Search Results (5)

The main goal of a garlic planter is to plant garlic cloves individually with even spacing, requiring minimization of missed or multiple plantings. This study adopted an air-suction metering device to increase planting speed. To handle irregularly shaped garlic cloves effectively, soft silicone suction holes were fabricated and attached to the metering device. Performance was assessed by varying suction hole diameters (8, 11, 14, and 17 mm) and material hardness levels (1.4, 16.7, and 27.2 HA Shore A) at multiple-metering rates. The optimal metering rate of 98.2% was achieved with a 14 mm suction hole diameter and 16.7 HA hardness. This success was attributed to the soft suction hole effectively conforming to the garlic clove surface. The findings revealed a critical limitation: when metering rates exceeded 90%, multiple-metering rates increased linearly, indicating inherent constraints of air-suction techniques for single-seed metering. These results provide valuable insights into design parameters needed to improve reliability, operational efficiency, and mechanization performance of garlic planters. The metering device type is expected to be applicable to autonomous or unmanned agricultural machines, advancing agricultural mechanization capabilities. Full article

With the advancement of modern agricultural technology, precision seeding has emerged as a critical approach to enhancing the crop yield and quality. Consequently, a garlic seeder insertion mechanism was developed to improve the accuracy and efficiency of garlic seeding. The single-seed extraction mechanism and the adjustment mechanism for the garlic clove direction were designed based on the appearance dimensions of garlic cloves, enabling precise single-seed selection and orientation. A kinematic model of the insertion planting process was established, with key parameters meticulously described and analyzed, providing theoretical support for determining optimal insertion parameters. A timing sequential control method was adopted to accurately control the periodic motion of the insertion planting mechanism. A speed detection device was utilized to monitor the travel speed of the crawler-type chassis and a rotational speed controller was developed to accurately regulate the rotational speed of the insertion mechanism, ensuring uniform planting distances. Field trials demonstrated that when the preset planting distance was set at 150 mm and sowing operations were conducted at speeds of 0.10 m/s, 0.15 m/s, and 0.20 m/s, the average sowing spacing values were 148 mm, 149 mm, and 151 mm, respectively, the maximum sowing spacing error and root mean square (RMS) error were 30 mm and 7 mm, with an average error of less than 10 mm, and the maximum coefficient of variation was 0.046. The upright rate exceeded 85%, and the missing seeding rate was below 5%. The above results indicated that the designed garlic planting machine insertion mechanism and control method conform to the agronomic requirements for garlic sowing operations. Full article

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. Full article

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. Full article

According to the agronomic requirements of garlic sowing, the garlic morphology is studied and a garlic seed metering mechanism with excellent seeding performance is designed. Based on this design, a new garlic seeding machine with an adjustable-size seeding device is developed to realize efficient single-seed metering and seeding of different varieties of garlic. Further, the design scheme of the garlic seeder prototype is established, with the key components of the garlic seeding being designed on the basis of the garlic seeding mechanism. To achieve garlic single-seed metering for different varieties of garlic, the optimal adjustment size of the garlic seed metering device is determined through discrete element simulation analysis. A field experiment confirms the effectiveness of applying the proposed garlic planter to field sowing in terms of the metrics of missing seed and multiple seed rates. The results of the discrete element simulation test reveal that an adjustment size of 40 mm yields the best single-seed metering performance. At an operating speed of 15–35 rpm, the metering device can achieve more than an 80% qualification rate of single-seed metering, with a unit speed of 0.628–1.465 m/s. Thus, the developed garlic seeding device meets the requirements of precision sowing in China and can effectively realize the mechanized planting of garlic. Full article