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1、计算机视觉技术在农业上的应用（英文）黄喜梅 毕建杰 张楠 丁筱玲 李飞 侯发东 山东农业大学机械与电子工程学院山东省园艺机械与装备重点实验室 山东农业大学作物生物学国家重点实验室 摘 要： 随着图像处理技术和计算机的发展, 计算机视觉技术在农业的生产中有了更广泛的应用, 并且均取得了许多重要成果, 综述了农产品缺素诊断、水分诊断、杂草识别、产品品质检测和分级、农业采摘与分选等方面的研究进展, 最后提出了其存在的问题和对未来的展望。关键词： 图像处理; 计算机视觉技术; 农业生产; 前景; 作者简介：黄喜梅 (1991-) , 女, 山东菏泽人, 研究生, 研究方向为机器视觉, E-Mail:。
2、收稿日期：2017-08-21Application of Computer Vision Technology in AgricultureXimei HUANG Jianjie BI Nan ZHANG Xiaoling DING Fei LI Fadong HOU College of Mechanical and Electronic Engineering, Shandong Agricultural University, Shandong Provincial Key Laboratory of Horticultural Machineries and Equipments;
3、China State Key Laboratory of Crop-Biology, Shangdong Agricultural University; Abstract： With the development of image processing technology and computer, computer vision technology has been widely used in the production of agriculture, and has made many important achievements. This paper reviews it
4、s research progress on diagnosis of agricultural products, water diagnosis, weed identification, product quality testing and grading, agricultural picking and sorting and other aspects, and finally put forward its existing problems and prospects for the future.Keyword： Image processing; Computer vis
5、ion technology; Agriculture production; Prospect; Received： 2017-08-21Computer vision technology is ascience studying how to makethe machinesee.In other words, it is the machine vision which uses computer and video camera to replace human eyes to identify, judge, measure and track the targets, and t
6、hen proceed image processing by using computer to process the images into those suitable for human observation or to transmit for instrument detection.With the function of computer vision technology becoming more and more powerful, it has been more and more widely applied in the various fields in da
7、ily life, which reduces the human labor intensity, improves the productivity of mankind, realizes the intelligent and automatic agricultural production.The research on computer vision technology started late in China, and the technology is not advanced enough, neither do the research complete enough
8、.However, a certain achievements have been made with the continuous progress of the various technologies in China.In order to promote the increase of agricultural mechanization level in China, this paper reviewed its research progress on agricultural products diagnosis, water diagnosis, weed identif
9、ication, product quality testing and grading, agricultural picking and sorting and other aspects, and briefly probed into its development prospects.Application of Computer Vision Technology in AgricultureResearch on computer vision technology in nutrient deficiency diagnosisThe nutrients needed by c
10、rops are the food of their lives, which therefore play a crucial role in their physiological status.If the nutrient elements of a crop are deficiency, then the growth process of the crop will be greatly affected, resulting in problems like fruitlessness, yellow leaves, short plants.Therefore, it is
11、very necessary to study the nutrient deficiency of crops.CAO1used image processing technology to extract the color and texture characteristics of soybean leaves, obtaining 9 groups of color characteristicparametersand5groups of texture parameters, and then used principal component analysis and multi
12、variate linear regression method to establish soybean plant nitrogen discriminant model.The verification results show that the model had a certain precision and could be used for the rapid detection and analysis of nitrogen in soybean.In the research of diagnosing tomato disease of nutrient deficien
13、cy intellectively in the soilless agriculture, XU2used genetic algorithm to select features of leaves image in order to get best information for diagnosing, which achieved the desired effect.ZHANG et al.3analyzed the red, green, blue (RGB) and their relative ratios rgb, as well as the correlations a
14、mong nitrogen, phosphorus and water content of the leaves and their color parameters, and the results showed that there were high linear correlation between the nitrogen content and the green weight G and leaf chroma H.2007, LI4extr-acted the RGB and the combined color characteristic values of tomat
15、o images using image processing software, which were conducted with regression analysis with the real-time nitrogen indicators, and the estimation model for nitrogen content in different leaves were established by combining percent ground cover of vegetation (PGCV) with nitrogen nutrition parameters
16、.WU5used RGB color model to discriminate walnut deficiency with chroma as key features with the following process:first, convert leaves image to grayscale, and calculate threshold by using the least squares method and segment image with this threshold to make the distinction between normal and deficiency regions, at last, compare pre-pixel gray in blade area with the typical gray values deriving from statistical analysis to determine the missing elements
