Design and Experimental Study of Spoon-Clamping Type Garlic Precision Seeding Device

Zhang, Xinyan; Yi, Shujuan; Tao, Guixiang; Zhang, Dongming; Chong, Jun Hua

doi:10.1155/2022/5222651

Cited by 3 publications

(3 citation statements)

References 10 publications

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“…The setting range of simulation parameters was determined by combining the results of physical test measurements [35][36][37] , as shown in Table 6. The discrete element model of sprouted American ginseng seeds was established [38][39][40] , (as shown in Figure 8a).…”

Section: Establishment Of Discrete Element Simulation Modelmentioning

confidence: 99%

Parameter calibration of American ginseng seeds for discrete element simulation

Zhang¹,

Wang²

2022

International Journal of Agricultural and Biological Engineering

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The accurate contact parameters of American ginseng seed particles are the basis for establishing the discrete element simulation model of American ginseng seeds. The parameters of American ginseng seeds were calibrated by combining the physical tests and simulation tests together. The basic physical parameters, contact parameters and repose angle of sprouted American ginseng seeds were determined by physical tests. The simulation parameters were significantly screened by conducting the Plackett-Burman test. Meanwhile, it was determined that the collision recovery coefficient, static friction coefficient and rolling friction coefficient of interspecific contact parameters have significant influences on the repose angle of the simulation test. By the steepest climb test, the optimal interval for the value of the significance parameter was determined. Subsequently, the second-order regression equation between contact parameters and the repose angle was established, the regression equation was optimized and solved, and the best combination of simulation parameters was determined. The collision recovery coefficient between sprouted American ginseng seeds was 0.346, the static friction coefficient was 0.769, and the rolling friction coefficient was 0.490. By the calibrated seed group of American ginseng for discrete element simulation test, the average repose value angle was 38.80°, and the relative error with the measured repose angle was 0.733%. The results revealed that the simulation parameters of American ginseng seeds were reliable, which can provide a basis for the design and performance optimization of American ginseng seed-metering device in the later stage.

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Section: Establishment Of Discrete Element Simulation Modelmentioning

confidence: 99%

Parameter calibration of American ginseng seeds for discrete element simulation

Zhang¹,

Wang²

2022

International Journal of Agricultural and Biological Engineering

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“…The authors considered the planting rate, planting interval, and clove damage rate. Zhang et al [7] conducted one-factor and multifactor experiments to optimally design key components of a spoon-clamping type planter and optimize working parameters. As mentioned above, experimental studies offer high prediction accuracy using actual metering devices.…”

Section: Introductionmentioning

confidence: 99%

“…Research has been conducted to enhance planting performance not only in garlic but also in other crops [11][12][13][14][15][16][17]. However, studies that utilized experimental approaches have limitations in design variables or operating conditions and are applicable only within specific sample ranges [3,[5][6][7]. Previous studies that used simulation models executed optimal designs without design variable constraints; however, in some cases, the analysis model was not validated [8], and the actual shape of garlic cloves was not considered in the analysis model, constraining its reliability [10,11].…”

Section: Introductionmentioning

confidence: 99%

Bucket Size Optimization for Metering Device in Garlic Planter Using Discrete Element Method

Lee

Kim

et al. 2023

Agriculture

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In this study, the discrete element method was used to optimize the bucket size for the metering device in a garlic planter for enhancing the productivity of garlic farming according to the garlic size. Statistical information concerning the actual shape of garlic cloves was incorporated, and the mechanical properties of garlic were determined using the bulk density, sliding test, and repose angle test for enhancing the fidelity of the simulation model. The optimal bucket size achieving the target plant rate of 97.5% was determined using the developed discrete element model for the three garlic size groups. The linear search method was used for optimization, and batch simulation was performed to validate the optimized results and confirm the performance index of the metering device. A Gaussian distribution based on statistical information accounted for the various garlic sizes in each group. Finally, a metering test verified the reliability of the optimization technique. The differences between the simulation and test results were within 10% for all performance indices, including missing plant rate, multi-plant rate, and planting rate, indicating the high reliability of the analysis model. Subsequently, the larger garlic groups (Groups 2 and 3) exhibited metering performance close to the target plant rate.

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Contour Resampling-Based Garlic Clove Bud Orientation Recognition for High-Speed Precision Seeding

et al. 2022

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Achieving fast and accurate recognition of garlic clove bud orientation is necessary for high-speed garlic seed righting operation and precision sowing. However, disturbances from actual field sowing conditions, such as garlic skin, vibration, and rapid movement of garlic seeds, can affect the accuracy of recognition. Meanwhile, garlic precision planters need to realize a recognition algorithm with low-delay calculation under the condition of limited computing power, which is a challenge for embedded computing platforms. Existing solutions suffer from low recognition rate and high algorithm complexity. Therefore, a high-speed method for recognizing garlic clove bud direction based on deep learning is proposed, which uses an auxiliary device to obtain the garlic clove contours as the basis for bud orientation classification. First, hybrid garlic breeds with the largest variation in shape were selected randomly and used as research materials, and a binary image dataset of garlic seed contours was created through image sampling and various data enhancement methods to ensure the generalization of the model that had been trained on the data. Second, three lightweight deep-learning classifiers, transfer learning based on MobileNetV3, a naive convolutional neural network model, and a contour resampling-based fully connected network, were utilized to realize accurate and high-speed orientation recognition of garlic clove buds. Third, after the optimization of the model’s structure and hyper-parameters, recognition models suitable for different levels of embedded hardware performance were trained and tested on the low-cost embedded platform. The experimental results showed that the MobileNetV3 model based on transfer learning, the naive convolutional neural network model, and the fully connected model achieved accuracy of 98.71, 98.21, and 98.16%, respectively. The recognition speed of the three including auxiliary programs was 19.35, 97.39, and 151.40 FPS, respectively. Theoretically, the processing speed of 151 seeds per second achieves a 1.3 hm2/h planting speed with single-row operation, which outperforms state-of-the-art methods in garlic-clove-bud-orientation recognition and could meet the needs of high-speed precise seeding.

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Design and Experimental Study of Spoon-Clamping Type Garlic Precision Seeding Device

Cited by 3 publications

References 10 publications

Parameter calibration of American ginseng seeds for discrete element simulation

Parameter calibration of American ginseng seeds for discrete element simulation

Bucket Size Optimization for Metering Device in Garlic Planter Using Discrete Element Method

Contour Resampling-Based Garlic Clove Bud Orientation Recognition for High-Speed Precision Seeding

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