Fine-grained leaf classification has concentrated on the use of traditional shape and statistical features to classify ideal images. In this paper we evaluate the effectiveness of traditional hand-crafted features and propose the use of deep convolutional neural network (ConvNet) features. We introduce a range of condition variations to explore the robustness of these features, including: translation, scaling, rotation, shading and occlusion. Evaluations on the Flavia dataset demonstrate that in ideal imaging conditions, combining traditional and ConvNet features yields state-of-the- art performance with an average accuracy of 97 . 3% ± 0 . 6% compared to traditional features which obtain an average accuracy of 91 . 2% ± 1 . 6% . Further ex...
The automatic characterization and classification of plant species is an important task for plant ta...
International audienceTo recognize tree species from pictures of their leaves, one way is to automat...
Abstract Background Automated species identification is a long term research subject. Contrary to fl...
Fine-grained leaf classification has concentrated on the use of traditional shape and statistical fe...
In the modern era, deep learning techniques have emerged as powerful tools in image recognition. Con...
Recent progress in machine learning and deep learning has enabled the implementation of plant and cr...
The use of machine learning and computer vision methods for recognizing different plants from images...
Data augmentation plays a crucial role in increasing the number of training images, which often aids...
Plant leaf classification involves identifying and categorizing plant species based on leaf characte...
Modern scientific and technological advances allow botanists to use computer vision-based approaches...
Plant identification has applications in ethnopharmacology and agriculture. Since leaves are one of ...
Plant identification has applications in ethnopharmacology and agriculture. Since leaves are one of ...
The use of machine learning and computer vision methods for recognizing different plants from images...
The automatic characterization and classification of plant species is an important task for plant ta...
International audienceTo recognize tree species from pictures of their leaves, one way is to automat...
Abstract Background Automated species identification is a long term research subject. Contrary to fl...
Fine-grained leaf classification has concentrated on the use of traditional shape and statistical fe...
In the modern era, deep learning techniques have emerged as powerful tools in image recognition. Con...
Recent progress in machine learning and deep learning has enabled the implementation of plant and cr...
The use of machine learning and computer vision methods for recognizing different plants from images...
Data augmentation plays a crucial role in increasing the number of training images, which often aids...
Plant leaf classification involves identifying and categorizing plant species based on leaf characte...
Modern scientific and technological advances allow botanists to use computer vision-based approaches...
Plant identification has applications in ethnopharmacology and agriculture. Since leaves are one of ...
Plant identification has applications in ethnopharmacology and agriculture. Since leaves are one of ...
The use of machine learning and computer vision methods for recognizing different plants from images...
The automatic characterization and classification of plant species is an important task for plant ta...
International audienceTo recognize tree species from pictures of their leaves, one way is to automat...
Abstract Background Automated species identification is a long term research subject. Contrary to fl...