CAJ | 학술논문

为探讨植物病虫害互不交叉、重叠的数字典型特征值来进行病虫害计算机识别，研究了椪柑病虫害为害状图像傅里叶变换幅度谱的多重分形特征。首先，用改进型分水岭算法检测病虫害为害状边缘，并对其进行区域合并，形成病虫害为害状边界。其次，对病虫害果进行二维离散傅里叶变换，依据病虫害为害状边界进行图像标记，提取标记区域内的傅里叶变换幅度谱图。最后，对傅里叶变换幅度谱图进行多重分形分析及多重分形谱的二次拟合，将拟合抛物线段的高度、宽度和质心坐标作为病虫害特征值，并以此为输入变量，建立 BP 神经网络椪柑病虫害识别模型来进行病虫害识别，椪柑蓟马、花潜金龟子、吸果夜蛾、侧多食跗线螨、椪柑炭疽病5类病虫害30组测试样本中吸果夜蛾识别正确率最高96.67%，侧多食跗线螨识别正确率最低86.67%，平均正确识别率为92.67%。试验结果表明：傅里叶变换幅度谱图的多重分形谱高度、宽度和质心坐标较精确地刻画了病虫害为害状这类复杂生物体的特征，该方法可进行椪柑病虫害自动识别，并可推广到其他植物的病虫害机器识别中。
위탐토식물병충해호불교차、중첩적수자전형특정치래진행병충해계산궤식별，연구료병감병충해위해상도상부리협변환폭도보적다중분형특정。수선，용개진형분수령산법검측병충해위해상변연，병대기진행구역합병，형성병충해위해상변계。기차，대병충해과진행이유리산부리협변환，의거병충해위해상변계진행도상표기，제취표기구역내적부리협변환폭도보도。최후，대부리협변환폭도보도진행다중분형분석급다중분형보적이차의합，장의합포물선단적고도、관도화질심좌표작위병충해특정치，병이차위수입변량，건립 BP 신경망락병감병충해식별모형래진행병충해식별，병감계마、화잠금구자、흡과야아、측다식부선만、병감탄저병5류병충해30조측시양본중흡과야아식별정학솔최고96.67%，측다식부선만식별정학솔최저86.67%，평균정학식별솔위92.67%。시험결과표명：부리협변환폭도보도적다중분형보고도、관도화질심좌표교정학지각화료병충해위해상저류복잡생물체적특정，해방법가진행병감병충해자동식별，병가추엄도기타식물적병충해궤기식별중。
Plant pests and diseases image recognition is one of the key technologies of digital agricultural information collection and processing. Usually, based on pest infestation-like plant, it is carried out according to the size, shape, color, texture, etc., or a combination of several parameters. Machine recognition of diseases and insect pests needs to use digitalized characteristics without overlapping. Multi-fractal analysis of Fourier transform spectra was adopted to investigate the possibility of extraction of damage pattern characteristics for Citrus reticulata Blanco var. Ponkan. First, images of the boundary of a damaged pattern are extracted with an improved watershed algorithm and region merging. Secondly, a Discrete Fourier Transform (DFT) was applied to the damaged fruit image. With reference to the boundary of a damaged pattern, a fruit image magnitude spectrum was extracted. Thirdly, a fruit image magnitude spectrum was multi-fractiously analyzed and the multi-fractal spectrum of DFT magnitude spectrum was quadratic fitted. Height, width, and centroid coordinate of a fitting parabolic section were chosen feature values to identify the diseases and insect damage of fruits, with these three feature values as inputs of a BP neural network identifying diseases and insect damage of Ponkan, and the accuracy was up to 92.67%. Finally, the amplitude spectrum of the Fourier transform was adopted for multifractal analysis and multi-fractal spectrum of a quadratic fit;fit parabola segment height, width, and centroid coordinates were regarded as pests’ Eigen values, and then used as input variables to establish a BP citrus pest identification neural network model for pest identification. Among 5 classes of pests, in 30 groups of test samples, such as Pezothrips Kellyanus, Oxycetonia Jucunda, Oraesia Emarginata, Polyphagotarsonemus Latus, Colletotrichum Gloeoporioides Penz, the highest recognition rate was for Oraesia Emarginata, that is 96.67%, Polyphagotarsonemus Latus was the lowest at 86.67%, and the average correct recognition rate was 92.67%. The test came to the conclusion that the height, width, and centroid of a multi-fractal spectrum of a Fourier transform spectrum of damaged fruit image better illustrates the features of the disease and insect damage of fruits, such as a complicated biological entity. This method is possibly applicable to automatic recognition of disease and insect damage of Citrus reticulata Blanco var. Ponkan, and it’s able to be applied to disease and insect damage recognition for other plants.