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Picking robots have been widely used for citrus harvesting in recent years. However, the rest citrus during continuous picking can be irregularly disturbed by the wind, robot force, and the load weight of bearing branches under the natural environment. The citrus in the disturbed state cannot be rapidly and accurately detected, and then localized online, leading to the low efficiency of automatic robotic picking. In this study, online target detection and rapid localization were proposed using improved YOLOv5s+DeepSORT. The position of citrus at rest was predicted using the motion-tracking trajectory of disturbed citrus within a short period of time. The coordinates of the citrus were then obtained rapidly. Firstly, the CBAM (Convolutional Block Attention Module) attention mechanism was added to the YOLOv5s network, in order to detect the small and occluded targets. The SIoU loss function was used to enhance the direction matching between the prediction and the calibration frame, in order to improve the convergence speed of regression. Secondly, the target re-identification network was improved in the DeepSORT more suitable for the feature extraction of citrus targets. The feature extraction of the network was enhanced to improve the tracking performance on the disturbed citrus; The Count counter was used to accumulate the number of tracking frames in each citrus for an optimal target. Since the disturbance of the rest citrus was progressively propagated over time, the localization prediction and picking were only for targets with optimal tracking trajectories at a time. The real-time updating was realized in real time. Finally, the values of the depth camera were combined within the critical distance range, excluding the influence of background citrus on the detection speed. The number of tracking targets each time was limited to effectively improve the tracking speed of disturbed citrus. The experimental results show that the P (precision) and mAP (average detection accuracy) of improved YOLOv5s were improved by 3.9 and 1.1 percentage points, respectively, with a detection rate of 69.3 frames per second. The MOTA (Multi-Object Tracking Accuracy) and MOTP (Multi-Object Tracking Precision) of the improved DeepSORT were improved by 9.2 and 5.4 percentage points, respectively, whereas, the average number of ID (identity) switching times of targets was reduced by 32 times. Grasping experiments were conducted in the laboratory, in which the citrus was randomly swung along different orientations with an amplitude of about 10 cm. When the predicted localization time was 1, 2, 3, 5, 7, and 10 s, the average precision values of disturbed citrus localization were 21.3%, 53.0%, 81.9%, 83.7%, 86.1%, and 94.9%, respectively. The citrus picking test was conducted with the citrus localization time of 3 s. The average grabbing time for each citrus was 12.8 s, which was 5.6 s shorter than that without the optimization. The efficiency was improved by 30.4%. This finding can provide technical support and references for citrus picking in disturbed states. [ABSTRACT FROM AUTHOR]

Gender sorting silkworm pupae is a highly labor-intensive step in the agricultural industry nowadays. The existing sorting machine also suffers small applicable variety, low accuracy and low efficiency. Furthermore, gender sorting can depend mainly on the reliable difference in the gonad characteristics of male and female silkworm pupa. In this study, an online gonad image acquisition and automatic gender classification device was designed for silkworm pupae. The silkworm pupae were automatically flipped to capture the gonad feature image. Firstly, the vibration feeding, conveyor slide and rotation structures were analyzed using the physical parameters of silkworm pupae. The key influencing factors were determined in the automatic identification and sorting of male and female silkworm pupa: the spiral obliquity of vibration feeding, slideway obliquity and rotational speed. Then, the spiral obliquity of vibration feeding, slideway obliquity and rotational speed of silkworm pupae were optimized to improve the sorting speed under the same recognition and computer hardware conditions. Finally, an information fusion control system was designed using a laser and photoelectric sensor with SIEMENS-S7-200SMART-ST30 as the controller. The automatic opening and closing of the anti-congestion device were accurately controlled to realize the automatic moving and reset of the silkworm pupae pushing and receiving device, the automatic collection of image information, the automatic start and stop vibrating feeding system. The Box-Behnken test was carried out to improve the performance of the sorting device. The three-factor and three-level orthogonal experiments were conducted, in which the spiral obliquity, slideway obliquity, and rotational speed were taken as influencing factors, whereas, the average sorting time, sorting accuracy, and breakage rate of single silkworm pupa were as the response indexes. The results of variance and response surface showed that the best parameters were the spiral obliquity of 15.62°, slideway obliquity of 31.77° and rotational speed of 21.09 r/min, single silkworm pupae average sorting time of 5.13 s, sorting accuracy rate of 95.97%, and breakage rate of 0.07%. Under such optimal parameters, the prototype test showed that the sorting time, sorting accuracy, and breakage rate were 5.03 s, 96.47%, and 0.09%, respectively, which fully met the practical application requirements of the gender sorting for silkworm pupae. The developed device was achieved in the automatic feeding, online gonad image acquisition, automatic recognition and sorting of silkworm pupae, indicating the higher efficiency and accuracy of gender classification. Therefore, the finding can provide the theoretical basis and technical support to the automatic gender sorting device of silkworm pupae. It is of great significance in the automated breeding of the silkworm industry. [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.)

Navel orange is a very popular fruit in China, which is mainly cultivated along the Yangtze River. Navel oranges are classified into different grades based on external quality and internal quality before they are sold. Soluble solids content is one of the main indices for evaluating the internal quality of navel orange. Therefore, it is very important to improve the detection accuracy of soluble solids content in production. So far, visible and near infrared spectroscopy (Vis-NIR) is one of the most widely used and effective techniques in internal quality assessment of fruits. In this study, 199 Fukumoto navel oranges were taken as experimental samples. The transmission spectra of navel oranges of three positions including pedicle upwards (P1), pedicle horizontal (P2) and pedicle downward (P3) were acquired by using a special visible and near infrared transmission spectrum measurement system designed by ourselves. The average spectra (P4) and weighted spectra (P5) of P1, P2 and P3 were calculated. The transmission spectra, including P1, P2, P3, P4 and P5 were preprocessed by multivariate scattering correction, standard normal variate transformation, first derivative and second derivative respectively. The best pretreatment results were obtained based on first derivative after comparative study. Then the spectra data preprocessed by first derivative were divided into 30 to 50 intervals with step length of 5, and backward interval partial least squares was used to select the optimal band combination. Good results observed when P1, P2, P3, P4 and P5 were divided into 35, 40, 30, 35 and 40 intervals, in which 161, 180, 114, 308 and 170 variables were retained. On this basis, competitive adaptive re-weighted sampling (CARS) was used to select feature variables. After running CARS for 20 times in each selection, 24, 23, 18, 39 and 22 variables were kept respectively. Finally, Five PLS models were established, including P1-PLS, P2-PLS, P3-PLS, P4-PLS and P5-PLS. Among the P1-PLS, P2-PLS and P3-PLS models, P2-PLS model was the best one, as the value of correlation coefficients of prediction was 0.924 and the value of root mean square error of prediction was 0.404. This model can be realized by adjusting the navel oranges to pedicle horizontal in modeling. P4-PLS model and P5-PLS model had achieved good prediction results, as the value of correlation coefficients of prediction was higher than 0.91 and the value of root mean square error of prediction was lower than 0.43. P4-PLS model was based on the average spectra of P1, P2 and P3, and had potential to be realized by rolling the navel oranges in actual application. However, P5-PLS model was based on weighted spectra of P1, P2 and P3, which was difficult to realize in on-line detection. This study can provide a reference for the development of on-line detection equipment for the assessment of internal content of substances in navel orange. [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.)

In view of the poor adaptability of the existing automatic spraying device to citrus fruit trees with different crown size in hilly and mountainous areas, a bidirectional multi-mode automatic spraying device was development in this paper. The device was mainly composed of mechanical framework, high-pressure atomizing mechanism, air supply mechanism and automatic control system (which including power supply module, communication module, automatic target module, vehicle speed measurement module, motor drive module and solenoid valve). Changing the angle of spraying rod through the mode conversion mechanism, the vertical spraying mode, the 45° tilt spraying mode and the ground spraying mode were realized, which satisfied the adaptability requirements of the spraying device for citrus fruit trees of different sizes, and effectively improved the utilization of pesticides. Firstly, the canopy height of the fruit trees and the distance from the high pressure atomizing nozzle assembly to the crown surface were detected by the automatic target module composing of an ultrasonic sensor and a laser diode array, The ultrasonic sensor was responsible for detecting the top and bottom layers of the canopy, and guided the position adjustment of spraying rod and the on-off of solenoid value, so that the spraying rod could spray against the canopy; the laser diode array was responsible for detecting the distance between the spraying rod and the fruit trees, and guided the boom to adjust to the setting position. Secondly, the speed measuring module was used to detect the moving speed of the crawler-type walking tractors in real time. Thirdly, the collected datas were processed by the controller. The effects of the detection delay on the effect of the spraying were reduced by the cooperation of each module. The laboratory test results showed that the spray range and spray width of the device in 3 spray modes were in line with the quality requirements of plant protection machinery. The spray amount for one operation was 72-190 L/hm2, which met the requirements of the low dose spray. The ultrasonic sensor was arranged at 0.8m in front of the spraying rod, when the working speed of the crawler-type tractor was not more than 8 km/h, it effectively reduced the target error caused by the delay of signal detection and processing, and the target precision was 99.7%, which was in line with the design requirements. Orthogonal experimental design method was used in field experiments, the effects of vehicle speed, air supply intensity and sprinkler-to-canopy distance on spraying efficiency were investigated by establishing L9(34) orthogonal experimental design table. The results of spraying in vertical spraying mode and 45° tilt spraying mode showed that the average droplet coverage on the canopy surface was 82.5% and 78.7%, respectively, and the droplet deposition density was 109 and 106 droplets/cm2, respectively; the average droplet coverage in the canopy was 16.1% and 30.6%, respectively, and the droplet deposition density was 35 and 64 droplets/cm2, respectively. The spraying effects met the requirements of national standards. [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.)