Abstract
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%.
Highlights
China is a country that has a high production of cotton
The proposed detection method based on image processing and improved YOLO V4 neural network model can effectively detect the impurity rate of machine-picked seed cotton collected by camera
Compared with the raw cotton impurity image recognition method based on edge detection [7], this study used a YOLO v4 neural network with improved loss function to train the model of impurities in a seed cotton picking machine, and input the machine seed cotton image segmented by multi-channel fusion algorithm into the model for impurity classification and recognition, which reduced the interference in the process of classification and recognition and improved the accuracy of impurity recognition
Summary
China is a country that has a high production of cotton. With the mechanization of cotton picking increasing year after year, the impurities mixed with cotton increased. The working conditions of machine-picked cotton, such as the high-quality equipment and complicated machinery, make a difference in cotton cleaning. Suppose the rotational speed of the cotton-picking processing equipment is too low: in this case, the cleaning effect of cotton is not good, and a rotational speed which is too high will cause damage to the cotton fiber [1,2]. Real-time monitoring of the impurity content in seed cotton can provide a basis for adjusting equipment parameters of machine cotton picking and processing equipment. This is of great significance for improving the quality of cotton
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