Detection for navigation route for cotton harvester based on machine vision

Abstract: Auto-navigation has a great significance in increasing the operating quality and production efficiency of agriculture machinery, such as improving the working environment and security situation for workers, reducing the labor intensity, etc. The vision navigation has many technical advantages that it can adapt to the complicated field of the operating environment, has wide detection range and has rich and complete information. It is the research focus in the field of agriculture machinery auto-navigation. How to extract routes fast, accurately, and effectively in the natural environment is the key in vision navigation. The vision navigation routes' detect of a Cotton-picker is the main premise to achieve its auto-navigation. There are many difficulties in detecting the operation routes of a cotton-picker in the field. For example, during harvest, there are many kinds of target features like stalks, cotton, bolls, leaves, weeds in the cotton field and its outside region. In addition, a little cotton may be left on the stalks in the harvested region when we use machine to pick. This paper puts forward the detection algorithms of the operation routes of a cotton-picker, the edge and the end of the cotton field by analyzing the different color features of the harvested region, the un-harvested region, the outside region, and the end of the field. First, we used the difference of color components, such as 3B-R-G, |R-G|, |R-B| and |G-B| to extract the target features of the inner and outside of the cotton field respectively, and smooth the image using the moving average method by the set length. Then by finding the rose critical point of the crest that is based on the lowest trough point to the un-harvested region, and connecting with the line detect result of the previous frame, we determine the alternate point group of a straight line transform. At last, we extracted the navigation routes based on Passing a Known Point Hough Transform (PKPHT). The test proves that the extracted line by this algorithm can match the harvested region, the un-harvested region and the edge of the field accurately. The processing time is 56.10 ms per frame, which can meet the demand of the real production of a cotton-picker in the field. This research can provide the reference to the vision navigation routes' detection of wheat, corn and many other crops when we harvest them by machine.