CAJ | 학술논문

如何快速准确检测到作物病害信息是作物病害防治中的一个首要问题，根据作物叶片症状识别作物病害是作物病害检测的一个基本方法。由于病害叶片颜色、形状和纹理之间的差异很大，使得很多经典的模式识别方法不能有效地应用于作物病害识别中，为此提出了一种基于局部判别映射（local discriminant projects，LDP）的作物病害识别方法。首先，利用区域增长分割算法分割病害叶片中的病斑图像；然后，将病斑图像重组为一维向量，再由LDP对一维向量进行维数约简；最后，利用最近邻分类器识别作物病害类别。利用LDP算法将高维空间的一维向量样本点映射到低维子空间时，能够使得类内样本点更加紧凑，而类间样本点更加分离，从而得到最佳的低维分类特征。利用该方法在5种常见玉米病害叶片图像数据库上进行了病害识别试验，识别精度高达94.4%。与其他作物病害识别方法（如基于神经网络、主分量分析+概率神经网络和贝叶斯方法）和监督子空间学习算法（如算法局部判别嵌入和判别邻域嵌入）进行了比较。试验结果表明，该方法对作物病害叶片图像识别是有效可行的，为实现基于叶片图像处理技术的作物病害的田间实时在线检测奠定了基础。
여하쾌속준학검측도작물병해신식시작물병해방치중적일개수요문제，근거작물협편증상식별작물병해시작물병해검측적일개기본방법。유우병해협편안색、형상화문리지간적차이흔대，사득흔다경전적모식식별방법불능유효지응용우작물병해식별중，위차제출료일충기우국부판별영사（local discriminant projects，LDP）적작물병해식별방법。수선，이용구역증장분할산법분할병해협편중적병반도상；연후，장병반도상중조위일유향량，재유LDP대일유향량진행유수약간；최후，이용최근린분류기식별작물병해유별。이용LDP산법장고유공간적일유향량양본점영사도저유자공간시，능구사득류내양본점경가긴주，이류간양본점경가분리，종이득도최가적저유분류특정。이용해방법재5충상견옥미병해협편도상수거고상진행료병해식별시험，식별정도고체94.4%。여기타작물병해식별방법（여기우신경망락、주분량분석+개솔신경망락화패협사방법）화감독자공간학습산법（여산법국부판별감입화판별린역감입）진행료비교。시험결과표명，해방법대작물병해협편도상식별시유효가행적，위실현기우협편도상처리기술적작물병해적전간실시재선검측전정료기출。
Crop diseases often seriously affect both the quality and quantity of agricultural products and cause economic losses to farmers. How to accurately and quickly recognize the crop disease information is an important problem in preventing and controlling crop diseases. Crop disease recognition by crop leaf symptoms is a basic method of attempting to address this problem. Studies show that relying on pure naked-eye observing of the leaf symptoms by experts to detect the crop diseases can be prohibitively expensive, especially in developing countries. Automatic detection of crop diseases is an essential research topic, as it may prove benefits in monitoring large fields of crops, and thus automatically detect the symptoms of diseases as soon as they appear on crop leaves. In a research study of identifying and diagnosing crop diseases, the pattern of the disease is important part. Leaf spots are considered the important units indicating the existence of disease and regarded as indicators of crop diseases. A technique to detect the disease spot is needed. It is important to select a threshold of gray level for extracting the disease spot from the crop leaf. In order to classify disease leaf sample categories, a set of spot features for the classification and detection of the different disease leaves are required. The disease leaf images of the crop would be processed by using a series of image pre-processing methods, such as image transforming, image smoothing, and image segmentation. In this paper, crop disease leaf spots were segmented by the seeded region growing based region algorithm. Because the crop leaves look differ in many ways, most of classical pattern recognition methods are not effective to extract the disease features and reduce the dimensionality of diseased leaf images. A novel manifold learning algorithm called local discriminant projects (LDP) was proposed and was applied to crop disease recognition. After being projected into a low-dimensional subspace, the data points in the same class were close to each other, whereas the gaps between the data points from different classes became wider than before. In LDP, the class action was introduced to construct the objection function. There was no need to calculate the inverse matrix, so the small sample size problem occurring in traditional linear discriminant analysis was naturedly avoided, and much computational time would be saved by using LDP for dimensionality reduction. After each spot image was reorganized as one-dimensionality vector and its dimensionality was reduced by LDP, the nearest neighbor classifier was adopted to recognize crop disease. The extensive experiments were performed on a real maize disease leaf image database and compared with the traditional disease recognition methods and the supervised subspace learning algorithms in recognition performance. The mean correct classification rate of the proposed method was 94.4%. The proposed method was compared with the classical crop disease recognition methods (ANN, PCA+PNN, and Bayesian) and supervised subspace algorithms (LDE, DNE). The experiment results showed that the proposed method was effective and feasible for crop disease recognition. The preliminary study showed that there is a potential to establish an online field application in crop leaf disease detection based on leaf image processing techniques.