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A method combining experimental and simulation optimization was used to calibrate parameters to enhance the accuracy of discrete element model parameters during kiwifruit stem separation. First, physical experiments were conducted to determine the intrinsic and contact parameters of kiwifruit stalk. Second, the mechanical parameters of the kiwifruit stalks were determined using three-point bending and shear tests. On this basis, simulation tests were conducted on kiwifruit stalks by combining the Hertz-Mindlin model with a bonding model, and the optimal combination of bonding parameters was confirmed using the bending strength and maximum shear force. Finally, a discrete element model of the kiwifruit was built with the determined bonding parameters and simulated, and the reliability of the model was verified through mechanical tests. The results showed that the density was 867.5 kg/m3, Poisson's ratio was 0.26, the modulus of elasticity was 3.25 × 108 Pa, the recovery coefficient between the fruit stalks and steel parts was 0.365, and the average values of the static and dynamic friction coefficients between the kiwifruit stalks and steel parts were 0.268 and 0.152, respectively. The kiwifruit stem bonding parameters were normal stiffness per unit area kn=7.201×1011 N/m3, shear stiffness per unit area kt=2.379×1011 N/m3, critical normal stress σmax=5.937×108 Pa, critical shear stress tmax = 2.354×109 Pa, and bonded disc radius Rj=0.164 mm. Compared with the results of the mechanical tests, the relative errors of the bending strength and maximum shear of the discrete element model were 2.13% and 2.84%, respectively. The results showed that the discrete element model improves the simulation of the bending and shearing processes of kiwifruit stalks and is capable of characterizing the physical properties of kiwifruit stalks. The results of this study provide a theoretical foundation for the optimal design of end effectors.

Vernonia anthelmintica (L.) Willd. is a traditional medicinal herb in Chinese medicine, extensively used by various ethnic groups due to the numerous advantages derived from its total flavonoids. These benefits encompass anti-inflammatory and antioxidant effects, and the promotion of melanin production, showcasing its significant efficacy in addressing vitiligo. To improve transdermal absorption and enhance the antioxidant effectiveness of the treatment, ethosome containing total flavonoids were prepared utilizing the ultrasound injection technique. The resulting ethosome was then carefully mixed with 0.7% Carbomer 934 gel in equal parts, yielding a gel concentration of 0.302 mg/g. This formulation produced small, consistent ethosome that exhibited high encapsulation efficiency and notable stability. In vitro analyses demonstrated sustained release characteristics of the gel and considerable therapeutic effectiveness against vitiligo resulting from hydroquinone exposure. Histological examinations performed through hematoxylin and eosin (H&E) staining of mouse skin revealed increased melanin production and increased activities of tyrosinase (TYR), cholinesterase (CHE), and mouse monoamine oxidase (MAO), while levels of superoxide dismutase (SOD) and malondialdehyde (MDA) were reduced. These findings underscore the promising effectiveness of this treatment strategy and validate the efficacy of the dosage form.

Many current satellite-derived bathymetry methods usually rely on in-situ water depth, which limits their applications. In this study, an ICESat-2 assisted dual-band model (IDBM) is proposed, which can be used to derive bathymetry independent of in-situ water depth. First, the Sentinel-2 blue and green band diffuse attenuation coefficient is derived using ICESat-2 lidar photon data from adjacent deep water according to the Jerlov ${{K}_d}$ spectral curves and the sensor spectral response function. Then, these two coefficients are applied to the IDBM model to derive the water depth. Datasets for four study areas are used to validate the feasibility of the proposed IDBM model, in addition, the original QAA (quasi-analytical algorithm) assisted dual-band model (QDBM) is also used to derive bathymetry for comparison purposes. Experiment results show that the proposed IDBM model is effective and can achieve higher or similar accuracy compared to the original QDBM model. The average mean absolute error (MAE) and root mean square error (RMSE) of the IDBM model reach 1.23 and 1.67 m, while those of the QDBM model are 1.44 and 1.91 m, respectively. The performance of the IDBM model has improved to some extent. Especially for the SI case, the MAE and RMSE of the QDBM model are 1.46 and 2.00 m, while those of the IDBM model are 0.99 and 1.55 m, which are reduced by 0.47 and 0.45 m. In conclusion, the proposed IDBM model is feasible and effective to derive bathymetry without in-situ water depth.

Accurate and fast detection of abnormal hydroponic lettuce leaves is primary technology for robotic sorting. Yellow and rotten leaves are main types of abnormal leaves in hydroponic lettuce. This study aims to demonstrate a feasibility of detecting yellow and rotten leaves of hydroponic lettuce by machine learning models, i.e. Multiple Linear Regression (MLR), K-Nearest Neighbor (KNN), and Support Vector Machine (SVM). One-way analysis of variance was applied to reduce RGB, HSV, and L*a*b* features number of hydroponic lettuce images. Image binarization, image mask, and image filling methods were employed to segment hydroponic lettuce from an image for models testing. Results showed that G, H, and a* were selected from RGB, HSV, and L*a*b* for training models. It took about 20.25 s to detect an image with 3 024 × 4 032 pixels by KNN, which was much longer than MLR (0.61 s) and SVM (1.98 s). MLR got detection accuracies of 89.48% and 99.29% for yellow and rotten leaves, respectively, while SVM reached 98.33% and 97.91%, respectively. SVM was more robust than MLR in detecting yellow and rotten leaves of hydroponic. Thus, it was possible for abnormal hydroponic lettuce leaves detection by machine learning methods.

To address challenges in manual detection of electronic component defects in facility greenhouses, this paper presents an electronic component defect detection method using the Improved YOLOv5 recognition algorithm. By introducing the Convolutional Block Attention Module into the YOLOv5 backbone network, the model’s identification and classification of defect types are enhanced, the network’s receptive field is improved, and recognition accuracy is increased. The proposed method also utilizes the $\alpha $ -CIoU loss function to expedite network regression. The effectiveness of the Improved YOLOv5 model was evaluated on a self-made electronic component dataset. Experimental results revealed an average accuracy of 91.9% and a detection time of 2.1 seconds per frame. Compared to the original YOLOv5 model, the average precision value increased by 1.5%, and the single-image detection speed improved by 0.2 seconds per frame. These improvements meet the accurate and efficient requirements for electronic component defect detection within the greenhouse equipment such as roller shutters and ventilators. This study provides valuable theoretical and technical support for defect detection in electronic components, contributing to the performance optimization of electrical equipment in facility greenhouses. The proposed method shows great potential for further development and application in real-world scenarios.

To address the issue of low positioning accuracy of mobile robots in trellis kiwifruit orchards with weak signal environments, this study investigated an outdoor integrated positioning method based on ultra-wideband (UWB), light detection and ranging (LiDAR), and odometry (ODOM). Firstly, a dynamic error correction strategy using the Kalman filter (KF) was proposed to enhance the dynamic positioning accuracy of UWB. Secondly, the particle filter algorithm (PF) was employed to fuse UWB/ODOM/LiDAR measurements, resulting in an extended Kalman filter (EKF) measurement value. Meanwhile, the odometry value served as the predicted value in the EKF. Finally, the predicted and measured values were fused through the EKF to estimate the robot’s pose. Simulation results demonstrated that the UWB/ODOM/LiDAR integrated positioning method achieved a mean lateral error of 0.076 m and a root mean square error (RMSE) of 0.098 m. Field tests revealed that compared to standalone UWB positioning, UWB-based KF positioning, and LiDAR/ODOM integrated positioning methods, the proposed approach improved the positioning accuracy by 64.8%, 13.8%, and 38.3%, respectively. Therefore, the proposed integrated positioning method exhibits promising positioning performance in trellis kiwifruit orchards with potential applicability to other orchard environments.

Flexible perovskite solar cells suffer huge efficiency loss upon area scale-up due to brittleness of ITO and poor perovskite film quality. Here Meng et al. solve this by inserting a conductive and glued polymer layer between ITO and perovskite layers and obtain efficiency of 17% for 30 cm2 devices.

The harvesting of fresh kiwifruit is a labor-intensive operation that accounts for more than 25% of annual production costs. Mechanized harvesting technologies are thus being developed to reduce labor requirements for harvesting kiwifruit. To improve the efficiency of a harvesting robot for picking kiwifruit, we designed an end-effector, which we report herein along with the results of tests to verify its operation. By using the established method of automated picking discussed in the literature and which is based on the characteristics of kiwifruit, we propose an automated method to pick kiwifruit that consists of separating the fruit from its stem on the tree. This method is experimentally verified by using it to pick clustered kiwifruit in a scaffolding canopy cultivation. In the experiment, the end-effector approaches a fruit from below and then envelops and grabs it with two bionic fingers. The fingers are then bent to separate the fruit from its stem. The grabbing, picking, and unloading processes are integrated, with automated picking and unloading performed using a connecting rod linkage following a trajectory model. The trajectory was analyzed and validated by using a simulation implemented in the software Automatic Dynamic Analysis of Mechanical Systems (ADAMS). In addition, a prototype of an end-effector was constructed, and its bionic fingers were equipped with fiber sensors to detect the best position for grabbing the kiwifruit and pressure sensors to ensure that the damage threshold was respected while picking. Tolerances for size and shape were incorporated by following a trajectory groove from grabbing and picking to unloading. The end-effector separates clustered kiwifruit and automatically grabs individual fruits. It takes on average 4–5 s to pick a single fruit, with a successful picking rate of 94.2% in an orchard test featuring 240 samples. This study shows the grabbing–picking–unloading robotic end-effector has significant potential to facilitate the harvesting of kiwifruit. Keywords: Kiwifruit, End-effector, Harvesting robot, Trajectory model, Simulation analysis

Kiwifruit harvesting with robotics can be troublesome due to the clustering feature. The gripper of the end effector will easily cause unstable fruit grasping, or the bending and separation action will interfere with the neighboring fruit because of an inappropriate grasping angle, which will further affect the success rate. Therefore, predicting the correct grasping angle for each fruit can guide the gripper to safely approach, grasp, bend and separate the fruit. To improve the grasping rate and harvesting success rate, this study proposed a grasping detection method for a kiwifruit harvesting robot based on the GG-CNN2. Based on the vertical downward growth characteristics of kiwifruit, the grasping configuration of the manipulator was defined. The clustered kiwifruit was mainly divided into single fruit, linear cluster, and other cluster, and the grasping dataset included depth images, color images, and grasping labels. The GG-CNN2 was improved based on focal loss to prevent the algorithm from generating the optimal grasping configuration in the background or at the edge of the fruit. The performance test of the grasping detection network and the verification test of robotic picking were carried out in orchards. The results showed that the number of parameters of GG-CNN2 was 66.7 k, the average image calculation speed was 58 ms, and the average grasping detection accuracy was 76.0%, which ensures the grasping detection can run in real time. The verification test results indicated that the manipulator combined with the position information provided by the target detection network YOLO v4 and the grasping angle provided by the grasping detection network GG-CNN2 could achieve a harvesting success rate of 88.7% and a fruit drop rate of 4.8%; the average picking time was 6.5 s. Compared with the method in which the target detection network only provides fruit position information, this method presented the advantages of harvesting rate and fruit drop rate when harvesting linear clusters, especially other cluster, and the picking time was slightly increased. Therefore, the grasping detection method proposed in this study is suitable for near-neighbor multi-kiwifruit picking, and it can improve the success rate of robotic harvesting.

This study presents a novel method to apply the RGB-D (Red Green Blue-Depth) sensors and fuse aligned RGB and NIR images with deep convolutional neural networks (CNN) for fruit detection. It aims to build a more accurate, faster, and more reliable fruit detection system, which is a vital element for fruit yield estimation and automated harvesting. Recent work in deep neural networks has led to the development of a state-of-the-art object detector termed Faster Region-based CNN (Faster R-CNN). A common Faster R-CNN network VGG16 was adopted through transfer learning, for the task of kiwifruit detection using imagery obtained from two modalities: RGB (red, green, blue) and Near-Infrared (NIR) images. Kinect v2 was used to take a bottom view of the kiwifruit canopy's NIR and RGB images. The NIR (1 channel) and RGB images (3 channels) were aligned and arranged side by side into a 6-channel image. The input layer of the VGG16 was modified to receive the 6-channel image. Two different fusion methods were used to extract features: Image-Fusion (fusion of the RGB and NIR images on input layer) and Feature-Fusion (fusion of feature maps of two VGG16 networks where the RGB and NIR images were input respectively). The improved networks were trained end-to-end using back-propagation and stochastic gradient descent techniques and compared to original VGG16 networks with RGB and NIR image input only. Results showed that the average precision (APs) of the original VGG16 with RGB and NIR image input only were 88.4% and 89.2% respectively, the 6-channel VGG16 using the Feature-Fusion method reached 90.5%, while that using the Image-Fusion method reached the highest AP of 90.7% and the fastest detection speed of 0.134 s/image. The results indicated that the proposed kiwifruit detection approach shows a potential for better fruit detection.

During the process of automatic picking, malfunctions caused by collisions between the picking manipulator and obstacles can damage plants and/or injure staff. To solve this problem, a flexible joint based on an elastic actuator is designed. Using a collision detection algorithm based on monitoring the current, a feedback control system with a flexible joint moment is set up, and a protection mechanism for the staff and plants against collisions with the manipulator during the automatic picking process is established. The results of the experiments on a prototype show that the proposed feedback control system with flexible joint moment can detect collisions of the manipulator with high accuracy. Based on the jump value of the current during collision detection, the impact moment on the flexible joint is estimated to have a minimum resolution of $2.444\times 10^{-{3}}\,\,\text{N}\cdot \text{m}$ . The frequency of collision detection is 37 Hz, and the minimum estimation error in the impact moment of the flexible joint is 2.25%.

Double-arm picking robots are widely used in agricultural production for their high collaborative efficiency. While picking, area planning and collision detection between the mechanical arms is a crucial challenge for the double-arm robot, which needs to establish a collision-free path for fruit picking. In this study, we developed a double-arm cooperation method for robotic picking of kiwifruit. Firstly, the problem of dividing the picking area was simplified into a multiple traveling salesmen problem (MTSP) to be solved. The picking sequence of each robotic arm was formulated by the principle of similar picking numbers, and combined with the brainstorming optimization algorithm (BSO). Secondly, a double-arm parameter model was built to solve the forward and backward movements of the robotic arms and to figure out the joint position. The spatial mathematical relationship of the bounding boxes between the robotic arms was used to detect the collision between the two robotic arms, in order to achieve the avoidance between the robotic joints. Then, simulation software was applied to the simulation and analyzed the availability of picking area planning and collision detection. The simulation results showed that the optimized picking sequence planning using BSO was more efficient; the smooth joint trajectory during the movement of the robotic arms met the limits on the range of movement and on the angular velocity of the robotic arm joints. Finally, based on the simulation result, a double-arm collaboration platform was tested. The double-arm collaboration platform harvesting trials showed that the average picking success rate was 86.67%, and collision detection time was 3.95 ± 0.83 s per fruit. These results indicated that the proposed method could plan the operation tasks of the double-arm picking robot system, and effectively implement the collision-free picking operation.

Harvesting with roots can help retain the freshness of hydroponic lettuce. And different root lengths may have different effects for freshness. To determine the optimal root length of hydroponic lettuce for retaining freshness, a root cutting and storage experiment was carried out to study some influenced factors of freshness such as changes in weight loss rate, color, and chlorophyll contents. Root lengths of samples were selected as five groups, i.e., 0 cm, 3 cm, 6 cm, 9 cm, and whole root length. Balance, chroma meter, and chlorophyll meter were used to measure the influenced indicators of different root lengths every other day for 15 days. Results showed that the relationship between the loss rate of chlorophyll content and the root length of hydroponic lettuce was not apparent. The chromatic aberration and the root length of hydroponic lettuce maintained a linear relationship. In the group of 0 cm, the chromatic aberration was 8.70 NBS that was maximum in the five groups. In the group of 9 cm, the chromatic aberration was 6.00 NBS that was minimal in the five groups. The relationship between the weight loss rate of hydroponic lettuce and the root length was linear. In the group of the whole root length, the weight loss rate of lettuces was 1.23% that was minimal in the five groups. It can be known that retaining the primary root or whole root of the hydroponic lettuce was beneficial for reducing the change in chromatic aberration, and increasing root length could reduce the weight loss rate of the lettuce. Therefore, considering the influence of the root length on the changes in weight loss rate, color, chlorophyll contents, and the hydroponic characteristics of lettuce, the optimal length of root retention for hydroponic lettuce was selected as 9 cm.

Usually the planters used in agricultural machinery face two problems: The planters have to spread the soil twice using the tray seed planter, and it is difficult for them to dig the holes before planting. This study has designed a spiral digging end-effector to dig holes in parallel effectively and quickly so that the seeds can be planted in the holes easily. A spiral digging end-effector was designed with five spiral bits, a synchronous belt, a cylinder, a gear motor, and a connecting plate based on the tray size and the pumpkin seed characteristics. Before the optimization of the end-effector’s parameters, the substrate discrete element model parameters were calibrated using the method of “material funneling” applied for the EDEM discrete element model simulation. The contact parameters and model parameters (substrate–substrate static friction coefficient, substrate–substrate rolling friction coefficient, and surface energy) that have significant impacts on the substrate AOR (angle of repose) were selected by applying the Plackett–Burman Design and the path of steepest ascent method, and their respective optimal value range were determined. The optimal parameter values were obtained through the Central Composite Design response surface analysis test, and the parameters of the spiral digging end-effector were optimized combining the substrate particle simulation. The verification test results indicate that the substrate discrete element model parameters were accurate and reliable if the substrate static friction coefficient was 0.427; the substrate rolling friction coefficient was 0.039, and the surface energy was 0.228. When the cone angle is 30°, the spiral angle is 80°, and the rotational speed is 240 r/min, the section width at the hole depth of 13 mm is 23.1 mm, and the particle overflow proportion is about 2.05%. Compared with the protrusions penetrating end-effector, soil porosity is increased, and soil aeration is improved. Therefore, the spiral digging end-effector can effectively and quickly dig holes in the seedling substrate and can sow pumpkin seeds, which provides a research basis for the design and improvement of automatic pumpkin seed sowing equipment.

The success of organic and green agricultural fruit production depends on quality and cost. As the kiwifruit industry becomes ever more commercialized, it is in the interests of the industry to mechanize production, which can promote industrialization and improve industrial value and market prospects. Currently, New Zealand, Italy, Chile, and China carry out research into the mechanism of kiwifruit production. This review describes in detail the current state of the art of pollination, harvesting and grading equipment, including detection and identification, non-destructive end effector, harvesting robots and grading devices. Process technologies that include artificial pollination, harvest mechanization, grading and standardization of production problems are analysed and compared. These problems directly affect the quality of kiwifruit products. Finally, to solve the various problems that the kiwifruit industry experiences, it is necessary to accelerate the development of mechanized kiwifruit production, realize the mechanization of information acquisition and standardization in order to advance precision agriculture and agricultural wisdom for the future. Mechanization of the kiwifruit industry must adapt to adjustments in how China’s economic structure develops. Keywords: Kiwifruit, Mechanization, Pollination, Harvesting, Grading

During the process of automated crop picking, the two hand–eye coordination operation systems, namely “eye to hand” and “eye in hand” have their respective advantages and disadvantages. It is challenging to simultaneously consider both the operational accuracy and the speed of a manipulator. In response to this problem, this study constructs a “global–local” visual servo picking system based on a prototype of a picking robot to provide a global field of vision (through binocular vision) and carry out the picking operation using the monocular visual servo. Using tomato picking as an example, experiments were conducted to obtain the accuracies of judgment and range of fruit maturity, and the scenario of fruit-bearing was simulated over an area where the operation was ongoing to examine the rate of success of the system in terms of continuous fruit picking. The results show that the global–local visual servo picking system had an average accuracy of correctly judging fruit maturity of 92.8%, average error of fruit distance measurement in the range 0.485 cm, average time for continuous fruit picking of 20.06 s, and average success rate of picking of 92.45%.

This paper attempts to design an automated, efficient and intelligent tomato grading method that facilitates the graded selling of the fruit. Based on machine vision, the color images of tomatoes with different morphologies were studied, and the color, shape and size were selected as the key features. On this basis, an automated grading classifier was created based on the surface features of tomatoes, and a grading platform was set up to verify the effect of the classifier. Specifically, the Hue value distributions of tomatoes with different maturities were investigated, and the Hue value ranges were determined for mature, semi-mature and immature tomatoes, producing the color classifier. Next, the first-order Fourier descriptor (1D- FD) was adopted to describe the radius sequence of tomato contour, and an equation was established to compute the irregularity of tomato contour, creating the shape classifier. After that, a linear regression equation was constructed to reflect the relationship between the transverse diameters of actual tomatoes and tomato images, and a classifier between large, medium and small tomatoes was produced based on the transverse diameter. Finally, a comprehensive tomato classifier was built based on the color, shape and size diameters. The experimental results show that the mean grading accuracy of the proposed method was 90.7%. This means our method can achieve automated real-time grading of tomatoes.

Hiking is a popular recreational activity and to cater to public demand, it is apt to increase the number of hiking trails. Various methodologies have been proposed to evaluate the suitability of forest trails to be constructed as hiking trails, but they can be costly and require relevant knowledge in analyzing digital information through a high-throughput dataset. Therefore, there is a need to come up with a simple method to obtain first-hand information on the trail condition, particularly considering the aspects of safety and suitability to hikers, using both on-ground and aerial observations. In this study, we introduce a new assessment approach to analyze and select old forest trails to be reconstructed as new hiking trails. This is useful for park managers who prioritize safety, comfort, and aesthetic features of the recreation site for their visitors. Trail condition assessment was carried out along the trail whereby a 2×2 m sampling plot was constructed at every 100 m. Aerial drone survey was conducted to produce an ortho-mosaic that revealed the percentage of exposed trail from above. Potential phytotourism products and scenic spots were identified and recorded for their locations along the trail to promote the aesthetic value of the recreation site. A strength distribution plot was prepared based on the trail condition, canopy coverage, and aesthetic features along the trail that were categorized using three altitude ranges (n ≤ 150 m, 150 < n < 250 m, n ≥ 250 m a.s.l.). This is to assess the trade-offs in safety, comfort, and aesthetic features along the trail. The development of this methodology offers a direct and cost-effective, yet informative approach to evaluate the quality of a potential hiking trail, thus could effectively aid in the promotion of nature-based tourism.

A consistent plantule point and a uniform distribution of cucurbit seeds are required for factory seedlings and grafting. However, precision sowing Cucurbita ficifolia seeds automatically with a positioning and orientation method is difficult due to their large, non-spherical shape. To overcome this challenge, a machine vision-based precision seeder was designed specifically for Cucurbita ficifolia seeds. To achieve consistent seed placement and orientation at a 45° angle, a method using machine vision and precise seed positioning and orientation has been proposed. The plantule point location was determined by comparing the pixel values at both ends of seed. The positioning and orientation module was developed to achieve seeding with same orientation of plantule point and located in the center of the plug tray. Finally, the positioning and orientation precision seeder for seeds were tested and evaluated. The results showed that the accuracy rate of seed plantule point identification and the success rate of the seeds orientation actuator were 97.56% and 100.00%. 95.21% of seeds achieved a deflection angle of less than 15°. The germination port of 96.06% of the seeds is distributed in a circle with the center of the hole as the center and a radius of 10 mm. The qualified rate of positioning and orientation reached 87.80%, and the sowing efficiency was about 3750 grains/h. The results showed that the seeder achieves the consistent orientation of the germination port of seeds and sows the seeds in the center of the hole. This finding provides technical support for seedling testing and large-scale factory seedlings. [ABSTRACT FROM AUTHOR]