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The seeds cannot fully sprout owing to the seed quality, sowing precision and environmental differences. In order to get the accurate information of the null plug holes and the boundary of plug holes and provide the basis for automatic seedling supplement device, a automatic seedling supplementing test-bed was developed with seedling shortage detecting system and seedling supplementing system as core components. The seedling shortage detection system was composed of a hardware system for image processing, which include a CMOS industrial camera, a controller and a computer, and a software system programmed in MATLAB. Color images (RGB images) of Arabidopsis plug seedlings with the age of 25d and 35d was acquired with the automatic seedling supplementing test-bed. The grayscale images of seedlings and plug holes are obtained by graying the color images applying different linear transformations to three color components of R, G and B. Applying Otsu algorithm, binary images of the grayscale images of seedlings were obtained by threshold segmentation. Then the morphological corrosion operation and expansion operation of the binary images were carried out by the disk 2 × 2 type structure operator. By marking single connected domain, analyzing the characteristics of connected domain and removing the isolated area, the noise in the binary images were removed and the characteristic images of the seedlings were extracted from the background effectively. The feature images of plug tray were acquired by removing feature images of plug seedlings from that of the plug tray according to the extracted seedling information and de-noising. And then statistics on the peak value, peak width and standardizing structure of plug tray were made according to the row and column pixel of binary images of plug trays, so that the edges of plug holes were determined accurately. The statistics of the pixel of the seedling image in each plug hole were made to determine whether the hole was short of seedlings according to the feature images of the seedlings, the position, and the edge information of the plug holes. The results showed that the statistics value of the seedlings image pixel of the plug holes with the Arabidopsis plug seedlings of 25 and 35d were 1 895 to 4 572, and 3 725 to 8 710, respectively, while the statistics value of the seedlings image pixel of the plug holes without seedlings was 0. There were significant differences in the statistics value of the seedling image pixels between the plug holes with seedlings and the null plug holes. The testing results of the missing plug tray hole were the same as the actual situation, and the statistical value of seedling pixels corresponds to the projection area of stem and leaf. According to the threshold value of seedlings at different growth stages, the undeveloped seedlings can be marked for removing, so the seedling early stage was the super time for the detection of the null plug hole and the determination of the undeveloped seedlings. The accuracy rate of judging the null plug holes and the seedling holes with the Arabidopsis plug seedlings of 25 and 35d were all 100% applying the detection device. The accurate determination of position of the null plug hole provides basis for automatic supplementing system taking out the substrate without seedlings, removing dysplasia seedlings and supplementing the healthy seedlings with same age. [ABSTRACT FROM AUTHOR]

To improve the efficiency of mechanization in transplanting vegetable seedlings and achieve the automation of vegetable transplanter in dry land, an automatic vegetable pot seedling transplanter for dry land was developed. This transplanter is mainly composed of 2 rotating seedling picking-up mechanisms of planetary gear train with ellipse gears and incomplete non-circular gears, a horizontal and longitudinal intermittent seedling sending device, 2 eccentric disc dibble-type planting apparatuses, and a power transmission system. Two rotating seedling picking-up devices and 2 eccentric disc dibble-type planting apparatuses were symmetrically arranged along the longitudinal surface center of the machine, constituting 2 sets of automatic seedling picking-up and planting systems. The seedlings were provided automatically from 2 standard seedling pots with 128 holes using the horizontal and longitudinal intermittent seedling sending device. The engine drive shaft could transfer power to the principal axis of the apparatus through chain drive to enable the rotating and planting of the apparatus. Meanwhile, the principal axis of the apparatus could transfer the power to the rotating seedling picking-up mechanisms of planetary gear train with ellipse gears and incomplete non-circular gear and intermittent seedling sending device. Subsequently the intermittent seedling sending device could move horizontally and longitudinally to send the seedling pots to the target place, where the seedling picking-up mechanism took the seedlings from the pot to the eccentric disc dibble-type planting apparatus. To test the performance of this automatic vegetable pot seedling transplanter for dry land, the experiments were conducted in the test field where the soil condition met the mechanical industry standard in People's Republic of China Dry Land Planting Machinery (JB/T 10291-2013). The tomato seedlings (ZheZa 809) used in this experiment were cultivated in the standard seedling pots with 128 holes and transplanted at the age of 35 days with an average height of 150 mm. The seedling sending, picking-up, delivering and planting were all carried out at a constant transplanting frequency by adjusting the speed of the input belt pulley of the walking gear-box. The seedling transplanting efficiency was tested at the low speeds of 45, 60, 75 and 84 plants/min, respectively, as well as at the high speed of 96 plants /min. The coefficient of variation for planting spacing, missing transplanting rate, qualified rate of planting depth and qualified rate of transplanting were determined according to the mechanical industry standard Dry Land Planting Machinery (JB/T10291-2013). The seedling-standing ratio was calculated based on the Technical Specifications of Quality Evaluation for Rape Transplanter (NY/T 1924-2010) according to the standards for agriculture industry in People's Republic of China. The results showed that the coefficient of variation for planting spacing and the missing transplanting rate increased slightly at the range of 6.6%-8.9% and 1.7%-2.8% accordingly when increasing the planting frequency from 45 to 96 plants/min. However, the qualified rate of planting depth, the qualified rate of planting perpendicularity and the qualified rate of transplanting decreased slightly in the range of 97.5%-98.9%, 96.7%-97.8%, and 91.1%-94.7% with the increasing of planting frequency. The coefficient of variation for planting spacing, the missing transplanting rate, the qualified rate of planting depth and the qualified rate of planting all met the requirements of the machinery industry standards (JB/T 10291-2013), and the qualified rate of planting perpendicularity was also within the specified value in the standards for agriculture industry (NY/T 1924-2010). The automatic seedling-sending device, the seedling picking-up device and the planting apparatus of the automatic transplanter coordinated well with each other to complete the process for sending seedlings, picking up seedlings, delivering seedling, planting and soil covering automatically. [ABSTRACT FROM AUTHOR]

Branch and leaf pruning has been one of the most important links in the process of tomato planting for reducing the disease rate and increasing economic benefits. However, the manual pruning of tomato branches and leaves cannot fully meet large-scale production in recent years, due to the time-consuming and labor-intensive task. An accurate and rapid identification of the pruning position can be greatly contributed to the automatic operation of tomato branch and leaf pruning. In this study, a Recognition method was proposed for the pruning point of the tomato lateral branch using an improved Mask R-CNN. Firstly, the backbone network of ResNet50 in the original Mask R-CNN was replaced with the MobileNetv3-Large to reduce the model complexity. Efficient Channel Attention was added to the feature map C3 and C4, in order to focus more on the features of the lateral and main branch rather than other features. Then, the tomato lateral and main branches were predicted using the improved Mask R-CNN. Three steps were selected to avoid some single branches taken as multiple masks. The lateral and main branch masks were first distinguished by the aspect ratio of the bounding boxes. The overlap and pole constraints were then analyzed for the adjacent masks that belonged to the same branch. The masks with similar constraints were finally merged and joined in the images. The pruning point of the lateral branch was only positioned at one of the two ends of the lateral branch. The lateral pruning point identification was proposed with the help of the main branch, in order to determine the coordinate of the lateral pruning point. The range near the main branch was first determined. And then the branch pruning end was determined by estimating which one of the lateral branch left and right endpoints was in the range. The center point close to the endpoint of the pruning end was finally determined as the pruning point of the lateral branch. The original and improved Mask R-CNN were also compared to verify the detection performance of the lateral and main branches. The recall rate and precision of the original Mask R-CNN were 87.9% and 93.3%, respectively, whereas, the recall rate and precision of the improved Mask R-CNN were 91.2% and 88.6%, respectively. The number of backbone network parameters in the improved Mask R-CNN was only 21.1% of that in the original one. The average segmentation time of the improved Mask R-CNN decreased by 0.038 s than before. The results showed that the backbone network of MobileNetv3-Large reduced the model parameters with the high speed in the improved Mask R-CNN. More branches were recognized, particularly when adding the Efficient Channel Attention mechanism into the feature map C3 and C4. Lateral and main branches that were divided into multiple masks were selected randomly to verify the performance of merging masks. The merging success rate of lateral branch masks was lower than that of the main branch masks, due to the more outstanding curved shape of the lateral branch. The average success rate of merging masks was 86.2%, indicating the excellent performance of merging masks. The presence of multiple pruning points was effectively reduced, where the single branch was normally taken as the multiple masks. Some images were selected randomly in the test set to verify the recognition accuracy for the pruning point of the lateral branch. The result showed that the recognition success rate on sunny days was higher than that on cloudy. The average recognition success rate was 82.9%, which fully met the requirements of lateral branch pruning. This finding can provide the technical support for the tomato branch and leaf pruning automatically. [ABSTRACT FROM AUTHOR]

A contact model can be expected to accurately and rapidly characterize the tidal flat soil using the discrete element method (DEM). In this study, the contact model was optimized using the API secondary development function of the discrete element simulation software (EDEM). The target model was selected as the Hertz-Mindlin contact model. Firstly, the plasticity and adhesion were added to the normal contact force. Secondly, the sliding friction force with the relative tangential velocity was input into the tangential contact force. Then, the moist elasto-plastic adhesion (MEPA) model was obtained. The maximum normal pressure and normal adhesion force of the piston pull-out test were selected as the indexes of virtual calibration. Plackett-Burman test showed that there were significant effects of the correction coefficient of sliding friction, Young 's modulus of soil, static friction coefficient between soil particles, rolling friction coefficient between particles, static friction coefficient between particles, and geometry on the maximum normal pressure of the probe. The maximum adhesion force of the probe depended mainly on the adhesion coefficient between particles and the geometry and their interaction. The piston pullout test showed that the combination of parameters was achieved in the comparison indexes for the virtual calibration of tidal flat soil under target working conditions, including the 0 h settling time, 0 min contact time, and 210 mm/min separation velocity. The virtual calibration of DEM parameters was realized using non-significant and significant parameters. Among them, the non-significant parameters were referred to the existing data, while the significant parameters were continuously adjusted to make the simulated curve of piston pull-out force close to the actual. The MEPA, JKR, EEPA, and Bonding models were compared to characterize the tidal flat soil. The shoveling test platform was then built to verify the accuracy of the models. The tidal flat soil with a thickness of 11 cm was accumulated in the soil box. The digging shovel had a cutting depth of 5cm in the soil box. Two tests were set firstly, where the angle of the digging shovel was adjusted to 25° and 30°, respectively, and the forward speed was 0.08m/s. Then one test was that the digging shovel angle of 20°, and the forward speed of 0.16 m/s. The force was accurately obtained at the sensor of the shoveling device. The EDEM and the multi-dynamics (RecurDyn) software were also selected to simulate the shoveling. The test results showed that the average absolute error of the MEPA model was controlled within 50 N, compared with the actual shoveling resistance. The simulation accuracies of the MEPA model were about 65.957 %, 74.206%, and 59.326 % higher than those of the JKR, EEPA, and the Bonding model, respectively. Compared with the field test, the relative errors of the MEPA model were 5.598% and 6.362% in the soil accumulation thickness and side margins, respectively. Both errors were remained within 10%. Therefore, the MEPA model can be used to better simulate the tidal flat soil. It is of great significance to simulate and optimize the tidal flat device. [ABSTRACT FROM AUTHOR]

This study aims to determine the rheological properties and structural stability of the beach soil for shellfish cultivation. A series of rheological tests were carried out on the beach soil with different water content and standing time using a rotary rheometer. A single-factor test was also conducted to explore the influence of water content and standing time on the rheological properties of the beach soil. The soil shear was then analyzed to determine the influence of water content and standing time on yield stress. At the same time, the numerical model was established for the yield stress, water content and resting time after the central composite design (CCD) test in the response surface method (RSM). A comparison was then made on the influence of water content and resting time on yield stress. Finally, the reliability and accuracy of the regression model were further verified to compare the error between the measured and the actual values. The results show as follows: 1) The shear stress gradually decreased with the increase of water content under the same shearing rate in the shearing process, whereas there was a gradual increase with the increase of standing time; In addition, the beach soil was characterized by the consolidation. The longer the static time was, the longer the solid-liquid transformation was, and the later the solid-liquid node was. The static time shared the greatest impact on the solid-liquid node in the early stage. The longer the static time was, the smaller the impact on the solid-liquid node was. 2) When the average shear stress and yield stress were at the shear rate of 10 - 60 s-1 under the same standing time, the overall shear stress of the tidal flat soil gradually decreased with the increase of water content, thus reducing the structural stability of the tidal flat soil. In addition, the overall shear stress of the tidal flat soil gradually increased under the same moisture content with the increase of static time, indicating the increase in the structural stability of tidal flat soil. The change rate of yield stress with the static time showed that there was a cut-off point of moisture content in the beach soil, which was between 64.5% and 67.0%. In the samples exceeding this moisture content cut-off point, the change rate of yield stress with the static time remained stable after the static time exceeded 31 h, where there was no fluctuation in the yield stress. The yield stress ranged from 2 240 to 4 380 Pa. In addition, the yield stress in the sample of 62.0%-69.5% was between the 0-53 h, and the yield stress between 62.0%-69.5% was between 1 870-5 410 Pa. 3) The P and R 2 values of numerical models were above 0.05, and 0.953 4, respectively, indicating the high reliability and accuracy. There was a more significant influence of water content on the yield stress, compared with the resting time. However, the interaction between water content and resting time shared no significant influence on the yield stress. The test verified that the measured yield stress was basically consistent with the predicted one, with an error of less than 15.0%. The rheological properties of beach soil can be better characterized as well. The findings can provide a strong reference for the research and optimization of beach shellfish harvesting machinery [ABSTRACT FROM AUTHOR]

Automatic harvesting of fruits and vegetables is a key process to realize the automation and intellectualization of agricultural production, which can reduce production costs and improve the production efficiency. Tomato is one of the most fruits and vegetables planted in China. The researches on tomato harvesting robots are focused on the single fruit harvesting. The end-effector of the harvesting robot touches the tomato fruit directly and picks one tomato once during harvesting, so the tomato fruits are easy to be damaged and the harvesting efficiency is very low. To overcome the above shortages of the single fruit harvesting, the method of the tomato cluster picking was presented, and the determination method of picking point was developed based on the corner detection on the tomato stem skeleton for the binocular vision system of the tomato cluster picking robot in the paper. The test platform of the vision system was set up and the experiments on the determination of the picking points have been conducted. In the paper, the color images of the tomato clusters were collected in greenhouse in the agricultural production base. The target tomato cluster was segmented from background by OTSU method according to the color features of the tomato clusters and denoised by the morphologic method and threshold method. The centroid of tomato fruit cluster was obtained by enclosing rectangle method. Furthermore, the rectangle region of interest of the target stem was recognized according to constraint relationship between the fruit cluster and the fruit stem, which included the whole stem of the target tomato cluster. Converting the RGB (red, green, blue) color channel map to the HSV (hue, saturation, value) channel map, there are different grayscales between the stem and the background. The target stem of the tomato cluster was separated from background by using the gray difference of H channel and denoised by morphologic method, median filtering method and extracting the maximum connected region. To avoid the effects of the irregular stem on the picking point determination, we abstracted the stem skeletons by Zhang’s thinning algorithm to simplify the stem original shape and extract the kernel information of the stem, and then, the Harris algorithm was applied to extract the corner points of the skeleton, and the corner points were sorted from large to small according to the vertical axis coordinates. The picking feature points on the target stem were obtained by averaging pixel coordinates of the 5 corner points. Sixty groups of experiments on the determination of picking points of the tomato cluster were carried out on the binocular vision test platform. The results show that the success rate of position information obtaining of the picking point on the stem is 90%. Comparing X and Y coordinates data of the picking point and centroid of the tomato fruit clusters, the X coordinates of the picking feature points vary in small range, while Y coordinates vary within wide limits, which results from the main stem growing in longitudinal direction. The mean absolute value of the pixel deviation of the Y coordinates is 21. The research provides the basis for locating the picking point on the stem for the tomato fruit cluster picking robots. [ABSTRACT FROM AUTHOR]

The seeds cannot fully sprout owing to the seed quality and environmental differences. So it is difficult to provide seedling pots without empty seedling holes for automatic transplanting. In order to reduce the number of seedling shortage and the manual labor intensity of seedling supplement, and to improve transplanting quality, a method for supplementing seedlings in cotyledon phase was presented in the paper. According to the delicate seedling, undeveloped root system and the unconsolidated substrate in pot, a new type of end-effector for supplementing pot-seedling in cotyledon phase with 4 seedling shovels was designed. The 4 angular seedling shovels were synchronously driven up and down by stepper motor moving the X-shaped press rod. They can shape enveloping surface when they are inserted into the lower limit position of the whole pot and surrounded the seedlings from the 4 sides of seedling substrates during seedling. And then the pot seedling was taken out from seedling hole and transplanted to the target hole. The seedling supplementing was finished while the pot seedling was left in the target pot hole and the seedling spades lifted it up along the inclined guide. The 3 factor and 3 level orthogonal tests were conducted respectively with the Zheza809 tomato pot seedlings in cotyledon phase with the seedling age of 16, 21 and 26 d cultivated in standard seedling tray with 72 holes. In the test, the velocities of inserting and taking out of matrix for the shovels were measured by speedtro-50 laser velometer, and the average velocities were 29.85 and 31.12 m/s respectively. The mixture ratio of peat, vermiculite and perlite in nursery matrix, the moisture content of the substrate and the seedling age were chosen as factors. The matrix net rate, which is the percentage of the weight of pot seedling taken out from the pot holes by the end-effector in the total mass of the pot seedling in the pot hole, was set as index. The results indicate that the net rate of taking out the free substrate without seedlings or the pot seedlings in cotyledon phase is affected significantly by the pot seedling age and the mixture ratio of peat, vermiculite and perlite in nursery matrix. The net rate increases with the seedling age, which reaches the highest value at the end of cotyledon phase and the mixture ratio of peat, vermiculite and perlite in nursery matrix of 2:2:1. The effect of matrix water content from 67.1% to 79.2% on the net rate is not significant, while the net rate keeps a high value owing to good cohesive force and plasticity of the substrate when the moisture content of the substrate is 74.1%. When the growth age is 26 d, the mixture ratio of peat, vermiculite and perlite in nursery matrix is 2:2:1 and the moisture content of the substrate is 74.1%, the average net rate reaches the maximum of 89.5% and the shape and volume of the seedling pot keep complete. To further verify the optimal combination obtained by the test, the experiments of seedling taking out and supplementing were carried out with tomato pot seedlings with the age of 26 d, the mixture ratio of peat, vermiculite and perlite in nursery matrix of 2:2:1 and suitable substrate moisture content. The results show that the success rates for the free substrate without seedlings and pot seedlings are both 100%, which can take out the pot seedlings in cotyledon phase with crisp plant and loose matrix as a whole and fill them into target hole on the seedling pot completely. [ABSTRACT FROM AUTHOR]