Detection Of Rice Density During Maturity Based On Image Processing

The strong development of agricultural mechanization can increase productivity of farmers and economic benefits. Raising the level of agricultural mechanization is not only conducive to high-yield harvest, but also will help to reduce environmental pollution and agricultural sustainable and efficient development. A long time, the Information of crop density to obtain timely and accurate information has been constrained the level of agricultural machinery automation of key factors, and also is the field of agricultural engineering research problems. Such as during rice and wheat combine harvester work, not considering the changes in walking speed,threshing cylinder and the volume of the feed density is directly proportional to the growth of rice and wheat。That is, the greater the crop density, the feed volume of a corresponding increases. But because of the random distribution of soil, fertilizer, water, light and other factors of field crops, crop growth can not be very uniform in density, and so feeding volume is random change. Random changes will affect a series of changes such as the feeding threshing gap, roller speed, the length of intaglio and the exhaust volume variable parameters of the fans which purge grain out, it is difficult to ensure that the work of the combine harvester machinery in a ideal performance of the scope of work.According to the phenomenon of non-net, entrainment, threshing losses, etc in the work course of combine, this paper studies the distribution of rice canopy density, uses computer image processing techniques and raises a detection method of the rice canopy density distribution based on the RGB color images model .Experimental site selects in the rice producing town of the FengLe town of Feixi. Experiment in two periods: early rice-2varieties; late rice-4varieties. Data acquisition and processing methods are: (1)Sample data acquisition: Through the same limited area of A, we get the rice images under natural state And then cut it from the roots of rice from the same height, weight it , weight it then get the weight of parameters such as gross weight, net weight etc. we get the value of the density of rice by calculating and that does not take into account other factors secondary , The establishment of the density value of the volume of D and F a function of feeding relations F = f (D). (2)Image processing: The integrated use of image processing, plant pathology, Chromaticity, geometric features, from the characteristics of knowledge, using computer image processing technology we can analyze the color images to find the R, G, B the optimal combination, then get the 2R+G color feature vector images. Features of color images using iterative method will be automatically selected threshold Valley to separate leaf from the background. 2R + G color combinations from the color images can segment maximally related directly factors between rice leaves and feeding, such soil, weeds in the background factors all is 0 after the image segmentation.(3)Data fitting: By comprehensive use of computer data processing fitting techniques, the relevance of analytical techniques, a significant test of technology, the image pixel value of the canopy and the amount of rice to feed data fitting and related test, We can carry out data fitting and related test between the pixel value of the canopy Images of rice and the feeding volume of rice and finally establish the detection model that the feeding volume of rice changs with the image pixels.Through correlation analysis and test of significance can be concluded: the volume of feed rice combine harvester and the 2R + G color feature map changes in the value of the pixel has significant positive correlation.In this paper, the detection model of the feeding volume of the mature rice changing with the 2R + G color images to improve the working condition of combines harvesters has great reference value. This method provides a feasible option which helps combine harvester in operation for the process of feeding real-time adjustment to adjust the volume and improve labor productivity.