摘要
[目的]去除复杂背景影响,提高角斑病病斑分割精度和速度。[方法]首先对预处理后的b*通道图像采用大津法进行初分割,去除大部分背景和噪声。再对目标部位的灰度图,用基于遗传算法改进的KSW熵阈值分割法进行二次分割,得到病斑的二值图像,并计算病斑面积,最后与叶片面积做比得到病斑密度。[结果]该方法计算的病斑密度与方格板手动计算的结果的绝对误差约为0.02,而病斑的分割速度提高了45%以上。[结论]该方法为黄瓜角斑病病害程度自动诊断提供技术依据。
[Objective] The aim was to eliminate the influence of complex background,and improve the precision and speed of angular leaf spot segmentation.[Method] Firstly,the Otsu method was used to segment the b* channel image after preprocessing to remove background noise.Then,the gray image of the target area was segmented through the GA-improved KSW entropy thresholding method,and the binary image of disease spot was obtained as well as disease spot area was computed. Finally,the disease spot density was calculated.[Results]The results showed that the absolute error of the method calculated by the algorithm was about 0. 02 compared with grid plate manual calculation method,and the segmentation speed increased by more than 45%.[Conclusion]The method provides the technical basis for automatic diagnosis of disease degree of cucumber spot disease.
出处
《安徽农业科学》
CAS
2017年第32期212-215,共4页
Journal of Anhui Agricultural Sciences
基金
陕西省农业科技创新与攻关项目(2015NY034)
关键词
瓜角斑病
图像分割
遗传算法
KSW熵
Angular leaf spot of cucumber
Image segmentation
Genetic algorithm
KSW Entropy
