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Efficient detection of pepper clusters can be crucial to realize the automated harvesting in the development of pepper industry. However, the existing detection of pepper cluster cannot fully meet the large-scale in recent years, due to the low efficiency, generalization, and real-time algorithm. In this study, an improved YOLOv5s model was proposed to accurately detect the pepper clusters in real time. 400 images were collected from the Xuande Pepper Industrial Park, Qinglong County, Guizhou Province, Qianxinan Buyi and Miao Autonomous Prefecture, Guizhou Province, China. 148 images were also downloaded from the Internet, and then screened to enhance data diversity. The collected 548 images were then labeled using the LabelImg tool. In addition, the data enhancement was performed on each image and labeled bounding box. Two new images were randomly generated using random rotation and brightness adjustment. As such, a total of 1644 images were obtained as the dataset. Firstly, the EfficientViT network was optimized using the lightweight convolutional neural network (CNN) MobileNetV3 Block Convolution (MBConv) module and the ReLU with lightweight self-attention mechanism. CSPDarkNet53 network was used to replace the backbone of YOLOv5s. The global receptive field of model was enhanced to extract the important features. The better performance was suitable for the targets with the different scales, shapes, and background information. Secondly, Optimal Transport Assignment (OTA) was selected as the label assignment strategy. The optimal label assignment plan was obtained from the global perspective to achieve the effective label assignment. The robustness and accuracy of the model were improved for the complex scenes and target distribution. The WIoU loss function was also used as the bounding box localization of the detector, instead of the CIoU one. At the same time, the high-quality anchor frames were achieved to generate the benefit gradients for the better overall performance of the detector. A common experiment of target detection was performed on a homemade dataset, in order to verify the effectiveness of the improved model. The experimental results show that the improved model was achieved in a mean accuracy (mAP) of 97.3%, a parameter count of 5.9 M, and a detection speed of 131.6 frames per second (FPS). Specifically, the mAP and detection speed of the improved YOLOv5s model increased by 1.9 percentage points, and 14.5%, respectively, whereas, the number of parameters decreased by 15.7%, compared with the original. In addition, the mAP of the improved YOLOv5s model was 8, 16.9, 8.6, and 1.5 percentage points higher than that of EffcientDet-D1, SSD512, RetineNet-R50, and YOLOXs, respectively. The better performance of the improved YOLOv5s was achieved in terms of detection accuracy, detection speed, and number of model parameters. Therefore, the improved YOLOv5s model can be expected to accurately and rapidly detect the pepper clusters. [ABSTRACT FROM AUTHOR]

Digitization has been one of the significant directions in the rural land governance reform in the future. However, it is still lacking in the logical concepts and framework for the digital governance of rural land. In this study, a three-dimensional system framework of “demand-baseline-function” was established using the perspective of the transaction cost. An emphasis was also put on the construction of the digital system for land acquisition and relocation. Firstly, the basic, core and conflict needs of the participants were clarified ranging from the government, land users, and the original land stakeholder. Secondly, the bottom line was defined as the legal, spatial, and regulatory governance. Thirdly, the digital system was classified to design various digital realities. The future functional modules were selected to entirely reduce the transaction costs, thereby integrating the multiple sharing systems for the overall operational efficiency. In addition, Zhejiang Province of China was selected as the experimental area for the system design. The reason was that Zhejiang Province has been the representative region in the digital reform of land governance in recent years. Nevertheless, there was a high transaction cost in the process of land acquisition and relocation. It is also a high demand to effectively build a digital system during this time. As such, the actual system was designed to combine with the improved the digital framework. The result showed that the “demand-baseline-function” framework effectively guided the development and design of the “intelligent land acquisition and demolition” digital system in the actual application situation. The transaction costs were also reduced to fill the logical blank for the previous system of land digital governance. The demand analysis demonstrated that the improved system provided the port to fully meet the basic and core needs of the government, land users, collectives, and farmers. The information asymmetry, discourse game, and conflict transmission effectively reduced the transaction cost of the game between the subjects. The bottom line was clarified for the implementation boundary of the law and space of the land requisition and relocation process. The whole supervision system of land requisition and relocation was constructed to stabilize the land requisition and relocation environment. The functional analysis revealed that the improved system coordinated the actual and upgrade function of the “intelligent land acquisition and demolition” digital system. The logical framework effectively reduced the negotiation and decision cost, while the implementation and supervision cost, as well as the management and sunk costs in the land acquisition and demolition. Therefore, the “demand baseline function” framework can provide a strong reference for the transaction cost in the digital reform of land governance. Anyway, the underlying construction logic of the framework can also be applicable to the other areas without the digital construction guidance, providing for the most general transaction cost in the rural land governance. In addition, the logical framework of the system can also provide a strong reference for the digital transformation of rural land governance in the future, in order to improve the digital level of land space and the modernization of governance capacity. [ABSTRACT FROM AUTHOR]

Lameness is an abnormal gait or stance of an animal, indicating a more severe disorder of the locomotion system. The disturbance in the gait and body has posed a great threat to the welfare, health, and production of dairy cows in the herds. An early observation is typically performed on the sudden change in gait during day-to-day dairy practices. However, the current detection of lameness was widely used in the 2D cow video, particularly susceptible to light and environmental inferences. In this study, a novel approach was proposed to extract the movement features of the lameness cows using thermal infrared video under deep learning and image processing, in order to improve the detection accuracy and robustness against the complex illumination and environmental disturbances. Taking the multiparous lactating Holstein cows as the study objects, the datasets were collected at the Dingyuan farm in Hebei Province of China in September 2020. The gait features of lameness were related to the head, hoof, back, shoulder, and hip of cows. The performances of different target detection were then evaluated on the dataset of locomotion features from the thermal infrared and 2D images of cows walking. The results showed that the average accuracies of deep learning for the detection of cows locomotion features were 90.84%, 86.68%, and 79.68%, respectively, in the thermal infrared, daylight, and daylight images, whereas, those in the traditional image processing were 74.26%, 54.31%, and 48.95%, respectively. The test results demonstrated that the target detection of deep learning and image processing performed better on the thermal infrared images, compared with the 2D images. The thermal infrared images had effectively reduced the influences of external factors, such as the light and background. In addition, a lameness detection test was conducted on the walking cows using the thermal infrared video and 2D video, according to the arched curvature. It was found that the detection accuracies of cow lameness in the thermal infrared and 2D video were 90.0% and 83.3%, respectively, where the values of Macro-F1 were 0.90 and 0.83, respectively. Consequently, a better performance was achieved for the application of thermal infrared cameras in cow lameness detection using computer vision. This finding can provide a new potential strategy for the timely diagnosis and prevention of abnormal gait in cows in the dairy farming industry. [ABSTRACT FROM AUTHOR]

Buckwheat is widely cultivated in the regions of high altitude and cold mountains, such as northern and southwest China. The occurrence of crop diseases has posed a great threat to the quality and yield of buckwheat. Disease surveillance is an important measure to ensure the healthy development of the buckwheat industry. Since artificial intelligence has been extending to precision agriculture in recent years, machine learning and pattern recognition are beneficial to image classification, detection, and recognition for high accuracy and efficiency, while reducing the overhead in the detection of crop diseases. However, the existing deep learning cannot consider the complexity of disease images collected in the field, such as leaf overlap, uneven lighting, and shadow coverage. Therefore, it is necessary to accurately extract key features in a complex imaging environment for accurate recognition of crop diseases. In this study, a feasible multi-layer feature extraction in deep learning was proposed to extract the features of buckwheat diseases. A surveillance system of disease was then established according to the classified characteristics, thereby automatically identifying the categories of buckwheat diseases. Firstly, a combination of maximally stable extremal regions (MSER) and convolutional neural network (CNN) was used to detect the feature regions of buckwheat disease, where the disease and disease-free areas were separated to precisely locate the disease position. A two-level inception structure was then added to the traditional CNN. The first layer was used to extract the contour features of the disease, where the dimension of the feature was represented by shallow information. The second layer was selected to obtain more detailed features, so that the number of parameters was relatively small in each layer, avoiding the gradient disappearance in the training process. This structure was normally used to extract the features for the low-quality images of buckwheat disease, particularly for the higher accuracy of classification. An improved convolution based on cosine similarity was utilized rather than the traditional convolution operation, in order to reduce the sensitivity of illumination during the sampling process. After that, the positions with similar features to the convolution kernel behaved higher activation values in the feature map. In addition, the difference between features was also reduced to prevent the interference of sample noise, thereby achieving better feature extraction for samples with uneven illumination. Finally, a systematic dataset was established, including some images for eight types of buckwheat diseases. The results showed that the combined framework of MSER and CNN with the two-level inception recognition was an effective way for region detection. In the identification for the presence or absence of disease, the accuracy, recall, and F1-measure reached 97.54%, 96.38%, and 97.82%, respectively. Furthermore, the mean for the recognition of disease categories reached 84.86%, 85.78%, and 85.4%, respectively, indicating excellent performance in recognition accuracy and speed. The finding can provide promising technical support for the automatic recognition of buckwheat diseases. [ABSTRACT FROM AUTHOR]

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