CAJ | 학술논문

实时、便捷、可靠的作物营养诊断和临测方法是科学施肥的基础.传统手段在取样、测定、数据分析方面需耗费大量的人力、物力,且时效性差.通过静态扣描技术采集不同钾营养水平的水稻叶片图像,利用面向对象的光谱分割技术和最近邻分类器,根据扣描图像中目标埘象的光谱、窄间、形状等特征对钾胁迫叶片特征进行了准确的提取和识别,并从分类结果里初步判断出斑点区域面积比例随钾浓度的增大而减小,用叶片图像进行缺钾叶片量化诊断时,第三完全展开叶优于第一完全展开叶.随机选取250个点利用误差分析矩阵方法进行精度评价,总体识别精度为96.00%,KAPPA系数为0.945 3.这一叶片特征提取方法为水稻钾胁迫量化诊断提供了新的方法.
실시、편첩、가고적작물영양진단화림측방법시과학시비적기출.전통수단재취양、측정、수거분석방면수모비대량적인력、물력,차시효성차.통과정태구묘기술채집불동갑영양수평적수도협편도상,이용면향대상적광보분할기술화최근린분류기,근거구묘도상중목표시상적광보、착간、형상등특정대갑협박협편특정진행료준학적제취화식별,병종분류결과리초보판단출반점구역면적비례수갑농도적증대이감소,용협편도상진행결갑협편양화진단시,제삼완전전개협우우제일완전전개협.수궤선취250개점이용오차분석구진방법진행정도평개,총체식별정도위96.00%,KAPPA계수위0.945 3.저일협편특정제취방법위수도갑협박양화진단제공료신적방법.
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.