Today there is a rapid development taking place in phenotyping of plants using non-destructive image based machine vision techniques.Machine vision based plant phenotyping ranges from single plant trait estimation to ...Today there is a rapid development taking place in phenotyping of plants using non-destructive image based machine vision techniques.Machine vision based plant phenotyping ranges from single plant trait estimation to broad assessment of crop canopy for thousands of plants in the field.Plant phenotyping systems either use single imaging method or integrative approach signifying simultaneous use of some of the imaging techniques like visible red,green and blue(RGB)imaging,thermal imaging,chlorophyll fluorescence imaging(CFIM),hyperspectral imaging,3-dimensional(3-D)imaging or high resolution volumetric imaging.This paper provides an overview of imaging techniques and their applications in the field of plant phenotyping.This paper presents a comprehensive survey on recent machine vision methods for plant trait estimation and classification.In this paper,information about publicly available datasets is provided for uniform comparison among the state-of-the-art phenotyping methods.This paper also presents future research directions related to the use of deep learning based machine vision algorithms for structural(2-D and 3-D),physiological and temporal trait estimation,and classification studies in plants.展开更多
针对无人机精确植保过程中,果树冠层区域颜色特征和杂草相似度较高、难以分割等问题,采用基于超像素特征向量的果树冠层分割方法,以消除不同杂草特征对树冠分离的干扰,减小农药喷雾区域,节省农药使用量。通过分析无人机采集合成的样本...针对无人机精确植保过程中,果树冠层区域颜色特征和杂草相似度较高、难以分割等问题,采用基于超像素特征向量的果树冠层分割方法,以消除不同杂草特征对树冠分离的干扰,减小农药喷雾区域,节省农药使用量。通过分析无人机采集合成的样本图像在HSV彩色空间上色调与饱和度的分布情况,选取合适的阈值范围,提取样本图像中包含果树冠层与杂草的绿色区域,将提取的绿色区域RGB图像转换生成Lab和HSV彩色空间模型下的图像,然后运用简单的线性迭代聚类(Simple linear iterative clustering,SLIC)超像素分割算法将RGB图像预设分割成250个超像素单元,结合超像素的分割信息与RGB图像、Lab图像、HSV图像以及灰度图,提取超像素单元的特征向量,随机选取25%的超像素样本的特征向量作为SVM分类器的训练集,利用SVM分类器对所有样本进行预测分类,实现果树冠层与杂草分割。将基于超像素特征向量的方法和基于光谱阈值、K-means聚类的2种方法进行对比分析,结果显示,基于超像素特征向量的方法在识别果树冠层位置方面生产者精度为90.83%,在提取果树冠层轮廓上F测度值为87.62%,总体分割性能优于后两种方法。说明,基于超像素特征向量的方法能够较为准确地分割果树冠层与杂草,为实现无人机在果园中精确植保提供重要支撑。展开更多
Food security for the 7 billion people on earth requires minimizing crop damage by timely detectionofdiseases.Most deep learningmodels forautomated detectionof diseases in plants suffer fromthe fatal flaw that once te...Food security for the 7 billion people on earth requires minimizing crop damage by timely detectionofdiseases.Most deep learningmodels forautomated detectionof diseases in plants suffer fromthe fatal flaw that once tested on independent data,their performance drops significantly.This work investigates a potential solution to this problem by using segmented image data to train the convolutional neural network(CNN)models.As compared to the F-CNN model trained using full images,S-CNN model trained using segmented imagesmore than doubles in performance to 98.6%accuracy when tested on independent data previously unseen by the models even with 10 disease classes.Not only this,by using tomato plant and target spot disease type as an example,we show that the confidence of self-classification for S-CNN model improves significantly over F-CNN model.This research work brings applicability of automated methods closer to non-experts for timely detection of diseases.展开更多
文摘Today there is a rapid development taking place in phenotyping of plants using non-destructive image based machine vision techniques.Machine vision based plant phenotyping ranges from single plant trait estimation to broad assessment of crop canopy for thousands of plants in the field.Plant phenotyping systems either use single imaging method or integrative approach signifying simultaneous use of some of the imaging techniques like visible red,green and blue(RGB)imaging,thermal imaging,chlorophyll fluorescence imaging(CFIM),hyperspectral imaging,3-dimensional(3-D)imaging or high resolution volumetric imaging.This paper provides an overview of imaging techniques and their applications in the field of plant phenotyping.This paper presents a comprehensive survey on recent machine vision methods for plant trait estimation and classification.In this paper,information about publicly available datasets is provided for uniform comparison among the state-of-the-art phenotyping methods.This paper also presents future research directions related to the use of deep learning based machine vision algorithms for structural(2-D and 3-D),physiological and temporal trait estimation,and classification studies in plants.
文摘针对无人机精确植保过程中,果树冠层区域颜色特征和杂草相似度较高、难以分割等问题,采用基于超像素特征向量的果树冠层分割方法,以消除不同杂草特征对树冠分离的干扰,减小农药喷雾区域,节省农药使用量。通过分析无人机采集合成的样本图像在HSV彩色空间上色调与饱和度的分布情况,选取合适的阈值范围,提取样本图像中包含果树冠层与杂草的绿色区域,将提取的绿色区域RGB图像转换生成Lab和HSV彩色空间模型下的图像,然后运用简单的线性迭代聚类(Simple linear iterative clustering,SLIC)超像素分割算法将RGB图像预设分割成250个超像素单元,结合超像素的分割信息与RGB图像、Lab图像、HSV图像以及灰度图,提取超像素单元的特征向量,随机选取25%的超像素样本的特征向量作为SVM分类器的训练集,利用SVM分类器对所有样本进行预测分类,实现果树冠层与杂草分割。将基于超像素特征向量的方法和基于光谱阈值、K-means聚类的2种方法进行对比分析,结果显示,基于超像素特征向量的方法在识别果树冠层位置方面生产者精度为90.83%,在提取果树冠层轮廓上F测度值为87.62%,总体分割性能优于后两种方法。说明,基于超像素特征向量的方法能够较为准确地分割果树冠层与杂草,为实现无人机在果园中精确植保提供重要支撑。
文摘Food security for the 7 billion people on earth requires minimizing crop damage by timely detectionofdiseases.Most deep learningmodels forautomated detectionof diseases in plants suffer fromthe fatal flaw that once tested on independent data,their performance drops significantly.This work investigates a potential solution to this problem by using segmented image data to train the convolutional neural network(CNN)models.As compared to the F-CNN model trained using full images,S-CNN model trained using segmented imagesmore than doubles in performance to 98.6%accuracy when tested on independent data previously unseen by the models even with 10 disease classes.Not only this,by using tomato plant and target spot disease type as an example,we show that the confidence of self-classification for S-CNN model improves significantly over F-CNN model.This research work brings applicability of automated methods closer to non-experts for timely detection of diseases.
