The Detection of Impurity Content in Machine-Picked Seed Cotton Based on Image Processing and Improved YOLO V4

The detection of cotton impurity rates can reflect the cleaning effect of cotton impurity removal equipment, which plays a vital role in improving cotton quality and economic benefits. Therefore, several studies are being carried out to improve detection accuracy. Image processing technology is increasingly used in cotton impurity detection, in which deep learning technology based on convolution neural networks has shown excellent results in image classification, segmentation, target detection, etc. However, most of these applications focus on detecting foreign fibers in lint, which is of little significance to the parameter adjustment of cotton impurity removal equipment. For this reason, our goal was to develop an impurity detection system for seed cotton. In image segmentation, we propose a multi-channel fusion segmentation algorithm to segment the machine-picked seed cotton image. We collected 1017 images of machine-picked seed cotton as a dataset to train the detection model and tested and recognized 100 groups of samples, with an average recognition rate of 94.1%. Finally, the image segmented by the multi-channel fusion algorithm is input into the improved YOLOv4 network model for classification and recognition, and the established V–W model calculates the content of all kinds of impurities. The experimental results show that the impurity content in machine-picked cotton can be obtained effectively, and the detection accuracy of the impurity rate can increase by 5.6%.

Interfacial reactions between impurities and slag onset of Si purification by slag addition

The interfacial reactions between impurities (Al and Ti) and slag onset of Si purification by 51 mol% SiO2–34 mol% CaO–15 mol% MgO slag addition were studied to enhance impurity removal efficiency from Si. The Al distribution behavior at the Si/Slag interface was investigated; a short reaction time (10 s) resulted in the formation of successive SiO2–CaO–MgO–Al2O3 layers in the slag with a thickness of 10 µm; increasing the reaction time (60 s) resulted in the entire ternary slag being changed into SiO2–CaO–MgO–Al2O3 quaternary slag due to the diffusion of Al2O3. It was shown that the highest impurity removal rate of Al could be achieved at the onset of the slag refining process. Based on the Ti distribution at the Si/slag interface, the slag refinement with 51 mol% SiO2–34 mol% CaO–15 mol% MgO had no effect on Ti removal.

Gradient Cleaning Method of Potato Based on Multi-Step Operation of Dry-Cleaning and Wet Cleaning

In view of the poor effectiveness of existing potato cleaning methods in China and reflecting the findings of a research analysis of basic sizes and types of impurities on potato tubers, a gradient cleaning method for potato based on a multi-step dry-cleaning and wet cleaning operation was proposed. The method mainly consists of dry-cleaning and wet cleaning. The dry-cleaning stage, which combines vibration and brushing, could effectively remove impurities such as residual rhizomes, peeled potato skin, and large pieces of soil and crushed stone from the surface of potato tubers. The wet cleaning stage adopts the gradient cleaning method of pre-cleaning, rough cleaning and fine cleaning, which could further remove soil and crushed stone attached to the surface and hidden in the sprout eyes of potato tubers. The optimal parameter combination for the gradient cleaning method was determined as follows. The potato feeding amount was 3 t/h, the speed of the rubber chain rod mechanism was 25 r/min, the speed of the first and third brush roller was 40 r/min, the speed of the second and fourth brush roller was 56 r/min, the moving speed of the immersion mechanism conveying net chain was 0.04 m/s, the speed of the brush roller in the high pressure spray and brush roller combination mechanism was 40 r/min, the ultrasonic power was 1200 W, the ultrasonic frequency was 33 kHz, the bubble intensity was 300 L/min, and the moving speed of the conveying net chain in the ultrasonic and bubble combination mechanism was 0.05 m/s. Taking the impurity removal rate and damage rate of potato tuber as the test indexes, a potato cleaning performance test was carried out under the optimal parameters combination. The results showed that the average impurity removal rate and damage rate of potato tubers were 99.05% and 2.48%, respectively. Additionally, the operational performance fully met the requirements for potato cleaning. This study provides a new method for potato cleaning in China and can also provide a reference for cleaning other root and tuber crops.