Large-Scale Plant Classification with Deep Neural Networks

This paper discusses the potential of applying deep learning techniques for plant classification and its usage for citizen science in large-scale biodiversity monitoring. We show that plant classification using near state-of-the-art convolutional network architectures like ResNet50 achieves significant improvements in accuracy compared to the most widespread plant classification application in test sets composed of thousands of different species labels. We find that the predictions can be confidently used as a baseline classification in citizen science communities like iNaturalist (or its Spanish fork, Natusfera) which in turn can share their data with biodiversity portals like GBIF.Comment: 5 pages, 3 figures, 1 table. Published at Proocedings of ACM Computing Frontiers Conference 201

Deep residual neural network for EMI event classification using bispectrum representation

This paper presents a novel method for condition monitoring of High Voltage (HV) power plant equipment through analysis of discharge signals. These discharge signals are measured using the Electromagnetic Interference (EMI) method and processed using third order Higher-Order Statistics (HOS) to obtain a Bispectrum representation. By mapping the time-domain signal to a Bispectrum image representations the problem can be approached as an image classification task. This allows for the novel application of a Deep Residual Neural Network (ResNet) to the classification of HV discharge signals. The network is trained on signals into 9 classes and achieves high classification accuracy in each category, improving upon our previous work on this task

Classification and Protection Status of Remnant Natural Plant Communities in Arkansas

A classification and inventory of Arkansas\u27s remaining tracts of relatively undisturbed vegetation was initiated in 1979. Based on extensive literature surveys and field work, the classification includes five physiognomic classes, 17 cover classes, and 46 cover types, arranged hierarchically. High quality examples of ten of the cover types have been located in designated wilderness or state natural areas, where they are protected by law, while an additional three occur in research natural areas or Forest Service special interest areas. The remaining 33 cover types have no known long-term protection. Lands having wilderness, state natural area, research natural area, or special management area status total nearly 51,000 acres in the state. No more than one-tenth of this area, however, supports vegetation in relatively undisturbed condition

Biometry Protocol

The purpose of this resource is to measure and classify the plant life at a Land Cover Site to help determine the MUC classification. Educational levels: Primary elementary, Middle school, High school, Intermediate elementary

A survey of computer representations of trees for realistic and efficient rendering

This paper gives an overview of computer graphics representations of trees commonly used for the rendering of complex scene of vegetation. Looking for the right compromise between realism and efficiency has lead researchers to consider various types of geometrical plant models with different types of complexity. To achieve realist plant model, a complex structure of plant with full details is generally considered. In contrast, to promote efficiency, other approaches summarize plant geometry with few primitives allowing rapid rendering. Finally, to find a good compromise, structures with adaptive complexity are defined. Theses different types of representations and the ways to use them are presented, classified and discussed. The proposed classification principles rely on the type of structural details used in the plants representations. Characterization of all these methods is completed with various additional criteria including rendering primitive type, distance validity, interactive possibilities, animation ability and lighting properties. (Résumé d'auteur

Drought Stress Classification using 3D Plant Models

Quantification of physiological changes in plants can capture different drought mechanisms and assist in selection of tolerant varieties in a high throughput manner. In this context, an accurate 3D model of plant canopy provides a reliable representation for drought stress characterization in contrast to using 2D images. In this paper, we propose a novel end-to-end pipeline including 3D reconstruction, segmentation and feature extraction, leveraging deep neural networks at various stages, for drought stress study. To overcome the high degree of self-similarities and self-occlusions in plant canopy, prior knowledge of leaf shape based on features from deep siamese network are used to construct an accurate 3D model using structure from motion on wheat plants. The drought stress is characterized with a deep network based feature aggregation. We compare the proposed methodology on several descriptors, and show that the network outperforms conventional methods.Comment: Appears in Workshop on Computer Vision Problems in Plant Phenotyping (CVPPP), International Conference on Computer Vision (ICCV) 201

A Leaf Recognition Algorithm for Plant Classification Using Probabilistic Neural Network

In this paper, we employ Probabilistic Neural Network (PNN) with image and data processing techniques to implement a general purpose automated leaf recognition algorithm. 12 leaf features are extracted and orthogonalized into 5 principal variables which consist the input vector of the PNN. The PNN is trained by 1800 leaves to classify 32 kinds of plants with an accuracy greater than 90%. Compared with other approaches, our algorithm is an accurate artificial intelligence approach which is fast in execution and easy in implementation.Comment: 6 pages, 3 figures, 2 table