there has been continuous progress in global science and technology. However, many agricultural products still heavily rely on traditional methods of manual and mechanical harvesting, facing challenges such as high costs and low efficiency. To address these challenges, researchers have developed various harvesting robots to handle diverse tasks in complex farm environments. This study analyzed pertinent papers on harvesting robots retrieved from the Web of Science (WOS) core database and the China National Knowledge Infrastructure (CNKI) database, spanning the years 1993 to 2022. Using specialized software such as CiteSpace and VOSviewer, a bibliometric analysis was conducted to examine the research progress and hotspots in the field of harvesting robots. The analysis of 517 English papers indicated a continuous expansion in the research scale of harvesting robots. Furthermore, the research history can be divided into three distinct periods. Currently, research on harvesting robots is experiencing a rapid growth phase, with the number of related papers steadily increasing each year. In the year 2022 alone, 151 English papers were published. This growth is attributed to close collaborations among different countries/regions, institutions, and authors. China, the United States, and Japan play crucial roles in the research of harvesting robots. Notably, China has published 326 English papers, ranking first globally. Through analysis, it was also found that Chinese papers focused on harvesting robots earlier, thereby promoting the development of agricultural robots. Additionally, bibliometric analysis revealed that the research hotspots of harvesting robots mainly include system and structure design, object recognition and localization, and multi-robot coordination, among others. In the future, development trends of harvesting robots will focus on: 1) diversifying robot types, 2) expanding application scenarios,3) enhancing overall performance to reduce losses, and 4) reducing manufacturing costs. In conclusion, through a comprehensive bibliometric analysis, this study has provided valuable insights to advance the automation of harvesting.

Lycium barbarum L.(L. barbarum) has been one of the most favorite fruits in recent years. However, manual harvesting cannot fully meet large-scale production, as the labor force decreases. It is urgent to realize efficient harvesting by picking ripe fruits rather than unripe ones. This study aims to optimize the parameters during vibration harvesting of L. barbarum under various excitation modes. The L. barbarum fruits were also selected and measured in Ningxia in western China. The normal distribution of the detachment force and detachment acceleration were plotted after measurement. It was found that the detachment force of ripe fruits was similar to that of flowers. There were overlapping areas among the unripe fruits and leaves. By contrast, the detachment acceleration was ranked in the ascending order of the ripe fruits, unripe fruits, leaves, and flowers. Ripe fruits also presented the lowest detachment acceleration without overlapping with the rest, suitable for the vibration to harvest L. barbarum. As such, the vibration-picking was employed without touching the fruits. Only the excitation area above the ripe fruit area of the branch was excited by vibration equipment, according to the characteristic of infinite inflorescence. Furthermore, the vibration head was closer to the ripe fruit area during harvesting, particularly without colliding with the ripe fruits for less damage. Three excitation modes were proposed for the branch in the corresponding vibration equipment. The upper and lower points vibrated in the same direction (simultaneous vibration); the upper point was fixed, and the lower point made a reciprocating vibration (pendulum vibration); the upper and lower points vibrated in the opposite direction (reverse vibration). The kinematics model of the clamping head was established to simulate the branch and branch-stalk fruit. The transient analysis was implemented to obtain the vibration responses of the branch under different excitation modes. The results show that the excitation modes dominated the vibration response of the branch. Moreover, the vibration response of the fruit under excitation was acquired via kinematics simulation of ADAMS. The mechanism of fruit shedding was analyzed after simulation. The results indicate that the vibration response gradually increased from the vibration head to the selection point of the branch, and then to the fruit. The verification test showed that the average relative error of branch amplitude and fruit velocity were 23.78% and 14.01%, respectively. The vibration response of L. barbarum branch, stalk, and fruit was verified after the test. The parameter experiment was taken by the Box-Behnken test. The mathematical models were then established for the picking rate of ripe fruit, picking rate of unripe fruit, damage rate of ripe fruit and vibration amplitude, excitation mode, and vibration frequency. The influences of various factors were analyzed to determine the optimal combination of parameters: the vibration amplitude was 31.13 mm; the excitation mode was pendulum vibration, and the vibration frequency was 12.59 Hz. The experiments with the optimal parameters show that the picking rates of ripe and unripe fruit were 95.14% and 4.61%, respectively, and the damage rate of ripe fruit was 2.98%. A better performance was achieved in the pendulum vibration as the optimal excitation mode. Fruit damage was minimized due to the high picking efficiency, particularly for ripe fruits. The findings can also provide valuable insights into the mechanized harvesting equipment for L. barbarum fruits.