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BACKGROUND: MRI is important for the diagnosis of early knee osteoarthritis. MRI image recognition and intelligent segmentation of knee osteoarthritis using deep learning method is a hot topic in image diagnosis of artificial intelligence. OBJECTIVE: Through deep learning of MRI images of knee osteoarthritis, the segmentation of femur, tibia, patella, cartilage, meniscus, ligaments, muscles and effusion of knee can be automatically divided, and then volume of knee fluid and muscle content were measured. METHODS: 100 normal knee joints and 100 knee osteoarthritis patients were selected and randomly divided into training dataset (n=160), validation dataset (n=20), and test dataset (n=20) according to the ratio of 8:1:1. The Coarse-to-Fine sequential training method was used to train the 3D-UNET network deep learning model. A Coarse MRI segmentation model of the knee sagittal plane was trained first, and the rough segmentation results were used as a mask, and then the fine segmentation model was trained. The T1WI and T2WI images of the sagittal surface of the knee joint and the marking files of each structure were input, and DeepLab v3 was used to segment bone, cartilage, ligament, meniscus, muscle, and effusion of knee, and 3D reconstruction was finally displayed and automatic measurement results (muscle content and volume of knee fluid) were displayed to complete the deep learning application program. The MRI data of 26 normal subjects and 38 patients with knee osteoarthritis were screened for validation. RESULTS AND CONCLUSION: (1) The 26 normal subjects were selected, including 13 females and 13 males, with a mean age of (34.88±11.75) years old. The mean muscle content of the knee joint was (1 051 322.94±2 007 249.00) mL, the mean median was 631 165.21 mL, and the mean volume of effusion was (291.85±559.59) mL. The mean median was 0 mL. (2) There were 38 patients with knee osteoarthritis, including 30 females and 8 males. The mean age was (68.53±9.87) years old. The mean muscle content was (782 409.18±331 392.56) mL, the mean median was 689 105.66 mL, and the mean volume of effusion was (1 625.23±5 014.03) mL. The mean median was 178.72 mL. (3) There was no significant difference in muscle content between normal people and knee osteoarthritis patients. The volume of effusion in patients with knee osteoarthritis was higher than that in normal subjects, and the difference was significant (P < 0.05). (4) It is indicated that the intelligent segmentation of MRI images by deep learning can discard the defects of manual segmentation in the past. The more accuracy evaluation of knee osteoarthritis was necessary, and the image segmentation was processed more precisely in the future to improve the accuracy of the results. [ABSTRACT FROM AUTHOR]

Cotton is an important strategic material and one of our important economic crops. It plays an important role in our agricultural production, people's livelihood and secondary and tertiary industries. Cotton aphid is one of the major pests in the process of cotton growth and development. The lag and inaccuracy of aphid information will lead to the excessive application of pesticides, which will accelerate the destruction of cotton habitat and the pollution of cotton field environment. Therefore, it is of great practical significance to obtain the temporal and spatial distribution, hazard level and occurrence area of aphids in cotton fields in real time and accurately for the prevention and control of cotton aphid, cotton field management and farmland environmental protection. Remote sensing technology and machine vision technology speed up the acquisition of real-time and dynamic information and classification of aphids in cotton fields. This paper focused on "cotton aphid situation information monitoring", carried out literature review and combing at home and abroad, and described the research status and progress of traditional methods, remote sensing technology and image recognition on cotton aphid situation information. The problems in the monitoring of cotton aphid were discussed, and the research focus and development trend of the monitoring and diagnosis of cotton aphid damage information in the future were prospected. [ABSTRACT FROM AUTHOR]

Rice diseases and pests have posed a serious threat to agricultural production in recent years. Particularly, false and missed detections have been caused by the diversity, scale, and complex growth environments of these pests. This study aims to enhance the detection accuracy of rice diseases and pests, in order to facilitate the deployment of detection models on edge devices. An improved YOLOv8-based model was developed to detect rice diseases and pests. Several innovations were also incorporated to optimize the performance of the model. Firstly, partial convolution (PConv) was introduced to design the PCBlock structure and then integrated into the feature extraction module of YOLOv8. As such, the module of lightweight feature extraction known as C2f-PCBlock significantly reduced the number of model parameters, thereby enhancing the detection speed without compromising accuracy. The complexity of the model was then reduced to deploy on the resource- constrained edge devices. Secondly, the semantic information was diluted to cause the feature fusion across non-adjacent layers. A balanced Feature fusion layer was then added to the neck network. feature pyramid networks (FPN) was restructured into balanced feature pyramid (BFP). The preservation of critical features was improved during fusion. Additionally, the embedded Gaussian non-local attention (EGNA) mechanism was incorporated to mitigate the aliasing behavior from the multi- layer feature fusion. The important features were retained to reduce the loss of information, leading to more accurate detections. The loss function of the model was also enhanced to replace the standard IoU-based loss with MPDIOU. The distortions were avoided in the detection bounding boxes, due to the significant variations in sample sizes. MPDIoU was used to reduce these distortions for less computational burden, indicating more efficient training. Extensive experiments were conducted on a rice pest and disease dataset. The results demonstrate that the improved model, named PBM-YOLOv8, outperformed the rest. Specifically, PBM-YOLOV8 was achieved in the precision rate of 6.1, 8.0, 4.2, 1.3, and 1.9 percentage points higher than Fast R-CNN, SSD, YOLOV5n, YOLOv8n, and YOLOv9t, respectively. The recall rates were also significantly improved, with an increase of 7.1, 8.4, 5.6, 2.8, and 3.2 percentage points, respectively. Furthermore, the mean average precision (mAP) values were 5.7, 7.3, 3.8, 1.1, and 1.5 percentage points higher than those of the compared models, respectively. PBM-YOLOv8 was selected as a leading model, in terms of detection accuracy. Moreover, the PBM-YOLOv8 model was highly efficient with only 4.8M parameters, which was far less than the 41.3M of Fast R-CNN and the 15.6 M of SSD. A high processing speed was maintained with an FPS of about 92, in order to balance the accuracy with computational efficiency. The effectiveness of the improved model was further validated. PBM-YOLOv8 was deployed on the RK3588 embedded board. The deployment showed an accelerated detection speed of 71.4 frames per second, with an average accuracy of 95.1%. This performance can fully meet the demands of practical applications, thus enabling real-time and accurate detection of rice pests and diseases. The multi-thread optimization was adopted in the deployment. The parallel acceleration of multi-core NPU was also realized for the model inference and CPU for post-processing. The model has further enhanced the efficiency and feasibility for real-world use. [ABSTRACT FROM AUTHOR]

The crayfish industry, primarily focused on Procambarus clarkii, is expanding rapidly but encounters challenges due to inadequate automation. Traditional manual visual inspection methods used for evaluating crayfish size and integrity during breeding and processing are labor-intensive and prone to errors. This study presents an improved algorithm based on the YOLOv8n framework to intelligently recognize and grade crayfish by accurately detecting the body, tail, and claws of Procambarus clarkii. The proposed approach introduces innovation by replacing the original loss function CloU (Complete Intersection over Union) with MPDIoU (Modified Perfect Dark Intersection over Union). A novel scale factor, denoted as the ratio, has been introduced to adjust the size of the auxiliary bounding box within the loss calculation framework. This improvement, in conjunction with the MPDIoU loss function, notably enhances the accuracy and efficiency of bounding box regression. As a result, it enables the precise detection of the distinct body parts of crayfish, a pivotal advancement in automating the grading process. Empirical assessments demonstrated substantial enhancements in recognition accuracy. The incorporation of Inner-MPDIoU into the YOLOv8n model elevated the mean Average Precision (mAP) from 83.7% to 90.8% across loU thresholds ranging from 0.5 to 0.95. The results of this study highlight the effectiveness of the proposed algorithm in precisely recognizing critical elements of Procambarus clarkii. This investigation contributes to the overarching goal of attaining intelligent and accurate grading within the crayfish domain, potentially transforming conventional practices and enhancing industry productivity. The implications transcend mere automation, providing a groundwork for future exploration into intelligent systems tailored to the unique requirements of the crayfish industry. [ABSTRACT FROM AUTHOR]

Robotic harvesting had been constrained by the low positioning accuracy of strawberry stem picking points and the significant challenge of identifying occluded strawberries. In this study, we proposed an improved YOLO v8 model combined with Pose key-point detection for enhanced strawberry recognition and localization. The accuracy of picking point localization was also improved, especially for occluded strawberries in complex environments. To optimize the YOLO v8 model, we introduced the Bidirectional Feature Pyramid Network (BiFPN) and the Generalized Attention Module (GAM), which enhanced bidirectional information flow, dynamically allocated feature weights, and focused on extracting features of small targets and enhancing the features of occluded regions. As a result, the model's ability to accurately detect and localize strawberries in complex environments was significantly improved. Experimental results showed that the improved YOLO v8- pose model outperformed the original model in several metrics: the Precision (P) increased by 6. 01 percentage points, Recall (R) by 1. 98 percentage points, mean Average Precision (mAP) by 6. 67 percentage points, and mean Average Precision for key points (mAPkp) by 7. 85 percentage points. The positioning accuracy for strawberry stem picking points, based on key-point detection, achieved errors of just 1. 4 mm in both the X and Y directions and 2. 2 mm in the Z direction. Additionally, the occlusion level was classified according to the overlap area of occluded strawberries, and the model's performance under varying occlusion conditions was assessed. Under these conditions, the mAPkp of the improved YOLO v8-pose model increased by 9. 78 percentage points compared to the original model. Field trials further validated the model's effectiveness, with the strawberry-picking robot achieving a 95% success rate, picking each strawberry within 10 seconds. The high success rate and short picking time demonstrated the practicality of the model in real-world agricultural settings, indicating its high efficiency and accuracy. The improved YOLO v8 model with key-point detection accurately and robustly recognized strawberries, leveraged multi-scale features with the BiFPN architecture, and focused attention on relevant regions with the GAM, especially for occluded strawberries. These advancements significantly improved overall performance in precision, recall, and average precision, particularly under occlusion scenarios. In conclusion, these advanced techniques were integrated into a more capable strawberry-picking robot system. The enhanced accuracy and efficiency achieved in recognizing and localizing strawberries, even in challenging occlusion scenarios, highlighted the system's potential for practical agricultural applications. The findings contributed significantly to automated strawberry harvesting in agricultural robotics, paving the way for more efficient and cost-effective farming solutions in sustainable production. [ABSTRACT FROM AUTHOR]

: Accurate detection of immature green citrus fruits in trees is one of the most critical steps for production decisions, such as early yield prediction, precise water and fertilizer management, and regulation of number of fruits hanging. However, the large model has been confined to identifying the immature citrus, due to the similar green citrus to the canopy background. Great challenges have been brought to rapidly and accurately detect the fruits, even to deploy the model. In this study, an improved model, YOLO-GC (You Only Look Once-Green Citrus) was proposed to detect the green citrus fruits using YOLOv5s. The improved model was also deployed into the edge mobile devices, in order to achieve the real-time and convenient detection of green citrus fruits in trees. Firstly, the original backbone network was replaced with a lightweight GhostNet one, because the YOLOv5s network model was large and difficult to deploy. Meanwhile, the attention was then improved to the green citrus features. Accuracy degradation was reduced after the model was lightweight. Global Attention Mechanism (GAM) was embedded in the backbone network and the feature fusion layer, in order to extract the fruit features in complex environments. Secondly, a BiFPN (Bi-directional Feature Pyramid Network) architecture was introduced into the feature fusion layer for the multi-scale weighted feature fusion, in order to improve the dense and small-targeted fruits. Finally, the GIoU (Generalized Intersection over Union) loss function combined with Soft-NMS (Soft-Non-Maximum Suppression) was used to optimize the bounding box regression, in order to reduce the omission caused by the occlusion and overlapping of fruits and branches. The experimental results showed that the weight model memory of YOLO-GC was reduced by 53.9%, compared with the YOLOv5s. The number of parameters and the average inference time were reduced by 55.2% and 46.2%, respectively, whereas, the average precision (AP0.5) was improved by 1.2 percentage points. There was a lower amount of fruit leakage and misdetection in a variety of complex natural environments. The comprehensive performance of the YOLO-GC model was superior to that of CenterNet and seven commonly used network models, such as the YOLOv5s, YOLOv7, YOLOX, YOLOv8, CenterNet, Faster R-CNN, and RetinaNet target networks. The average accuracy of the YOLO-GC model was improved by 1.2, 1.3, 1.4, 0.9, 1.7, 4.6, and 3.9 percentage points, respectively, only 6.69 MB of weighted memory, thus achieving 97.6%, 90.3%, 97.8%, and 97.0% for the precision, recall, average precision, and F1 score, respectively. The YOLOGC model was then deployed to the Android mobile App for testing. The detection accuracy reached 97.2%, which was 2.4 percentage points higher than that of the YOLOv5s. Furthermore, the inference duration (103ms) was reduced by 85.8%. The IC model fully met the requirements of high-accuracy recognition and real-time inference of green citrus on the Android phone side. The finding can provide technical support to detect the green-like fruits in complex environments. The improved model was also deployed in the edge intelligent devices. [ABSTRACT FROM AUTHOR]

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Rice diseases and pests are one of the important factors affecting rice yield and quality. It is of great significance to strengthening the prevention and control of diseases and pests. The article elaborates on the development process of a rice pest detection and warning platform from three aspects of demand analysis, technical system architecture and functional modules. Through the use of surrounding environmental in- formation and image recognition technology, the rice pest detection and warning function was achieved, improving the efficiency of rice pest prevention and control. After analysis and research, it was found that there were still some problems with the rice pest detection and warning platform. In the future, in-depth research could be conducted from three aspects of enhancing the regional adaptability of the system, improving the recognition effect of small pests and constructing high-quality agricultural pest and disease datasets. [ABSTRACT FROM AUTHOR]

Automatic picking tea buds is ever increasing in the continuous expansion of famous brand tea market at present. However, manual picking cannot fully meet the needs of short-term concentrated harvesting in the large-scale tea production. Mechanical mass picking can also share the very short window period of famous brand tea. Therefore, it is the urgent demand to realize the intelligent and accurate picking of famous tea. Among them, visual identification has been confined to the small target of tea buds and the complex background under the natural environment in the field. Fortunately, Yolov5s-segment network model is suitable to identify tea buds, due to the easy deployment, simple integrated structure and real-time detection. In this study, a novel model was proposed to extract the contour and then locate the picking point of tea bud using improved Yolov5s-segment. The rapid recognition and accurate location of picking points were realized for the tea buds in large field scenes. Firstly, P2 micro-target detection layer was imported into the Neck and Head of the original network. The small targets detection of P3, P4 and P5 layers was achieved in the improved Yolov5s-segment network. Secondly, the contour features of tea bud were extracted in the natural lighting environment. CBAM (convolutional block attention module) was added into the end of Backbone of Yolov5s-segment network for the anti-interference of the improved model. Finally, the position coordinates of the bud stem were extracted, according to the bud contour. The picking points were located accurately for the single bud, one bud with one leaf, and one bud with two leaves. Furthermore, the micro target detection layer and CBAM attention mechanism were gradually added into the original Yolov5s-segment model. In ablation test, the CBAM attention mechanism was replaced with SE, ECA and SimAM. After that, the optimal model was compared with Yolov8s-segment and Yolact. Finally, the positioning test was carried out using the optimal model. The results showed that the best performance was achieved in the model with the micro-target detection layer and CBAM attention mechanism. Compared with the original Yolov5s-segment model, the accuracy, recall rate, F1 score, mean average precision mAP50 and mAP50-95 were improved by 7.0, 8.9, 8.1, 8.3 and 7.3 percentage points, respectively. In the comparison test, the mAP50 of the improved model increased by 4.2 and 9.5 percentage points, respectively, compared with the Yolov8s-segment and Yolact. The positioning test showed that the pixel coordinates of picking points were accurately obtained in the 0° and 45° shooting angles of camera under different states, indicating the high positioning accuracy. The improved model can be used to accurately extract the outline of single bud, one bud with one leaf and one bud with two leaves in large field scenes. The precise location of picking points can be realized at the same time. The findings can provide a theoretical basis for intelligent and rapid picking of famous tea. [ABSTRACT FROM AUTHOR]

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Safflower is one of the most important cash crops in China. Its production area is concentrated in Xinjiang, Gansu and Ningxia. However, the harvesting of safflower relies mainly on manual labour at present. Particularly, the operating environment is easily affected by weather factors. Fortunately, intelligent harvesting can be expected to improve the efficiency of safflower harvesting with labour cost savings. Previous research often focused on pneumatic, pulling, combing, and cutting harvesting. However, it is still required for manual work during harvesting. The autonomous operation can be realized by combining target detection and navigation in the harvesting robots. However, the complex working environment in the field has limited the accurate recognition and localization in the harvesting process. This study aims to promote the performance of safflower recognition under the complex environment in the field during intelligent harvesting. A lightweight safflower recognition was also proposed using an improved YOLOv8n. The computational resources of the device were then deployed to the model on the mobile for detection. A dataset of 2309 images was created to categorize into two classes: picked and no picked. The safflower blooming was categorized into four stages, namely the bud, first flowering, prime bloom, and septum stage. The prime bloom stage was the best picking time in the most economically beneficial period of safflower. Therefore, the safflower only in the prime bloom stage was picked rather than the bud, first flowering, and septum stage. The improvement procedures were as follows. Firstly, the Vanillanet lightweight network structure was applied to substitute for the Backbone of YOLOv8n, in order to reduce the complex structure of the model. Secondly, the large separable kernel attention (LSKA) module was introduced into the Neck, in order to reduce the amount of storage and computational resource consumption. Thirdly, The YOLOv8n's loss function was revised from the center intersection of union (CIoU) to the wise intersection of union (WIoU), in order to improve the overall performance of the detector. Finally, the stochastic gradient descent (SGD) was chosen to train the model for robustness. The experimental results showed that the frames per second (FPS) of the improved lightweight model increased by 7.41%, while the weight file was only 50.17% of the original one. The precision (P) and the mean average precision (mAP) values reached 93.10% and 96.40%, respectively. Furthermore, the FPS was improved by 25.93% and 19.76%, the weight file was reduced by 21.90% and 25.86%, respectively, compared with the YOLOv5s and YOLOv7-tiny models. Meanwhile, better robustness was achieved in the improved model. The Jetson Orin NX flatform was then selected to deploy for testing. The single-image detection time of YOLOv8n and YOLOv8n-VLWS was 0.38s and 0.27s, which was 28.95% shorter than the original model. The high precision and lightweight of real-time detection was realized for the safflower in the field. The findings can provide the technical support to develop intelligent harvesting equipment for safflower. [ABSTRACT FROM AUTHOR]

Pak choi (Chinese cabbage) has been one of the most popular vegetables with a wide planting area in China. Rapid detection and accurate identification of Pak choi insect infestation is of great significance to ensure the safety of vegetable supply. However, the insect pests can often appear in specific areas of leaves under the real vegetable field environment. The relatively large light and background interference posed a great challenge to the detection efficiency and accuracy. In this study, an improved YOLOPC model was proposed to identify Pak choi pests using the YOLOv5s network framework. Firstly, the CBAM (Convolutional Block Attention Modul) was introduced to place on the input end of CBS (Convolution layer + normalization layer Batch normalization layer + activation function layer SILU). The structure of CBAM-CBS was formed to dynamically adjust the weights of each channel and spatial position in the feature graph. The upsample and 1×1 convolution operations were used to adjust the size and number of channels of the feature graph, in order to realize the fusion of features at different levels. The feature representation of the model was enhanced at the same time. The loss function was improved more suitable for the accuracy of bounding box regression. The void convolution was used to improve the receptive field range of the network, in order to better learn the context information of the image. Specifically, the improvement strategy included the following three aspects: 1) The attention mechanism of space and channel was added to extract the network feature, in order to better learn the cabbage diamondback moth and leaf miner insect pests; 2) The alpha-IoU loss function was used to replace the CIoU one in YOLOv5s. Different levels of boundary box regression accuracy were adapted for the insect targets of cabbage, broccoli moth, and leaf leaf-divers at different scales and aspect ratios; 3) Atrous Spatial Pyramid Pooling (ASPP) was introduced to improve the receptive field range of the network, in order to better learn the context information of the image. The test results showed that the mean average precision (mAP) of the YOLOPC model was 91.4% with an increase of 12.9 percentage points, compared with the improved YOLOv5s model. The frame per second (FPS) was 58.82 frames/s, indicating an increase of 11.2 frames/s, or 23.53%. The number of parameters was only 14.4M with a decrease of 5M, or 25.78%. Furthermore, the average accuracy of the YOLOPC model was 20.1, 24.6, 14, 13.4, and 13.3 percentage points higher, respectively, Compared with the target detection of SSD, Faster R-CNN, YOLOv3, YOLOv7, and YOLOv8. Significant advantages were achieved in the accuracy, recall rate, frame rate, and number of parameters. This improved model can provide technical support for the rapid and accurate detection of Pak choi pests under a complex background. In short, the accuracy and immediacy of the improved model were significantly optimized in the field of pest detection of Pak choi. YOLOPC model also shared significantly improved parameters, detection rate, and accuracy, compared with the current mainstream ones. Therefore, the camera equipment can be deployed to identify and warn the pest of Pak choi for field planting. The improved mode can also be applied on the plant protection drone, in order to realize the precise spraying of variables and targeted pest control. The findings can provide a strong reference to saving pesticides and effectively reducing environmental pollution. [ABSTRACT FROM AUTHOR]

In few-shot learning, the Brownian distance covariance improves classification accuracy by enhancing feature embeddings, but it does not focus on the issue of sample-related features in classification. This paper proposed the deep masked Brownian distance covariance method that generated query-guided masks based on high-dimensional semantic relationships between each pair of query and support samples, and employed the masked Brownian distance covariance matrix as the image features. Under the 5way-1shot and 5way-5shot scenarios, it carried out validation and evaluation on CUB-200-211, MiniImageNet, and Tiered-ImageNet datasets. The experiments show that this method achieves superior classification accuracy. [ABSTRACT FROM AUTHOR]

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Premium teas are among the most important types of tea in the market, renowned for their high quality and reputation, and consisting of tender tea shoots. In recent years, manual picking has been unable to meet the large-scale production of premium teas due to the high cost and inefficient labor. On the other hand, traditional mechanical equipment tends to damage the tea quality easily. It is very necessary to develop intelligent and large-scale picking, in order to reduce labor costs. Deep learning models represented by YOLOv8n are effective tools for achieving accurate identification of tea shoots. However, YOLOv8n can still face some challenges, in terms of detection accuracy and the imbalance between detection accuracy and speed in actual tea shoot detection tasks. This study aims to detect the tea shoots using the improved YOLOv8n. The specific procedures were as follows. Firstly, one-bud and one-bud-one-leaf tea shoots were selected as the detection targets according to the picking standards for premium teas. Secondly, images of tea shoots were collected from various pitch angles and horizontal orientations and then underwent image processing. Data augmentation was applied, such as random cropping, linear transformation, and brightness adjustment. The dataset of tea shoots was obtained with 2215 images and 3227 detection targets. Finally, the YOLOv8n model was improved using the following modifications: 1) The dynamic snake convolution (DSConv) was introduced to the backbone network, in order to capture some morphological characteristics of tea shoots; 2) The path aggregation network (PANet) was replaced in the neck network with a weighted bi-directional feature pyramid network (BiFPN) to strengthen the feature fusion efficiency; 3) The simple attention module (SimAM) was added after each C2F module in the neck network, in order to improve the focus on recognizing tea shoots. An ablation experiment was designed to verify the effectiveness of the improved model, and a comparative experiment was conducted to demonstrate the advantages of the modified model. The results indicate that all three modifications were enhanced the detection accuracy of the model. Specifically, compared with the original model, the addition of the SimAM module increased the recall rate (R) by 2.4 percentage points, indicating effective attention to small tea shoots. The addition of the DSConv increased the precision (P) by 1.1 percentage points, indicating that it is effective in capturing the morphological characteristics of tea shoots. The introduction of the BiFPN enhanced the precision and mean average precision (mAP) by 1.6 and 2.1 percentage points respectively, with the recall rate by 1.7 percentage points, indicating a more efficient feature fusion than the PANet. The final improved model outperforms the original model by 4.2, 2.9, 3.7, and 3.3 percentage points in accuracy, recall, mean average precision, and F1 score respectively. The detection speed reaches 42 frames per second, achieving a balance between detection accuracy and speed. Meanwhile, the model size is 6.7 MB, which fully meets the deployment requirements for low-power mobile devices. Compared with other object detection models, such as Faster-RCNN, YOLOv5n, YOLOv7n, and YOLOv8n, the improved YOLOv8n model exhibited higher precision, recall, mean average precision, and F1 score, outperforming them by 57.4, 4.4, 4.7, 4.2 percentage points in precision respectively; 53.0, 3.6, 2.8, 2.9 percentage points in recall respectively; 58.9, 5.0, 4.6, 3.7 percentage points in mean average precision respectively; and 56.8, 3.9, 3.7, 3.3 percentage points in F1 score respectively. The higher accuracy and lower missed detection of the improved YOLOv8n model further confirmed its superiority in tea shoot detection tasks. Meanwhile, its moderate detection speed and model size were also suitable for application in low-power mobile devices during intelligent tea picking. The improved YOLOv8n model can lay the foundation for the intelligent picking of premium teas. [ABSTRACT FROM AUTHOR]

Fragrant pears have been one of the most favorite types in pear industry. However, manual picking cannot fully meet the demands of large-scale production, due to the labor intensity. Mechanical picking has the potential to significantly reduce the labor demands for the high productivity. Among them, the visual recognition has been one of the most key elements among the various picking robots. The complex orchard environment with the variable lighting, weather conditions, foliage obstructions and fruit overlap has also posed the significant challenges on both conventional image processing and machine learning-based object detection, thereby impeding the accurate identification of fragrant pears. This study aims to improve the accuracy and detection speeds in such unstructured settings. An optimized YOLOv8n-based object detection was introduced to specifically identify the fragrant pear. A dataset was also comprised of 4 500 images of fragrant pears. A variety of conditions were considered using image collection and data augmentation, such as different lighting and foliage obstructions within orchard environments. The Min-Max normalization was employed to consider the fragrant pear harvesting. A comprehensive evaluation was conducted on five lightweight YOLO versions: YOLOv3-tiny, YOLOv5n, YOLO6n, YOLOv7-tiny, and YOLOv8n. Different weights were assigned to optimize the model selection, in terms of the mAP, precision, recall, inference time, parameters and model size. The network structure was streamlined for the object detection of fragrant pear with YOLOv8n as the baseline. Four key areas were focused to optimize the model. Initially, the network backbone was redesigned to promote the network inference speed. The impact of the C2f structure on speed was assessed to replace the certain redundant C2f components. Subsequently, an effective PConv module was integrated into the detection head of network, in order to better recognize the obscured images. A weight-sharing strategy was also coupled with the detection head parameters to reduce the parameters. Additionally, the combination of simSPPF and Inner IoU was further augmented the inference speed and bounding box regression performance. Comparative trials were then conducted on the object detection of fragrant pear. The results revealed that the superior comprehensive performance was achieved in the YOLOv8n. A higher weighted score of 83.4% was obtained after the Min-Max normalization, compared with the YOLOv3-tiny, YOLOv5n, YOLOv6n, and YOLOv7-tiny. Thus, the baseline network was selected for the subsequent research. Optimization experiments were carried out with C2f structures. The replacement of C2f was identified in the 15th and 2nd layers of the network with Conv and Conv_Res modules, in order to enhance the computational efficiency. The optimization scheme was determined for the backbone network. Ablation experiments were conducted to verify the efficacy of the various improved modules. The refined YOLOv8n was achieved the superior accuracy and detection speed on the fragrant pear dataset, with a 0.4 and 0.5 percentage point increase in the F0.5 score and mean average precision, respectively, compared with the original YOLOv8n model. Detection speeds on GPU and CPU devices increased by 34.0% and 24.4%, respectively, indicating the rates of 99.4 and 15.3 frames per second, respectively. This high-precision and rapid detection can provide the valuable technical support to the real-time detection of fragrant pears in natural orchard environments. The improved model can also be expected to deploy into the fragrant pear picking robots. [ABSTRACT FROM AUTHOR]

Individual tree detection of bayberry trees can greatly contribute to the precise management, accurate prediction of yield, scientific irrigation and pest control. However, most of the commonly-used traditional techniques (such as remote sensing images) are limited to the digital development of orchards at present, due to the cumbersome process and low efficiency. This study aims to rapidly detect and count the number of bayberry trees using deep learning. An improved YOLOv7-ACGDmix model was proposed to detect the individual tree using the You Only Look Once version 7 (YOLOv7) model. Firstly, the Extended-Efficient Long-Range Attention Networks module of YOLOv7 was improved to integrate the mixed model. Both convolution and attention mechanisms modules (AC-E-ELAN, self-Attention and Convolution extendedefficient long-range attention networks) were obtained to enhance the learning and reasoning of the original model. The accuracy of model was achieved to recognize the bayberry tree individuals with blurred or heavily occluded boundaries in dense scenes; Secondly, the deformable convolutional networks version 2 was added into the E-ELAN (extended-efficient longrange attention networks) module. The DCNv2-E-ElAN (deformable convolutional networks version 2 extended-efficient longrange attention networks) module was then obtained to detect the features of different sizes, especially small features and intensive situations; Thirdly, the Content-Aware ReAssembly of Features upsampling operator was used to reduce the feature information loss of the input image in the network sensory field; Fourthly, the global attention mechanism was introduced to reduce the interference of complex background on the model. The reasonable filling was realized to correct the feature information, in order to improve the detection performance of the model; Finally, the Wise Intersection over Union loss function was used to reduce the competitiveness of the high-quality anchor box and the harmful gradient that generated by lowquality samples. The overall performance of the neural network was improved after that. A series of experiments were conducted on an open dataset. A total of 611 images were divided into a training set, validation set, and test set, according to 8:1:1. The test set was further divided into simple, complex and special scenes to test the robustness and generalization of the improved model. A comparison was then made with the mainstream models, respectively. The experimental results showed that the YOLOv7-ACGDmix model was significantly improved the leakage detection, compared with the original network. The precision rate, the recall rate, the mean average precision, and the F1 -score were 89.1%, 89.0%, 95.1% and 89.0%, respectively, which were 1.8, 4.0, 2.3, and 3.0 percentage points higher than the original YOLOv7 model, respectively. The mean average precision of improved model was improved by 9.8, 2.2, 0.7, and 2.3 percentage points, compared with the Faster R-CNN, SSD, YOLOv8, and the original YOLOv7 model, respectively. Finally, a field-collected dataset was tested to verify the performance. In summary, this finding can provide an effective solution to detect the individual bayberry trees using unmanned aerial vehicle imagery in the precision management of orchards. [ABSTRACT FROM AUTHOR]

Copyright of South China Fisheries Science is the property of South China Fisheries Science Editorial Department 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.)

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Copyright of Urban Architecture & Development is the property of Omniscient Pte. Ltd. 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 Electronic Communication & Computer Science is the property of Omniscient Pte. Ltd. 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.)

To improve the operational safety of rail transportation equipment for mountainous transmission lines in forest areas, the safety detection system of rail transportation equipment based on image recognition was established. The electronic control system of rail transportation equipment for mountainous transmission lines in the forest areas was provided, and the various sensors used in the perception module system were introduced. The Faster-RCNN algorithm based on the combination of split-attention networks and self-calibration convolutions was applied to obtain better feature extraction, and the improved Faster- RCNN algorithm was used for the surrounding personnel recognition experiments. The remote control software of rail transportation equipment for transmission lines was developed based on QT, and the remote control of the equipment was realized. The results show that the improved Faster-RCNN algorithm can significantly improve the accuracy of identifying personnel around equipment in strong lighting and complex environments of forest areas. The mean average precision of image recognition can reach 87.13%, which is 74.35% higher than conventional Faster-RCNN and 76.28% higher than cascaded Faster-RCNN. The results fully prove that the improved Faster-RCNN algorithm has excellent recognition ability and ensures the safe operation of railway transportation equipment for mountainous transmission lines in forest areas. [ABSTRACT FROM AUTHOR]

Copyright of Hydrogeology & Engineering Geology / Shuiwendizhi Gongchengdizhi is the property of Hydrogeology & Engineering Geology Editorial Office 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.)

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Copyright of Wool Textile Journal is the property of National Wool Textile Science & Technology Information Center 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.)

An automatic, rapid, and accurate detection is required to monitor the pest in the large-scale areas in the field. In this study, the YOLOv5 (you only look once version five) model was used to detect crop pests. The existing YOLOv5 was incorporated the the global response normalization attention mechanism (YOLOv5-GRNS). An accurate detection was realized for the targets on images with complex backgrounds and excessive pest density. The improved model also converged rapidly during training. Firstly, the Global Response Normalization (GRN) operation was introduced into the encoder module, named Convolution Three (C3) which incorporated the GRN attention mechanism. The C3 module was used to exchange the channel information for less background interference at the channel level, thereby improving the detection accuracy of dense targets. Secondly, the Shape Intersection over Union (SIoU) loss function was utilized to improve the convergence speed and detection accuracy of the improved model. Besides, 8 types of pests that harm major crops in Shaanxi Province were screened out, according to the public dataset IP102 (insect pests 102). Then the dataset was revised and expanded to obtain a new dataset, named IP8-CW (insect pests eight for corn and wheat). Extensive experiments of the YOLOv5-GRNS model were conducted on both the new IP8-CW dataset and the existing IP102 dataset. The mean average precision (mAP) was achieved at 72.3% with mAP@0.5 and 47.0% with mAP@0.5:0.95 in the IP8-CW dataset. The YOLOv5-GRNS model increased by 1.3% and 1.6%, respectively, compared with the standard YOLOv5. The best performance was also achieved in the larger IP102 dataset with a 96-class classification task, indicating the lower complexity and fewer parameters. Ablation experiments were then conducted on the IP8-CW dataset to explore the influence of different factors on YOLOv5-GRNS performance. The results showed that a more regular path was achieved in the prediction box fitting the ground truth box using the improved model with the SIoU loss function. Thus the convergence rate of 30 epochs was promoted, compared with the rest two loss functions. The performance of the improved model was significantly improved in the sandwich structure using the GRN operation as the normalization and the channel attention layer. Furthermore, the performance was also higher than that of the structure, where the GRN was only one of them. The ablation experiment showed that there was only a little improvement in the YOLOv5- standard models with the rest attention mechanisms. The improved model with GRN operation was achieved in the best detection performance with the lowest model complexity and minimum parameters. Class Activation Maps (CAM) used the heat maps to mark the key locations that the model focused on in red. This feature of CAM was used to verify the effectiveness of the improved attention mechanism on the YOLOv5-GRNS model. Three datasets showed that the YOLOv5-GRNS model was concentratedly and accurately focused on the target area, rather than the complex background or dense targets. In summary, the YOLOv5-GRNS can be expected to serve as a robust response in the field of pest detection. Moreover, the excellent performance of the YOLOv5-GRNS model was also verified in the detection of small and dense targets on different datasets, indicating better generalization and interpretability with fewer parameters and less computational complexity. The improved model can also be applied to the embedded devices and mobile devices. [ABSTRACT FROM AUTHOR]

Automated harvesting holds the promising potential in the informatization and automation of smart agriculture. Manual harvesting can be replaced to improve the high efficiency and cost saving. However, the existing models are limited to the large number of overlapping citrus fruits, branch and leaf occlusion in the complex environments, particularly for the much more parameters and high computational complexity. In this study, an improved YOLOv8-MEIN model was proposed for the citrus recognition using YOLOv8n. Firstly, the more efficient (ME) convolution module was designed to improve the CSPDarknet53 into 2-Stage feature pyramid network (C2f) module of YOLOv8n. The high number of common convolutional parameters and computation were reduced to fully meet the requirements of the real-time detection, due to the conditions of mobile hardware performance. The overall computation was also reduced to decrease the number of parameters in the model. Secondly, the Inner- complete intersection over union (CIoU) loss function was used to accelerate the bounding box regression for the better performance of the model. The weak generalization and slow convergence of the CIoU loss function were also compensated in the detection task. The reason was that the CIoU loss function only considered the shape loss in original YOLOv8, thus degenerating to the IoU loss function, when the target and bounding box were at the same aspect ratio. Finally, the YOLOv8-MEIN model was validated for the citrus detection. The YOLOv5s, YOLOv7-tiny, Faster R-CNN, and YOLOv8n models were selected for the training and testing on the citrus dataset. A comparison was carried out on the self-built citrus dataset under the same number of iteration rounds, in order to verify the performance of the YOLOv8-MEIN model. The YOLOv8-MEIN was improved 5.85 percentage points in the recall, 14.3 percentage points in the mean average precision (mAP) mAP0.5 metrics, and 34.8 percentage points in mAP0.5~0.95 metrics, compared with the two-stage Faster R-CNN, where the model size was only 5.1% of Faster R-CNN. Compared with the lightweight networks of YOLO series, such as YOLOv5s, YOLOv7-tiny, and YOLOv8n, the number of parameters was reduced by 59.1%, 52.3%, and 4.4%, respectively, whereas, the mAP0.5~0.95 values were improved by 0.7, 4.0, and 0.6 percentage points, respectively, indicating the outstanding mAP0.5. The experimental results show that the YOLOv8-MEIN model was achieved in the higher detection accuracy and efficiency with the smallest model volume and the lower computational cost. More convenient model was found for the transplantation and application suitable for the real-time target detection of citrus. The size of model and the number of parameters were reduced by 3.3 and 4.3 percentage points, respectively, compared with the mainstream target detections. The improved model was fully met the requirements for the fast and accurate recognition of citrus fruits in the complex environments. The finding can provide the technical references for the automated harvesting of citrus fruits. [ABSTRACT FROM AUTHOR]

In view of the problems of low matching rate and long running time of traditional image recognition algorithms, an improved ORB-FLANN(Oriented FAST and Rotated BRIEF-Fast Library for Approximate Nearest Neighbors) based workpiece image recognition method is proposed. The feature description of ORB algorithm and image feature matching algorithm are modified to solve the disadvantages of traditional image recognition algorithm in the case of scale and rotation transformation and reduce the mismatching rate of matching. For the feature points detected by ORB algorithm, SURF (Speeded Up Robust Features) algorithm is used to add orientation information and complete the feature description, so as to obtain the feature points with rotation-scale invariance. FLANN algorithm is combined with bidirectional matching strategy for coarse matching of feature points. Finally, the progressive sampling-congruence algorithm is used to further eliminate the mismatched point pairs and complete the fine matching. The experimental results show that compared with other methods, the improved algorithm can improve the matching accuracy of 2.6%~18.8% and 29.5%~43.9%, respectively, when processing scale and rotation transform images, and the running time is within 4s, improving the efficiency and accuracy of workpiece image recognition. [ABSTRACT FROM AUTHOR]

view of the large amount of garbage and the fact that a picture contains multiple garbage objects, this study proposes a garbage detection and classification algorithm based on the improved MobileNet network, which integrates the MobileNet network into YOLOv5(You Only Look Oncev5) target detection algorithm. At the same time, the CBAM(Convolutional Block Attention Modul) module is introduced in the backbone to filter meaningful information, and the vision transformer is used to aggregate and form image features. In addition, the weighted bidirectional feature pyramid network is used to distinguish the contribution of different features. At the same time, the ECA(Efficient Channel Attention) module is introduced to combine the image features and transmit them to the prediction layer. Finally, in order to obtain better performance when there is occlusion between garbage targets, soft-NMS(soft-Non Maximum Suppression) method and Alpha-IoU(Alpha-Intersection over Union) loss function is used to predict the extracted features. The experimental results show that the method proposed in this study can realize the location and recognition of multi-target and multi-category garbage., and the mAP(mean Average Percision) value reaches 90.31%, which is 4.95% higher than that of YOLOv5 network, and the processing speed is shortened by about 2.4 seconds. Compared with the Faster R-CNN(Region-based Convolutional Neural Network) algorithm which integrates ResNet(Residual Network) network, the algorithm proposed in this study improves the processing efficiency on the premise of ensuring the accuracy. [ABSTRACT FROM AUTHOR]

Manual picking cannot fully meet the large-scale apple harvesting at present, due to the high labor intensity and cost with low efficiency. Fruit picking robot has drawn much attention in recent years, in order to realize the automatic picking and yield estimation. Among them, the vision system can dominate the efficiency and stability of the picking robot. It is required for the high speed and accuracy of fruit recognition under complex natural environments. Therefore, the vision system can be expected to accurately recognize the fruits on the tree. This study aims to identify and locate the apples in natural environments, particularly on the interference factors, such as blocked fruits, ambient light, viewing angle distance. Apple was also taken as the test object. Traditional vision was improved to accurately segment the contour of target fruit. Instance segmentation and localization were proposed using the improved BlendMask model. The original backbone network was replaced with the high-resolution network, HRNet (High-Resolution Net), in order to alleviate the decreasing resolution of feature maps in deep networks. Convolutional block attention mechanism (CBAM) was also introduced in the fusion mask layer of the instance segmentation model. The instance mask was thus improved the instance segmentation. Ablation experiments were carried out to verify a variety of popular instance segmentation backbone networks. HRNet was selected as the backbone network. BlendMask model was used to achieve the better performance balance between real-time and segmentation accuracy. At the same time, fruit recognition and localization were implemented to consider the recognition accuracy in real time. Therefore, the improved model was suitable for the fruit target recognition and localization. Instance segmentation was designed to efficiently extract the surface point cloud of the instance. The instance mask was matched with the depth map, in order to obtain the 3D surface point cloud of apple target instance. The tangential and outlier noises were removed in the point cloud using uniform downsampling and statistical filtering. Then the center coordinates of the apples were estimated in the 3D space using the leastsquares method (LSM). The center of target localization was achieved in the form of linearization of spherical equations. Other geometric indicators were also be used in the localization framework to realize the center localization of different kinds of fruits. The experimental results show that the average segmentation accuracy of the improved BlendMask model was 96.65%, and the detection speed reached 34.51 frames/s. The average segmentation accuracy of improved BlendMask model was 96.65%. The accuracy, recall and average accuracy were improved by 5.48%, 1.25% and 6.59%, respectively; Compared with the current new instance segmentation models, SparseInst, FastInst and PatchDCT, the average accuracy of the model was slightly lagged behind by 0.29%, 0.04% and 1.94%, respectively, whereas, the detection speed was ahead by 6.11%, 3% and 3%, respectively. The detection speed was 6.11, 3.84 and 20.08 frames/s ahead, respectively. The improved BlendMask model shared the high segmentation accuracy in real time. The finding can provide a technical solution to the vision system in apple-picking robots. [ABSTRACT FROM AUTHOR]

Copyright of Fly Ash Comprehensive Utilization is the property of Hebei Fly Ash Comprehensive Utilization Magazine Co., Ltd. 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 Micronanoelectronic Technology is the property of Micronanoelectronic Technology Editorial Office 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.)

A root is one of the most important vegetative organs of plants. It is of great significance to explore the growth status of root under different water stress in modern agriculture. However, the existing observation of plant root are cumbersome, laborious and time-consuming. It is the high demand to meet the requirements of precision irrigation and water-saving under field conditions. In this study, a pipeline robot system was developed for the synchronous, in-situ and the high-throughput monitoring of plant root and root soil water using STM32. The system consisted of pipeline robot, data base station and PVC transparent pipeline. The pipeline robot system was embedded in the soil, where the macro camera and soil moisture sensor were carried by the robot. The root images were captured to obtain the soil moisture data while the robot cruising. Meanwhile, the robot shared the functions of autonomous timing cruise, wireless communication using command data and active return after encountering obstacles. The distorted correction, registered on plane, identified and segmented images were obtained for the parameters of the root area, length and density of the plants in the direction of the pipeline. The results of laboratory test show that: 1) The pipeline robot was clearly captured the root images, indicating the excellent performance on distortion correction and plane registration. The true length and area corresponding to a single pixel were 44 μm and 0.002 mm², respectively, whereas, the shooting range of a single root image was 14.17×10.60 mm. The characteristic information of root was obtained using MATLAB, where the images taken by the automatic cruise of pipeline robot. The image processing operations included the image distortion correction, image preprocessing, root region recognition and segmentation, and root feature extraction. Compared with the root characteristic parameters measured by the excavation, the relative errors of the root image processing program were 12.29%, 3.40% and 12.50%, respectively; 2) There was an excellent linear relationship between the output voltage of the soil moisture sensor that carried by the pipeline robot and the soil volumetric moisture content, where the coefficient of determination was 0.990; 3) The pipeline robot presented a high accuracy of autonomous cruise positioning, with a mean relative error of 1.47%. The field experiment show that: 1) The pipeline robot system was captured the plant root images with high-throughput in the field environment. Root growth dynamics was obtained to further extract the parameters of root length, area, average diameter and density from the images; 2) The pipeline robot was accurately monitor the soil moisture in the root zone, where the mean relative error of the measured was 2.23%, compared with the drying measurement; 3) Once the system was initially fully charged, the pipeline robot system operated independently for no less than 7 days, where the maximum cruise monitoring distance was about 48m. The pipeline robot system can be expected to realize insitu and high-throughput measurement of plant root and soil moisture in the root zone under field environment. The growth of root can be extracted after image recognition and segmentation. The finding can also provide the technical support to monitor the growth status of root in water-saving irrigation. [ABSTRACT FROM AUTHOR]

An accurate detection can greatly contribute to the wheat ears in field environments. Traditional object detection models with horizontal bounding boxes cannot accurately detect the densely distributed wheat ears, particularly on the significant occlusion between ears and stalks. The high miss detection of wheat ears often occurs in the variation of illumination conditions, dense distribution, and small scales, due to the overlap of prediction bounding boxes. It is a high demand to orient the wheat ears with less noise and of large background for the high performance. In this study, an improved Oriented Region-based Convolution Neural Networks (R-CNN) model was proposed to detect and count rotated wheat ears. Firstly, the spatial pyramid pooling cross-stage partial networks (SPPCSPC) was added to the backbone network to generate the last layer of the output feature map. The sensing field was then enlarged to enhance the perceptual ability of the network; Secondly, the feature aggregation network and the efficient two convolutional block attention module (E2CBAM) hybrid attention mechanism module were introduced into the neck network to enrich the feature information in the feature map; Finally, the prediction bounding boxes were optimized using the flexible non-maximal inhibition algorithm soft-non maximum suppression (Soft-NMS), in order to optimize the predicted bounding boxes screening. The E2CBAM module was improved using the convolutional block attention module (CBAM) in the E2CA module, instead of the CAM channel attention module. The E2CA module was composed of two parallel ECA branch structures: the maximum and average pooling. Two adaptive convolution kernels were then obtained to sum. Finally, the channel assignment was weighted for the important channel information. The key feature was captured to improve the detection performance of the model. To verify the E2CBAM hybrid attention module, the path aggregation network (PANet) was introduced into the neck network to enrich the semantic and target location in the feature map. The detection accuracy of the model was then improved by 0.19 percentage points. Furthermore, the detection accuracy was improved by 0.16 and 0.31 percentage points, whereas, the number of parameters increased by 0.24 and 0.20 M. respectively, in the CBAM and E2CBAM hybrid attention mechanism module. The floating-point computation remained unchanged. Compared with the CBAM, the E2CBAM hybrid attention mechanism module improved the detection accuracy of the model by 0.15 percentage points, while reducing the number of parameters by 0.04 M with the unchanged computation. The experimental results show that the improved Oriented R-CNN model accurately represented the head direction of wheat ears, indicating better detection performance. The mean mAP of average accuracy was 2.02 percentage points higher than the original model, compared with the mainstream-oriented bounding boxes detection models. Moreover, the mAP values were improved by 4.99, 2.49, 3.94, 2.25, and 4.12 percentage points, respectively, compared with the mainstream rotating target detection models, Gliding vertex, R3det, Rotated Faster R-CNN, S2anet, and Rotated Retinanet. The Oriented RCNN was utilized to accurately represent the head direction of wheat ears. The background area was also reduced in the prediction bounding boxes. The model detection was more visually appealing. The finding can provide an effective way for the practical observation of the growth status of wheat ears and counting the number of ears. [ABSTRACT FROM AUTHOR]

Wheat diseases have posed a severe threat to the grain quality and yield in wheat production. It is highly required for the automatic, rapid, and accurate identification of wheat diseases on equipment with limited resources, particularly for timely preventive measures. In this study, a lightweight detection was proposed for wheat diseases using an improved YOLOv8n, termed PGCW-YOLOv8. Firstly, a lightweight CPU network (PP-LCNet) was introduced to replace the backbone network of the YOLOv8 structure, in order to reduce the large model weight files. Depthwise separable convolution (DepthSepConv) structure was introduced into the Backbone layer to reduce the parameter quantity, thereby reducing the size of weight files for better detection performance. Secondly, a global attention mechanism (GAM) module was added to the Neck section, in order to enhance the feature extraction and fusion of the network. As such, the model was improved to better focus on the small features of the target disease. The higher detection accuracy of minor lesions was achieved to better understand the important information in the image through a global attention mechanism, thereby accurately identifying the diseases under complex background and lighting conditions. Thirdly, a lightweight content-aware reassembly of features (CARAFE) module was introduced to aggregate the context information within a larger receptive field, in order to improve the detection accuracy of the model. The detailed image information was then effectively preserved using upsampling and downsampling operations. Finally, the Wise-IoU boundary loss function was used instead of the original loss function, in order to enhance the bounding box regression performance of the network model. The position and size of disease features were better learnt to improve the detection of small target diseases. Experimental results show that the improved PGCW-YOLOv8 model reduced the computational complexity (GFLOPs), parameters, and model size by 13.6%, 12.5%, and 11.3%, respectively, in the wheat disease datasets collected in field environments, compared with the original YOLOv8n baseline model. Meanwhile, the precision, mean average precision (mAP), and frames per second (FPS) of the improved model increased by 4.5, 1.9 percentage points, and 23.1%, respectively, compared with the original. A comparison was made under the same experimental conditions with the mainstream deep learning models, such as Faster R-CNN, YOLOv5s, YOLOv7, Yolov7-tiny, YOLOXs, and EdgeYOLO. The improved PGCW-YOLOv8 model shared the highest precision and mAP values, while the lowest computational complexity (GFLOPs), parameters, and model size. Three datasets of comparative experiments indicate that the lightweight PGCW-YOLOv8 network model outperformed the original YOLOv8 and YOLOv5 models, especially in the accurate detection of the wheat disease features under complex background and multi-target situations. The finding can provide a strong reference for the intelligent detection of wheat diseases in real time, particularly for rapid detection applications, such as deployment on unmanned aerial vehicles and mobile terminal equipment. [ABSTRACT FROM AUTHOR]

Accurate and rapid crack detection has been one of the most important steps in duck eggs before processing. The shell of duck eggs may crack, particularly when subjected to the vibration, extrusion, and collision during production, transportation, packaging and storage. The microorganism can then invade through the crack, easy to cause the deterioration of duck eggs. Furthermore, the damage of one duck egg can greatly affect the processing requirements of the whole batch. However, manual detection cannot fully meet the large-scale production in recent years, due to the unstable accuracy rate, low efficiency and industrialization in the poultry egg industry at present. In this study, a lightweight detection was proposed for the multi-column cracks of duck eggs using improved YOLOv5l (you only look once version5 large). The better detection of duck egg was achieved in the simple, small amount of calculation and storage. A LED light was used to illuminate the duck egg under dark conditions. There was the difference of light transmittance between cracked eggshell and intact eggshell. Specifically, the Ghost_conv module was introduced to replace the original convolution in YOLOv5. The calculation and parameters of model were greatly reduced to increase the training speed with more lightweight. In addition, ECA (efficient channel attention) attention mechanism was added to the backbone Network and BIFPN (bi-directional feature pyramid network). The effective information was detected using the attention mechanism. Among them, the BIFPN was used to extract and fuse some features at different scales. The accuracy of model was improved to reduce the missing and wrong detection. At the same time, the fusion of CIoU and α-IoU loss function was used to replace the original GIoU function of YOLOv5, thus accelerating the regression prediction. A dataset of duck egg was obtained with 892 images, where a camera was used to capture duck eggs rolling on a conveyor. The dataset was then enhanced by random cropping, adding noise and mixup, in order to further improve the generalization and robustness of the model. The self-developed dataset of duck egg cracks was utilized to verify the detection performance of the improved model. The results show that the improved YOLOv5l network model shared the high performance. The detection accuracy of the improved model on the test set was 93.8%, which was 6.3 percentage points higher than that of the original YOLOv5l model. The number of parameters, floating point arithmetic and the occupied storage was reduced by 30.6%, 39.4% and 34.6%, respectively. The frame rate of detection was 28.954 frames /s, which was only 3.824 frames /s lower than the original YOLOv5l model. The detection accuracy of the improved YOLOv5l increased by 13.1, 12.5 and 8.2 percentage points, respectively, compared with the common target detection networks, such as single shot MultiBox detector (SSD), YOLOv4, and faster-RCNN (faster region convolutional neural networks). The high-precision detection with the low hardware resources can be expected to provide the promising solutions and technical applications. [ABSTRACT FROM AUTHOR]

The ripeness of Camellia oleifera fruits is closely related to their oil yield and tea oil quality. Manual one-time harvesting is the primary harvesting for Camellia oleifera at present. However, the uneven ripeness levels among fruits harvested in the same batch can significantly reduce the overall quality of the fruits. Furthermore, manual harvesting cannot fully meet the large-scale Camellia oleifera industry, such as low efficiency and high costs. Therefore, it is very necessary to implement intelligent harvesting for Camellia oleifera fruits. The maturity of Camellia oleifera fruit can be detected to determine the best maturity using deep learning. The purpose of this study is to establish the ideal ripeness of Camellia oleifera fruits in the natural environment. The harvesting period was also estimated to implement the intelligent harvesting, in order to improve the oil yield and quality of fruits. A dataset was constructed for the ripeness detection. The photographs of Camellia oleifera fruits were captured at different ripening stages in natural environments using a smartphone. The phenotypic characteristics of Camellia oleifera fruits were also determined to follow the industry standards. The ripeness was categorized into three stages: immature, mature, and over-mature. Data augmentation techniques were applied on the dataset, such as brightness adjustment, salt-and-pepper noise addition, and simulating artificial occlusion. After that, the dataset was divided into the training, validation, and testing sets with a ratio of 7:1:2. An improved YOLOv7 model was constructed to deal with the occlusion in the natural environment. A cross-attention module was added in the YOLOv7 feature extraction network. The vertical and horizontal information was calculated for each pixel in Camellia oleifera images using two attention-weighted procedures. The key features were identified to determine the ripeness of Camellia oleifera fruits, thus effectively avoiding the interference from the complex backgrounds, such as the branches and leaves. Additionally, the traditional non-maximum suppression was replaced with the distance- and intersection-based NMS. The normalized distance between the center points of two candidate boxes was selected to calculate the intersection ratio, particularly for the missed detections due to mutual occlusion of Camellia oleifera fruits, in order to detect the overlapping fruits. The YOLOv7 model was trained on 3098 images from the training set, then evaluated using 442 images from the validation set, and finally tested on 885 images from the test set. The better performance was achieved in the precision rate of 93.52%, a recall rate of 90.25%, an F1 score of 91.86%, an average precision of 94.60%, an average detection time of 0.77 s, and a model weight of 82.6 MB in the test set. The ablation experiments demonstrated that the improved model was used to effectively detect the ripeness of Camellia oleifera fruit. Compared with the original YOLOv7 model, the mean average accuracies were improved by 1.10 and 1.81 percentage points, respectively. The overall mean average accuracy was improved by 2.91 percentage points. However, the detection time and model size only increased by 0.015 s and 11.3 MB, respectively. Compared with the Faster R-CNN, EfficientDet, YOLOv3, and YOLOv5l models, the improved YOLOv7 model increased the average accuracy by 7.51, 5.89, 4.21, and 4.21 percentage points, respectively. Additionally, the detection time was reduced by 1.06, 1.12, 0.10, and 0.03 s, respectively. The maturity grade of Camellia oleifera fruits was accurately discriminated, compared with the previous. In summary, the improved YOLOv7 model was achieved in the higher accuracy with only a slight sacrifice in the detection time and model size. This finding can provide a theoretical basis to estimate the optimal harvesting period of Camellia oleifera fruits and intelligent picking under natural conditions. [ABSTRACT FROM AUTHOR]

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Identifying quartz, feldspar and cuttings in sandstone is of great significance for judging the dispositive environment, but traditional manual identification methods have problems of strong subjectivity and high dependence on experience. An image recognition method of sandstone micro-components based on Faster R-CNN targeted detection algorithm was constructed by using deep learning, convolutional neural network and other technologies. Intelligent recognition of three components, the average recognition accuracy of the three components is 89. 28% . In order to verify the reliability of the model, the experiments compared different algorithms and feature extraction networks. The results show that the recognition effect of the Faster R-CNN targets detection algorithm is better than that of YOLOv3, YOLOv4, and YOLOv5s, and the performance of the ResNet50 features extraction network is better than VGG16. The Faster R-CNN targets detection model using the ResNet50 features extraction network has significant advantages, it can better meet the identification requirements of rock slices, and provide intelligent technical solutions to traditional manual methods. [ABSTRACT FROM AUTHOR]