The timing,convenient and reliable method of diagnosing and monitoring crop nutrition is the foundation of scientific fertilization management. However,this expectation cannot be fulfilled by traditional methods,which always need excessively work on sampling,detection and analysis and even exhibit lagging timing. In the present study,stable images for potassium-stressed leaf were acquired using stationary scanning,and object-oriented segmentation technique was adopted to produce image objects. Afterwards,nearest neighbor classifier integrated the spectral,shape and topologic information of image objects to precisely identify characteristics of potassium-stressed features. Diagnosing with image,the 3rd expanded leaves are superior to the 1st expanded leaves.In order to assess the result,250 random samples and an error matrix were applied to undertake the accuracy assessment of identification. The results showed that the overall accuracy and kappa coefficient was 96.00% and 0.945 3 respectively. The study offered an information extraction method for quantitative diagnosis of rice under potassium stress.