摘要
【目的】为探究棉花叶片蚜害快速识别的可行性,本研究对健康和受棉蚜为害的棉花叶片的高光谱图像进行识别。【方法】以新陆早45号为研究对象,通过获取健康和受棉蚜为害棉花叶片的高光谱图像,提取不同处理下棉花叶片的感兴趣区域光谱图像信息,并采用3种降维手段获取高光谱特征。利用灰度共生矩阵(Gray-Level Co-occurrence Matrix,GLCM)提取图像的纹理特征,构建棉蚜为害诊断模型。【结果】采用全纹理特征数据结合随机蛙跳-偏最小二乘线性判别分析模型(RF-PLS-LDA)建模得到的预测集识别率为91.49%;以能量(Energy)作为输入,建立主成分载荷-偏最小二乘线性判别分析模型(PCA-Loading-PLS-LDA),对预测集识别率达到92.55%。【结论】以灰度共生矩阵二阶统计量能量建模可有效地简化模型,减少计算量,提高预测的稳定性。基于纹理特征向量能有效地实现蚜害棉花叶片的识别,为虫情的快速识别提供技术支持。
[Objective]The aim of this research is to explore the possibility of rapid identification of aphid damage to cotton leaves by identifying the hyperspectral image about healthy and aphid damage cotton leaves.[Method]Taking Xinluzao 45 as material,hyperspectral images of healthy and damaged cotton leaves by aphids were obtained,and hyperspectral image information was extracted from interested regions of different cotton samples.Then three descending dimension methods were used to extract hyperspectral feature,and Gray-Level Co-occurrence Matrix to extract image texture feature.Finally a cotton aphids damage diagnostic model was built up.[Result]The prediction accuracy based on all textural samples input Random frog-partial least-square-linear discriminant Function(RF-PLS-LDA)model was 91.49%.The prediction accuracy based on energy data input principal component analysis-loading partial least-square-linear discriminant function(PCA-Loading-PLS-LDA)model was 92.55%.[Conclusion]The second-order statistics(energy)of gray co-occurrence matrix can be used to simplify the model,reduce the computation and improve the stability of prediction.Based on the texture feature vector,the identification of aphid cotton leaves can be realized effectively,which provides a method for the rapid identification of insect pests.
作者
许敬诚
吕新
林皎
张泽
姚秋双
范向龙
洪延宏
Xu Jingcheng;Lv Xin;Lin Jiao;Zhang Ze;Yao Qiushuang;Fan Xianglong;Hong Yanhong(Agriculture College of Shihezi University/The Key Laboratory of Oasis Eco-Agriculture,Xinjiang Production and Construction Group,Shihezi,Xinjiang 832003,China)
出处
《棉花学报》
CSCD
北大核心
2020年第2期133-142,共10页
Cotton Science
基金
新疆生产建设兵团重大科技项目(2018AA004)
中央引导地方科技发展专项资金(2019BT0826)。
关键词
棉花
棉蚜
高光谱成像
纹理特征
灰度共生矩阵
cotton
cotton aphid
hyperspectral imaging
textural features
Gray-Level Co-occurrence Matrix
