| According to the theory and method of the computer image processing thechnique and machine vision, it is proved that it is possible that N status of winter wheat is diagnosed by processing the digital machine images near field, based on field experiment and combined with general observation and test. The technique of capturing digital image and the method of image pretreatment are founded. The characteristics, which can express the canopy color of winter wheat under varied N-status, are abstracted and filtered. The relation between the color features of wheat canopy and chlorophyll content and nitrogen concentration of wheat body are analyized. In additon, the paper puts forward two means to diagnose N status: one is the color features diagnosis and the other is leaf covering features diagnosis. Two methods of image classification and identification, as well image colormodels, are included in the color features diagnosis. Through being practicly detected, the correct rateof diagnosis is high. Conclusions is drawed as following:1. There is obvious difference in the leaf area, dry matter and plant height of Getting up, jointing, booting and flowering phase between "Zhongyou9507" and "Jing411". The variety of chlorophyll content and nitrogen concentration of wheat canopy has also difference. The results show that the SPAD and nitrogen concentration of canopy have linear relation from turn green to dough stage, moreover, they consistently change with the difference of N supplied by soil.2. The effective images of wheat canopy are obtained by the digital camera, by the means of the limitative condition, combined with standard image pretreatment. The size of image can be decreased by the interval distance method, and the initial information of images can be preserved maximumly. The change of the colorful information of images with environment in shorter time can basically be eliminated by the means of color comparison, can be corrected to the range of ten gray value difference.3. The digital image processing technique could be used to detect N status of winter wheat. The better results of diagnosis can be obtained by abstracting color and leaf covering features from the images.(1) The method was established by Stat. classification features to diagnose N status of winter wheat. There is a continuous difference curve between low N treatment and middle-high N treatment to classify wheat canopy images by feature Have and MOD from the phase of returning to green to dough stage. Feature F1[(R-B),(G-B)] can effectively distinguish field wheat canopy images of those between low N treatment and middle-high N treatment during jointing phase and booting phase through combination of two features . And there is a clear division among low N , middle N and high N treatment using the combination feature from jointing phase to flowering stage.(2)Diagnose N status of winter wheat by the means of mode matching and build four matchingmodes--constructing [H], integrated optical density[1], added histogram[EG] and histogramdistance[D].Except trefoil stage, turn green and dough stage, the correct rate which wheat canopyimages of "Jing411" classify three parts: nitrogen absence, nitrogen middling and nitrogen abundance is over 73 percent, the correct classification rate of "Zhongyou9507" reaches 70 percent only in the Getting up, jointing, booting and flower stage, the correct classification rate of "Zhongyou9507" and "Jing411" in the jointing, booting and flowering stage is high and reaches over 90 percent.(3) Diagnose N status of winter wheat through leaf colour models. From the diagnosis results of sixty images of every growth season, the correct classification rate is over 80 percent in the flowering stage, but the result is not good in the grain filling and dough stage. The average values of H and (R-B) are used as the inputs of every growth season diagnosis models. The average feature of H is higher than the one of (R-B), and the correct classification rate is still over 80 percent from the getting...
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