The mechanization and intelligentization of the production process are the main trends in research and development of agricultural products. The realization of an unmanned and automated picking process is also one of the main research hotspots in China’s agricultural product engineering technology field in recent years. The development of automated apple-picking robot is directly related to imaging research, and its key technology is to use algorithms to realize apple identification and positioning. Aiming at the problem of false detection and missed detection of densely occluded targets and small targets by apple picking robots under different lighting conditions, two different apple recognition algorithms are selected based on the apple shape features to study the traditional machine learning algorithm: histogram of oriented gradients + support vector machine (HOG + SVM) and a fast recognition method for multiple apple targets in a complex occlusion environment based on improved You-Only-Look-Once-v5 (YOLOv5). The first is the improvement of the CSP structure in the network. Using parameter reconstruction, the convolutional layer (Conv) and the batch normalization (BN) layer in the CBL (Conv + BN + Leaky_relu activation function) module are fused into a batch-normalized convolutional layer Conv_B. Subsequently, the CA (coordinate attention) mechanism module is embedded into different network layers in the improved designed backbone network to enhance the expressive ability of the features in the backbone network to better extract the features of different apple targets. Finally, for some targets with overlapping occlusions, the loss function is fine-tuned to improve the model’s ability to recognize occluded targets. By comparing the recognition effects of HOG + SVM, Faster RCNN, YOLOv6, and baseline YOLOv5 on the test set under complex occlusion scenarios, the F1 value of this method was increased by 13.47%, 6.01%, 1.26%, and 3.63%, respectively, and the F1 value of this method was increased by 19.36%, 13.07%, 1.61%, and 4.27%, respectively, under different illumination angles. The average image recognition time was 0.27 s faster than that of HOG + SVM, 0.229 s faster than that of Faster RCNN, and 0.006 s faster than that of YOLOv6. The method is expected to provide a theoretical basis for apple-picking robots to choose a pertinent image recognition algorithm during operation.

The aim of this paper is to detect the crack depth of metal material surfaces.. As a methode we use: a phase sensitive circuit is used to detect the signal of cracks and restrain interferential signal. The correlative circuit modules are used to design the quantitative indication system for crack depth on the surface of metal material. We conclude that the system is effective in indicating crack depth.

In this paper, the technology of relay feedback is first extended to the discrete field of controllers design assignment in the Internal Model Controller (IMC) configuration to achieve the automatic tuning in the complex dynamic factory circumstance. It is shown that this proposed method realizes automatic tuning easily based on the relay identification. For one thing, an improved simple proof of this identification and control procedure is provided. For another thing, this new controller entitles stronger disturbance rejection and better robust performance and the dynamic response compared with the former methods which are demonstrated by several simulation experiments. Furthermore, the proposed method successfully applies to the First-Order Plus Dead-Time (FOPDT) system with large time delay while many other traditional Proportional-Integral-Derivative (PID) self-tuning strategies even fail to converge in process control.

We study quantitative eddy detection by scanning drive magnetism in changed frequency to collect signal of crack. We form the multi-layer back propagation network model, and simulate experiment date at computer. The simulating result indicate that the algorithm is rapid and the result is precision. This work is the fundamental theory in quantitative indication of crack depth on steel material surface by eddy current flaw detection.