The sorting of male and female silkworm pupae is an essential process of silkworm breeding, with its accuracy directly affecting the quality of hybrid silkworm eggs and silk. Gonadal characteristics serve as a reliable basis for sex identification in silkworm pupae; however, the gonads only exist on the positive side of the tail. Due to the unique geometry of silkworm pupae, online sex recognition based on machine vision requires flipping and taking many photos of the same silkworm pupae. Thus, accurately selecting the frontal image from multiple images of the same silkworm pupae in different poses is a prerequisite for subsequent sex identification. To address this challenge, we proposed SPNet-GS (Silkworm Pupae Network for Gonad Selection), a lightweight model for online selection of frontal silkworm pupae images. The model first employed a large kernel convolution to enhance the receptive field and capture the relevant information between adjacent pixels. Then the correlation between long-distance pixels under multi-scale information can be obtained by dilated convolutions. Finally, the correlation information between near and far pixels was fused to enhance feature extraction. Experimental results demonstrated that our method outperforms other models with an average accuracy of 98.41% and an average F1 score of 99.02%. The average inference time of each image was 0.03 s, which can fully meet the requirements of online selection of male and female silkworm pupae. Moreover, the gender identification accuracy rates using the selected frontal image and gonad region image reached 84.68% and 94.58%, respectively. These results were 10% and 19.90% higher than using multi-pose images for sex identification, demonstrating the effectiveness of the frontal image selection strategy. The findings of this investigation may provide a valuable reference for the machine vision-based intelligent online sorting of silkworm pupae by gender.

Citrus industry can greatly contribute to the agricultural production and rural development in China. However, the conventional fruit counting is confined to the numerous practical challenges in the orchard terrains of the hilly and mountainous. The densely distributed citrus fruits are often partially occluded by branches and leaves, thus difficult to accurately identify and count. Manual counting is also labor-intensive and time-consuming, highly susceptible to the human error, due to the operator fatigue, inconsistent lighting, and the variability in fruit visibility. Such inefficient management cannot fully meet the increasing demand for the modern, efficient, and intelligent agriculture, particularly with the continuous expansion of the orchard scale. In this study, an efficient and light-weight citrus fruit detection and counting model was proposed to balance the high-resolution images that captured by unmanned aerial vehicles (UAVs) and deep learning. The YOLOv8n object detection framework was improved the performance with a series of the targeted architecture under resource-constrained scenarios. Firstly, the GhostConv module was introduced as a lightweight alternative to the standard convolution. Simultaneously, the C3Ghost module was used to reconstruct the C2f feature extraction structure. The computational complexity was significantly reduced to lower the number of the parameters, floating-point operations, and inference time. The detection accuracy was maintained suitable for the deployment on the mobile or edge-computing devices, such as UAV platforms. Secondly, the citrus fruits were distinguished from the complex backgrounds. The Convolutional Block Attention Module (CBAM) was embedded within the neck of the network. This attention mechanism was improved on the key object features, in order to suppress the interference from the irrelevant background. In addition, the Wise Intersection over Union (WIoU) loss function was also replaced to accelerate the network convergence. The accuracy of the bounding box regression was enhanced the overall model performance. Furthermore, the robust and continuous tracking of the individual fruits was achieved across video frames. The Deep Simple Online and Real-time Tracking (DeepSORT) algorithm was integrated into the counting pipeline. Experimental results demonstrate that the GhostConv and C3Ghost modules were reduced the parameters, floating-point operations, and inference time by 42.8%, 37.0%, and 43.2%, respectively. The CBAM module was improved the precision and mean average precision (mAP) by 1.6 and 2.7 percentage points, respectively. The WIoU loss function was led to an additional increase in the precision, recall, and mAP by 1.2, 0.2, and 0.6 percentage points, respectively. Overall, the optimal GCW-YOLOv8n model was achieved in the high accuracy of the detection, with the increases of 4.2, 3.4, and 3.7 percentage points in the precision, recall, and mAP, respectively. The computational demands were shortened the inference time by 75.7%. In fruit counting tests, the better performance was achieved in a peak accuracy of 98.51% and an average accuracy of 95.57% in the offline video analysis. In online real-time counting experiments under varying weather conditions, the stable performance was obtained with the minimal fluctuation in the CA values, indicating its robustness and adaptability. This finding can also provide a reliable technical solution to the real-time estimation of the citrus yield in the intelligent orchard under complex hilly environments.

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.

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.