Abstract: In order to realize diagnosis and identification of nitrogen nutrition in rice quickly and conveniently， we propose a diagnostic identification method of nitrogen nutrition in rice based on leaf HSV combined with LBP texture histogram features. The field experiment of early rice is conducted on “Zhong Jiazao” rice species， and four levels of nitrogen application are set. The images of the top three leaf tips of rice at the tillering stage are captured by the camera， and the HSV color features of each leaf tip are obtained separately by using the image processing technique， and with improving the LBP algorithm， the mean_LBP texture features are extracted by using the average gray value of the center point 5×5 range pixels instead of the gray value of the center point pixels as the threshold value. The H， S， V color histogram and mean_LBP texture histogram features are quantized， normalized， and concatenated and merged into a 1D feature vector with 1024 components， and after dimensionality reduction by PCA， GS_SVC， BP， KNN and RF methods are applied to construct a rice nitrogen nutrition diagnosis identification model， respectively. The experimental results show that the improved tip images of mean_LBP texture features combined with HSV color features have the identification accuracy rate reached 95.23% in GS_SVC that better than other models. Since the leaf tip is sensitive in the diagnosis of nitrogen nutrition in rice and image HSV， LBP features are not affected by subjective factors. It shows that the proposed method has good universality and reliability， which provides a feasible and new method for accurate nutritional diagnosis in rice and other crops.

Key words: SV color feature, LBP texture feature, nitrogen nutrition diagnosis, SVM