Search

Lifting and transportation equipment can greatly contribute to the harvesting of mountain bananas in the agricultural industry. However, some challenges still remain during post-harvest transportation, such as high labor intensity and cost, easy damage to fruits, and safety accidents. In this study, an Unmanned aerial vehicle (UAV) autonomous lifting and transportation equipment was designed to harvest the mountain bananas. The autonomous grasping and unloading of banana shafts were realized during the lifting and transportation. Manual participation was effectively reduced to ensure the safety of operators. The grasping mechanism, lifting gear, guide mechanism, and connecting parts were designed, according to the measured morphological features of the mountain banana. The specific needs were fully met by the postharvest transportation of mountain bananas. A three-dimensional model of the equipment was established using SOLIDWORKS software. The communication system of the upper and lower machines was also built. The key components of lifting and transportation equipment were simulated and theoretically calculated, using the explicit dynamics and magnetostatic analysis of ANSYS Workbench software. The parameters of an electromagnet and steering gear were determined to meet the requirements. The simulation highlighted that the better performance of equipment was achieved for subsequent testing. A laboratory experiment was then carried out to validate the efficacy of the equipment. The success rates of lifting and transportation were 92.59%, 96%, and 88.89%, respectively. The average time of grasping and unloading was 63.8 and 20.8 s, respectively. The effectiveness of equipment was verified to transport the mountain bananas. Furthermore, the field experiment UAV was carried out on the lifting and transportation equipment. The success rate of lifting and transportation was 83.33%, the total time of grasping and unloading was 90.8 s, and the average speed of lifting and transportation was 0.99 m/s, which was more than three times that of manual carrying speed (0.17~ 0.33 m /s). Because the banana bunch was suspended under the equipment without contacting with other objects, there was no damage to the fruit finger, indicating the better quality of the fruits during transportation. Nevertheless, the equipment produced a large lateral swing on the positioning and grasping of the banana shaft, due mainly to the strong wind field under the UAV. Therefore, there were some differences between the field test and the indoor experiment, but the expected performance was achieved anyway. The structure can be further optimized to improve the success rate of lifting and transportation of the equipment for less operation time. In conclusion, the equipment can fully meet the operational requirements for the autonomous lifting and transportation of mountain bananas by UAV. Overall, these findings can provide a strong reference for efficient, low-loss, and safe unmanned lifting and transportation equipment. A reliable and efficient solution can be offered for autonomous lifting and transportation in the banana industry towards sustainable growth and productivity. [ABSTRACT FROM AUTHOR]

Copyright of Transactions of the Chinese Society of Agricultural Engineering is the property of Chinese Society of Agricultural Engineering and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Banana fruits are typically harvested and transported while still green and hard, referred to as green bananas. However, frequent collisions often occur between fruit fingers, working parts, and adjacent fruit fingers during this stage. This study aims to investigate the mechanical properties of fruit fingers at different growth positions. The experimental material was selected as the finger of the banana plant (Musa spp.) on a supporting platform created by 3D printing technology. A complete factorial experiment was performed using a pendulum method. The test conditions included three impact energies (0.27, 0.52, and 0.88 J) and three repeated times (1, 5, and 10) across three growth positions (the inner rows of the first and second nodes, the outer rows of the first and second nodes, and the inner and outer rows of the last nodes) within a green banana bunch. Mechanical parameters were calculated, such as peak acceleration, coefficient of restitution (CoR), bruise area (BA), bruise volume (BV), and bruise susceptibility (BS). Among them, the BS represented the ratio of BV to the total absorbed impact energy. A multi-way analysis of variance was also conducted at a significance level of 5% to determine the statistical significance of the mean values of the parameters. The results demonstrated that there was a strong positive correlation between peak acceleration and bruise sizes, and a strong negative correlation between the CoR and BS. Specifically, the higher peak acceleration resulted in the more severe damage to the fruit fingers, whereas the smaller CoR made the green bananas more susceptible to damage. Moreover, the impact energy, repeated times, their interaction, and the location of banana growth significantly dominated the mechanical parameters of the BA, BV, and BS. The higher impact energy led to the greater peak acceleration, BA, BV, and BS, whereas, the lower CoR was observed. Consequently, the higher impact energy tended to increase the susceptibility of banana fruit to damage. Once the impact energy was below the threshold for the plastic deformation of green bananas, the peak acceleration and CoR were improved non-linearly with each impact up to the first five impacts, after which the increment decreased progressively for each subsequent impact. Similar trends were observed for the BA and BV, while the opposite was true for the BS. Therefore, the susceptibility of banana fruit decreased with the increasing impacts. It is challenging for the reduced number of impacts to less than five during production. Fruit damage can be minimized to reduce the energy required to hit the banana fingers. Once the impact energy exceeded the threshold, an initial increase in the peak acceleration and CoR was followed by a decrease, due to the plastic deformation and ductile fracture of the banana finger from continued impacts. Both BV and the rate of increase were enhanced with the number of repetitions. Furthermore, the BS of banana fingers in the outer row was generally smaller than that in the inner row, indicating that the inner row was more susceptible to impact-induced bruise damage. These findings can provide a strong reference to managing the protective measures and risk factors during banana harvesting and transportation. [ABSTRACT FROM AUTHOR]

China is the largest fruit producer and consumer in the world, while most orchards in the south of China are located in hilly areas. The ropeways can be expected for transporting orchards in mountainous areas. But the harsh working environment can often lead to machine failures in recent years. Therefore, it is of great significance to investigate and solve the problems at the initial stage of failures. Generally, the conventional bearing fault diagnosis system includes five links: signal acquisition, feature extraction, state identification, diagnosis analysis. and decision intervention. Deep learning is widely applied for bearing fault detection in recent years. Traditional machine learning also needs to manually extract the fault features at present, depending mainly on the deep professional knowledge. However, it is high demand for the high performance of fault diagnosis, particularly for simple structures and less calculation during feature extraction. This study aims to realize the fault diagnosis of ropeway drive system bearings in the mountain orchards with poor network environment, in order to ensure the safe and stable operation of ropeway in the mountain orchards. A one-dimensional end-to-end lightweight CNN detection, 1DMRL-CNN was established to directly detect the one-dimensional vibration signals for the mountain ropeways. Specifically, the new model was established using residual structure and depth separable convolution. The depth separable convolution was applied to greatly reduce the parameter and calculation amount of the improved model. In addition, the residual structure was applied to make up for the accuracy loss caused by depth separable convolution. The parameter amount and complexity of the model were reduced significantly while ensuring the detection accuracy. The stem block and BN layer were then introduced to improve the robustness and generalization ability of the new model suitable for the variable load working state of the ropeway. Finally, the hard_swish activation function was also adopted in the model. The channel attention mechanism was added to the last layer of the main module, in order to improve the feature extraction ability of the network model. Two datasets (Paderborn University and Case Western Reserve University) were used to verify the comprehensive performance, stability under variable load and anti-noise interference performance. The Paderborn University dataset showed that the fault classification accuracy of the improved model was 99.43%, which was 0.56, 0.99, and 1.23 percentage points higher than those of the one-dimensional classical CNN, similar optimal network, and the lightweight CNN architecture optimal network. The parameters and floatingpoint calculations were 83.44 kb and 0.20 M, which were 2.19%, 1.18%, 0.75%, and 0.83% of the one-dimensional classical CNN classification network architectures (such as Resnet18, Resnet34, Resnet50 and VGG16), 6.19% and 30.40% of the same type of networks 1D-Lenet5 and 1D-Inception, while 4.2%, 2.07%, 2.84%, 3.32% and 5.16% of the one-dimensional lightweight CNN architecture MobileNetV1, MobileNetV2, MobileNetV3-Large, ShuffleNetV1 and EfficientNet-1. In addition, the Case Western Reserve University dataset showed that the average accuracy rate of the improved model was 96.70% in six load scenarios, which was 9.1, 4.7, and 10.5 percentage points higher than those of Resnet18, WDCNN, and MobileNetV3-Large, respectively. The average recognition accuracy was 99.14% under four noise conditions, which was 4.74, 1.24, and 5.51 percentage points higher than those of Resnet18, WDCNN and MobileNetV3-Large, respectively. Finally, the fault classification of the improved model under actual working conditions was verified by the established ropeway dataset, where only 2 fault samples out of 1 400 samples were predicted incorrectly. The new network model was suitable for the bearing fault detection of mountain orchard transport ropeway, due to the small parameters, high accuracy and robustness under variable load and noisy working conditions. [ABSTRACT FROM AUTHOR]

Mechanical damage is one of the most important factors to affect the banana quality and sales. The collision damage of green bananas can also be early identified by naked eye. However, manual sorting of green bananas cannot fully meet the large-scale production in recent years, due to the time-consuming, low efficiency and easy to produce the deviation. In this study, the nondestructive detection and classification of mechanical damage of green bananas were realized to utilize the spectral and image information using hyperspectral technique. Firstly, a simulation device of collision damage was built and designed, according to the structural characteristics of green bananas. The same batch of healthy green Brazilian bananas were subjected to group impact experiments with different impact energies during simulation of collision device. Secondly, the samples were placed in the hyperspectral imager within 48 h after the impact to scan and collect the sample data. The sample data after black/white and lens correction were imported into the software ENVI5.3. The region of interest (ROI) of the image was selected to obtain the average spectral reflectance data and image information on the surface of the healthy green banana and the wounds. Thirdly, savitzky golay (SG) and multiplicative scattering correction (MSC) were combined to preprocess the original spectral data, and then Monte Carlo algorithm and principal component analysis (PCA) combined with Mahalanlet distance were used to remove the abnormal samples. A series of experiments were carried out to verify the classification and grouping accuracy of samples with different damage degrees. Support vector machine (SVM), least square support vector machine (LSSVM), and particle swarm optimization-least square support vector machine (PSO-LSSVM) were utilized to process the spectral reflectance data after removing abnormal samples. The successive projections algorithm (SPA) and the competitive adaptive reweighted sampling (CARS) were used to extract the characteristic wavelengths of green bananas, whereas, the interval combination optimization algorithm (ICO) was used to verify the accuracy of the extracted characteristic wavelengths. Finally, the low-dimensional image was obtained under the characteristic wavelength. The wound area and the pixel distribution data were identified using the binarization processing, Canny edge detection and image segmentation. The training and test set data of BP neural network were used to combine with the spectral reflectance data under the full pixel points derived from ENVI5.3. A nondestructive testing model was established for the classification and grouping of mechanical damage degree of green bananas. The test results show that the invisible minor collision damage was identified by hyperspectral technology. The recognition accuracy of the BP neural network detection model was 97.53%,92.59%, 93.82%, and 96.29% for the test set of healthy, mild collision, moderate collision, and severe collision samples. The accuracy of judging the overall damage degree was 95.06%. The visual image of damage level was output for the later use. This finding can provide the theoretical support to arrange the shelf life of green banana, particularly for the instrument of hyperspectral technology to identify the damage of green banana in real time. [ABSTRACT FROM AUTHOR]

Copyright of Journal of South China Agricultural University is the property of Gai Kan Bian Wei Hui and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Banana has been one of the major fruits in the production and consumption in China. But, the banana harvesting is a high labor consuming activity with the low efficiency and large fruit damage. This study aims to improve the operation efficiency and quality of the banana in the picking robot. An accurate and rapid recognition was also proposed to locate the fruit axis at the bottom of banana using the YOLOv5 algorithm. Specifically, a coordinate attention (CA) mechanism was fused into the backbone network. The Concentrated-Comprehensive Convolution Block (C3) feature extraction module was fused with the CA attention mechanism module to form the C3CA module, in order to enhance the extraction of the banana feature information. The original Complete Intersection over Union (CIoU) of loss function was replaced with the Efficient Intersection over Union (EIoU). As such, the convergence of the model was speeded up to reduce the loss value. After that, the anchor point was determined for the test to improve the regression formula of prediction target box. The camera coordinate system of the point was transformed to deal with the three-dimensional coordinates. D435i depth camera was then used to locate the fruit axis at the bottom of banana. The original YOLOv5, Faster R-CNN, and improved YOLOv5 model were trained to verify the model. The accuracy of the improved model increased by 2.8 percentage points, the recall rate reached 100%, and the average accuracy value increased by 0.17 percentage points, compared with the original. There were the 52.96 percentage points higher precision, 17.91 percentage points higher recall, and 21.26 percentage points higher average precision value, compared with the Faster R-CNN model. The size of the improved model was reduced by 1.06MB, compared with the original. The field test was conducted on July 1, 2022 in Dongguan Fruit and Vegetable Research Institute, Guangdong Province, China. A test was realized for the random real-time location of the fruit axis at the bottom of banana in the field environment. The original YOLOv5, Faster R-CNN, and improved YOLOv5 model were used to recognize and localize the single and double plants in the range of 1.0-2.5 m. Each model was tested for 10 times. The estimated and real values were recorded to calculate the mean error, the mean error ratio, and the mean value. The original YOLOv5, Faster R-CNN, and improved YOLOv5 model all performed better to identify the banana in the field of view within the localization range and the estimated values. Among them, the mean errors were 0.085, 0.168, and 0.063 m, respectively, while the mean error ratios were 4.165%, 8.046%, and 2.992%, respectively. The mean values of error and error ratio in the improved model were reduced by 0.105 m, and 5.054 percentage points, respectively, during the original training, compared with the Faster R-CNN model. By contrast, the error and error ratio of the improved YOLOv5 model were reduced by 0.022 m and 1.173 percentage points, respectively, compared with the original. In addition, the measurement error greater than 0.2 m in the test was a locating error. Only test 6 showed the locating errors with the low error rate in the improved YOLOv5 model. The locating errors were found in tests 3 and 4 of the original, while the Faster R-CNN model showed the localization errors in the tests of 1, 4 and 8. Together with the ideal localization, the lower error and higher dimensional accuracy, the improved YOLOv5 model was conducive to the migration application and rapid recognition of bananas in the complex environments. In this case, the vision module of banana picking robot can meet the requirements for the axial locating of the undertaking mechanism at the bottom of banana fruit in the field environment. [ABSTRACT FROM AUTHOR]

Copyright of Journal of South China Agricultural University is the property of Gai Kan Bian Wei Hui and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of South China Agricultural University is the property of Gai Kan Bian Wei Hui and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of South China Agricultural University is the property of Gai Kan Bian Wei Hui and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of South China Agricultural University is the property of Gai Kan Bian Wei Hui and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Transactions of the Chinese Society of Agricultural Engineering is the property of Chinese Society of Agricultural Engineering and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Transactions of the Chinese Society of Agricultural Engineering is the property of Chinese Society of Agricultural Engineering and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

There are a lot of blind application of chemical fertilizers in the planting of bananas, which leading to waste of fertilizers and environmental pollution. According to the distribution pattern of banana root system, a variable fertilizer apparatus was designed in this paper. Based on the image processing of banana root morphology and the statistics of the area area pixels, the distribution pattern of the banana root system in the horizontal direction was analyzed. Based on the characteristics of the root area distribution, the change of fertilization amount along with the root distribution in horizontal was normally distributed, that is, the amount of fertilizer in the main root was the largest. Based on this, a rotary plate variable fertilizer apparatus was designed. By calculating the fertilizer amount distribution discharged on the simulated ground by the simulated fertilizer discharger, it was determined that the distribution law of the fertilizer amount along the forward direction of the machine was also normally, which was the same with the banana root distribution. In order to ensure that the fertilizer discharging amount and the center position of the normal distribution of the root are consistent in space, taking the spatial consistency of the center positions of the two normal distribution as the goal, the operation process planning was carried out for the image acquisition interval time, algorithm time, motor response time, fertilizer falling time, etc., and the accompanying operation control model of the machines was established. The variable control system was used to achieve and ensure that the center positions of the two normal distributions were consistent in space. The center combination simulation test was carried out with the movement speed, the moving period of the rotating plate, the central angle of curved slot and the opening size of curved slot as independent variables, and the length of fertilizer distributing and the fertilizer discharge amount were used as test response values. The result of optimal parameter combination was that the movement speed is 0.25 m/s, moving period of the rotating plate is 10.00 s, the central angle of curved slot is 90.00°, and the opening size of curved slot is 20.00 mm. The model and test errors, simulation and test errors of the length of fertilizer discharge are 4.98% and 3.30% respectively, the model and test error, simulation and test error of fertilizer discharge amount are 11.15% and 6.68%, respectively. The field test was conducted to examine the field operation effect of the fertilizer discharger, the average qualified rate of fertilizer discharging length was 96%, the average qualified rate of fertilizer discharge amount was 95.33%，the operation performance of fertilizer apparatus was good. [ABSTRACT FROM AUTHOR]

Controlling the humidity in environment is very important for qualities of storage. In order to improve the working performance of desiccant rotary wheels of dehumidification system for the storage and the relative humidity uniformity of the storage environment, a set of pipeline type wheel dehumidifying device was integrated into the existing dehumidification system in this study. By increasing pipeline type passage of dry air, the pattern of indoor air flow was rearranged from middle-in and bottom-out to pipe-in and bottom-out. In this study, hybrid rice seeds were used as the test material. To determine the dehumidifying effect of the modified system, a three-factor three-level orthogonal experiment for the prototype was carried out and the results were analyzed. Experiment was carried out at temperature of 25 ℃, airflow velocity of 5 m/s, and cargo stacking in the intermediate bottom. For the 3 factors in the experiment, pipeline location was set as 1.25, 0.65 and 0.05 m, pipe diameter was set as 50, 40 and 32 mm, and number of holes on pipes was set as 5, 10 and 20. With the pipeline type wheel dehumidifying device, the relative humidity in the container could be decreased from 95% to 30%. When the pipeline was located at 0.05 m above the bottom of the container, the pipeline diameter was 50 mm, and the number of holes on pipes was 20. It was found this relative humidity distribution was the most uniform, and the coefficient of variance of relative humidity was 3.81%, which was about only 15% of the worst case. Because the position of the air supply pipe determined the revolving flow of the whole flow field. Meanwhile, it was found that the best dehumidifying effect occurred under the conditions that the pipeline location was 0.65 m, the pipe diameter was 40 mm, and the number of holes on pipes was 20. The capacity of the desiccant in combination of the pipeline location of 0.65 m, the pipe diameter of 40 mm, and the 20 holes on pipes was 71.74 g/(kg∙h), which was about twice of that in the worst case. The corresponding efficiency of the desiccant was 0.0906 g/(kg∙h∙kJ), which was 3 times that of the worst case. Taking into account the overall effect of the desiccant, more attention should be paid to the performance of the desiccant capacity and efficiency. The results show that the number of holes on pipes makes the strongest unique contribution to the capacity and efficiency of the desiccant, followed by the pipe diameter and the pipeline location. Overall, the research can provide guideline information for the optimization design of dehumidification system for the storage. [ABSTRACT FROM AUTHOR]