In a realistic scenario where a large number of workpieces need to be measured, any measurement method that can detect roughness only for a single workpiece is very limited in terms of measurement efficiency. To address this problem, a multi-object surface roughness detection model based on Faster R-CNN is proposed in this paper. The model features milled workpiece images with a convolutional neural network. And the obtained features will feed into a Region Proposal Network for inferring those regions where workpieces may be present. The regions and features go through a ROI pooling layer and a predictor to get more accurate target regions and measure the roughness of the workpieces in the regions. The experimental results show that the model proposed in this paper can accurately detect those regions where workpieces are present in the image and detect the corresponding roughness grade of the workpieces. A mean average precision of 97.80% and a detection speed of 5.82 fps for the test set of milled workpieces were achieved by the model under different placement angles and variable light conditions.

In this paper, we present a nutrient solution control system, designing a nutrient solution electrical conductivity (EC) sensing system composed of multiple long-range radio (LoRa) slave nodes, narrow-band Internet of Things (NB-IoT) master nodes, and a host computer, building a nutrient solution EC control model and using the particle swarm optimization (PSO) algorithm to optimize the initial weights of a back-propagation neural network (BPNN). In addition, the optimized best weights are put into the BPNN to adjust the proportional–integral–derivative (PID) control parameters Kp, Ki, and Kd so that the system performance index can be optimized. Under the same initial conditions, we input EC = 2 mS/cm and use the particle swarm optimization BP neural network PID (PSO-BPNN-PID) to control the EC target value of the nutrient solution. The optimized scale factors were Kp = 81, Ki = 0.095, and Kd = 0.044; the steady state time was about 43 s, the overshoot was about 0.14%, and the EC value was stable at 1.9997 mS/cm–2.0027 mS/cm. Compared with the BP neural network PID (BPNN-PID) and the traditional PID control approach, the results show that PSO-BPNN-PID had a faster response speed and higher accuracy. Furthermore, we input 1 mS/cm, 1.5 mS/cm, 2 mS/cm, and 2.5 mS/cm, respectively, and simulated and verified the PSO-BPNN-PID system model. The results showed that the fluctuation range of EC was 0.003 mS/cm~0.119 mS/cm, the steady-state time was 40 s~60 s, and the overshoot was 0.3%~0.14%, which can meet the requirements of the rapid and accurate integration of water and fertilizer in agricultural production.

Due to the problem of single factor and no benchmark in the design of image characteristic index, the measurement results of machine vision-based grinding surface roughness measurement are greatly influenced by the light source brightness, and the accuracy is limited. To address this problem, a method based on the combination of a full-reference (FR) image quality algorithm visual saliency-induced index ( VSI ) and a backward propagation (BP) neural network for grinding surface roughness measurement is proposed. First, the VSI is applied to characterize grinding surface roughness. Its performance is then compared with the mainstream color image-based indices. Meanwhile, the ability of anti-interference to light source is analyzed for the VSI . Furthermore, in order to improve the prediction accuracy, the BP neural network is selected and applied to construct the roughness prediction model. The experimental results show that the VSI has significant advantages in the abilities of measurement accuracy and anti-interference to light source brightness levels. BP can significantly improve the prediction accuracy for VSI .