As a classic and effective edge detection operator, the Sobel operator has been widely used in image segmentation and other image processing technologies. This operator has obvious advantages in the speed of extracting the edge of images, but it also has the disadvantage that the detection effect is not ideal when the image contains noise. In order to solve this problem, this paper proposes an optimized scheme for edge detection. In this scheme, the weighted nuclear norm minimization (WNNM) image denoising algorithm is combined with the Sobel edge detection algorithm, and the excellent denoising performance of the WNNM algorithm in a noise environment is utilized to improve the anti-noise performance of the Sobel operator. The experimental results show that the optimization algorithm can obtain better detection results when processing noisy images, and the advantages of the algorithm become more obvious with the increase of noise intensity.

The wide variety of crops in the image of agricultural products and the confusion with the surrounding environment information makes it difficult for traditional methods to extract crops accurately and efficiently. In this paper, an automatic extraction algorithm is proposed for crop images based on Mask RCNN. First, the Fruits 360 Dataset label is set with Labelme. Then, the Fruits 360 Dataset is preprocessed. Next, the data are divided into a training set and a test set. Additionally, an improved Mask RCNN network model structure is established using the PyTorch 1.8.1 deep learning framework, and path aggregation and features are added to the network design enhanced functions, optimized region extraction network, and feature pyramid network. The spatial information of the feature map is saved by the bilinear interpolation method in ROIAlign. Finally, the edge accuracy of the segmentation mask is further improved by adding a micro-fully connected layer to the mask branch of the ROI output, employing the Sobel operator to predict the target edge, and adding the edge loss to the loss function. Compared with FCN and Mask RCNN and other image extraction algorithms, the experimental results demonstrate that the improved Mask RCNN algorithm proposed in this paper is better in the precision, Recall, Average precision, Mean Average Precision, and F1 scores of crop image extraction results.

This paper designs and implements an energy management system based on the Spring Boot framework. The system mainly includes three layers, which are the data collection layer, the business logic layer, and the display interface layer from bottom to top. The data collection layer is based on the RS-485 electrical standard and the MODBUS communication protocol. The two protocols connect all energy consumption monitoring points into a mixed topology communication network in the enterprise. The programs in the data collection layer poll each energy consumption monitoring point in the network to collect the data and transmit to the business logic layer. The business logic layer is developed on the basis of the Spring Boot framework and mainly includes two parts: the MySQL database and Tomcat server. In the MySQL database, the stored data are horizontally split according to the time column and stored in different data tables. The split of data reduces the load of a single data table and improves the query performance of the database. The Tomcat server is built into the Spring Boot framework to provide a basic environment for system operation. The Spring Boot framework is the core component of the system. It is responsible for collecting, storing, and analyzing data from energy consumption monitoring points, receiving and processing data requests from the display interface layer. It also provides standard data interfaces to the external programs. The display interface layer is developed on the basis of the Vue framework and integrated into the Spring Boot framework. The display layer combines an open-source visualization chart library called ECharts to provide users with a fully functional and friendly human–computer interaction interface. Through the calculation of hardware and software costs, considering the personnel cost in different regions, the total cost of the energy management system can be estimated. The cost of construction was approximately 210,000 USD in this paper. Since the system was actually deployed in a manufacturing company in December 2019, it has been operating stably for more than 600 days.

This work focuses on the problem of non-contact measurement for vegetables in agricultural automation. The application of computer vision in assisted agricultural production significantly improves work efficiency due to the rapid development of information technology and artificial intelligence. Based on object detection and stereo cameras, this paper proposes an intelligent method for vegetable recognition and size estimation. The method obtains colorful images and depth maps with a binocular stereo camera. Then detection networks classify four kinds of common vegetables (cucumber, eggplant, tomato and pepper) and locate six points for each object. Finally, the size of vegetables is calculated using the pixel position and depth of keypoints. Experimental results show that the proposed method can classify four kinds of common vegetables within 60 cm and accurately estimate their diameter and length. The work provides an innovative idea for solving the vegetable’s non-contact measurement problems and can promote the application of computer vision in agricultural automation.

Aiming at the problems of low data fusion precision and poor stability in greenhouse wireless sensor networks (WSNs), a multi-sensor data fusion algorithm based on trust degree and improved genetics is proposed. The original data collected by the sensor nodes are sent to the gateway through the sink node, and data preprocessing based on cubic exponential smoothing is performed at the gateway to eliminate abnormal data and noise data. In fuzzy theory, the range of membership functions is determined, according to this feature, the data fusion algorithm based on exponential trust degree is used to fuse the smooth data to avoid the absolute degree of mutual trust between data. In this paper, we have improved the crossover and mutation operations in the standard genetic algorithm, the variation is separated from the intersection, the chaotic sequence is used to determine the intersection, and the weakest single-point intersection is implemented to improve the convergence accuracy of the algorithm, weaken and avoid jitter problems during optimization. The chaotic sequence is used to mutate multiple genes in the chromosome to avoid premature algorithm maturity. Finally, the improved genetic algorithm is used to optimize the fusion estimation value. The experimental results show that the cubic exponential smoothing can significantly reduce the data fluctuation and improve the stability of the system. Compared with the commonly used data fusion algorithms such as arithmetic average method and adaptive weighting method, the data fusion algorithm based on trust degree and improved genetics has higher fusion precision. At the same time, the execution time of the algorithm is greatly reduced.

Based on computer vision technology, this paper proposes a method for identifying and locating crops in order to successfully capture crops in the process of automatic crop picking. This method innovatively combines the YOLOv3 algorithm under the DarkNet framework with the point cloud image coordinate matching method, and can achieve the goal of this paper very well. Firstly, RGB (RGB is the color representing the three channels of red, green and blue) images and depth images are obtained by using the Kinect v2 depth camera. Secondly, the YOLOv3 algorithm is used to identify the various types of target crops in the RGB images, and the feature points of the target crops are determined. Finally, the 3D coordinates of the feature points are displayed on the point cloud images. Compared with other methods, this method of crop identification has high accuracy and small positioning error, which lays a good foundation for the subsequent harvesting of crops using mechanical arms. In summary, the method used in this paper can be considered effective.