This paper proposes an efficient method to extract the leaf region and count the number of leaves in digital plant images.The plant image analysis plays a significant role in viable and productive agriculture.It is us...This paper proposes an efficient method to extract the leaf region and count the number of leaves in digital plant images.The plant image analysis plays a significant role in viable and productive agriculture.It is used to record the plant growth,plant yield,chlorophyll fluorescence,plant width and tallness,leaf area,etc.frequently and accurately.Plant growth is a major character to be analyzed among these plant characters and it directly depends on the number of leaves in the plants.In this paper,a new method is presented for leaf region extraction from plant images and counting the number of leaves.The proposed method has three steps.The first step involves a new statistical based technique for image enhancement.The second step involves in the extraction of leaf region in plant image using a graph based method.The third step involves in counting the number of leaves in the plant image by applying Circular Hough Transform(CHT).The proposed work has been experimented on benchmark datasets of Leaf Segmentation Challenge(LSC).The proposed method achieves the segmentation accuracy of 95.4%and it also achieves the counting accuracy of0.7(DiC)and 2.3(|DiC|)for datasets(A1,A2 and A3),which are better than the state-of-the-art methods.展开更多
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
文摘This paper proposes an efficient method to extract the leaf region and count the number of leaves in digital plant images.The plant image analysis plays a significant role in viable and productive agriculture.It is used to record the plant growth,plant yield,chlorophyll fluorescence,plant width and tallness,leaf area,etc.frequently and accurately.Plant growth is a major character to be analyzed among these plant characters and it directly depends on the number of leaves in the plants.In this paper,a new method is presented for leaf region extraction from plant images and counting the number of leaves.The proposed method has three steps.The first step involves a new statistical based technique for image enhancement.The second step involves in the extraction of leaf region in plant image using a graph based method.The third step involves in counting the number of leaves in the plant image by applying Circular Hough Transform(CHT).The proposed work has been experimented on benchmark datasets of Leaf Segmentation Challenge(LSC).The proposed method achieves the segmentation accuracy of 95.4%and it also achieves the counting accuracy of0.7(DiC)and 2.3(|DiC|)for datasets(A1,A2 and A3),which are better than the state-of-the-art methods.
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.
