Shape Recognition Based on Radial Basis Probabilistic Neural Network and Application to Plant Species Identification

2005 ◽  
pp. 281-285 ◽  
Author(s):  
Jixiang Du ◽  
Deshuang Huang ◽  
Xiaofeng Wang ◽  
Xiao Gu
Keyword(s):  
Neural Network ◽  
Species Identification ◽  
Plant Species ◽  
Probabilistic Neural Network ◽  
Shape Recognition ◽  
Radial Basis ◽  
Plant Species Identification

scholarly journals Plant Identification Using New Architecture Convolutional Neural Networks Combine with Replacing the Red of Color Channel Image by Vein Morphology Leaf

2020 ◽  
Vol 07 (02) ◽  
pp. 197-208
Author(s):  
Hiep Xuan Huynh ◽  
Bao Quoc Truong ◽  
Kiet Tan Nguyen Thanh ◽  
Dinh Quoc Truong
Keyword(s):  
Neural Network ◽  
Species Identification ◽  
Plant Species ◽  
Plant Identification ◽  
Color Channel ◽  
Data Set ◽  
Plant Species Identification ◽  
Channel Image ◽  
Shape Data

The determination of plant species from field observation requires substantial botanical expertise, which puts it beyond the reach of most nature enthusiasts. Traditional plant species identification is almost impossible for the general public and challenging even for professionals who deal with botanical problems daily such as conservationists, farmers, foresters, and landscape architects. Even for botanists themselves, species identification is often a difficult task. This paper proposes a model deep learning with a new architecture Convolutional Neural Network (CNN) for leaves classifier based on leaf pre-processing extract vein shape data replaced for the red channel of colors. This replacement improves the accuracy of the model significantly. This model experimented on collector leaves data set Flavia leaf data set and the Swedish leaf data set. The classification results indicate that the proposed CNN model is effective for leaf recognition with the best accuracy greater than 98.22%.

Periodic wavelet descriptor of plant leaf and its application in botany

2015 ◽  
Vol 13 (06) ◽  
pp. 1550043 ◽  
Author(s):  
Qing-Mao Zeng ◽  
Tong-Lin Zhu ◽  
Xue-Ying Zhuang ◽  
Ming-Xuan Zheng
Keyword(s):  
Species Identification ◽  
Plant Species ◽  
Shape Descriptor ◽  
Plant Leaf ◽  
Practical Applications ◽  
Wide Range ◽  
Periodic Wavelet ◽  
Plant Species Identification ◽  
Botanical Research ◽  
Wavelet Descriptor

Leaf is one of the most important organs of plant. Leaf contour or outline, usually a closed curve, is a fundamental morphological feature of leaf in botanical research. In this paper, a novel shape descriptor based on periodic wavelet series and leaf contour is presented, which we name as Periodic Wavelet Descriptor (PWD). The PWD of a leaf actually expresses the leaf contour in a vector form. Consequently, the PWD of a leaf has a wide range in practical applications, such as leaf modeling, plant species identification and classification, etc. In this work, the plant species identification and the leaf contour reconstruction, as two practical applications, are discussed to elaborate how to employ the PWD of a plant leaf in botanical research.

Intelligent Control of Flocculation Process Based on Radial Basis Probabilistic Neural Network for Sewage Treatment

2010 ◽  
Vol 44-47 ◽  
pp. 3289-3293
Author(s):  
Jing Wen Tian ◽  
Mei Juan Gao
Keyword(s):  
Neural Network ◽  
Control System ◽  
Process Model ◽  
Probabilistic Neural Network ◽  
Sewage Treatment ◽  
Sewage Treatment Plant ◽  
Treatment Plant ◽  
Least Square ◽  
Radial Basis ◽  
Flocculation Process

The flocculating process of sewage treatment is a complicated and nonlinear system, and it is very difficult to found the process model to describe it. The radial basis probabilistic neural network (RBPNN) has the ability of strong function approach and fast convergence. In this paper, an intelligent optimized control system based on radial basis probabilistic neural network is presented. We constructed the structure of radial basis probabilistic neural network that used for controlling the flocculation process, and adopt the K-Nearest Neighbor algorithm and least square method to train the network. We given the architecture of control system and analyzed the working process of system. In this system, the parameters of flocculation process were measured using sensors, and then the control system can control the flocculation process real-time. The system was used in the sewage treatment plant. The experimental results prove that this system is feasible.

scholarly journals Plant Species Identification by Bi-channel Deep Convolutional Networks

2018 ◽  
Vol 1004 ◽  
pp. 012015
Author(s):  
Guiqing He ◽  
Zhaoqiang Xia ◽  
Qiqi Zhang ◽  
Haixi Zhang ◽  
Jianping Fan
Keyword(s):  
Species Identification ◽  
Plant Species ◽  
Convolutional Networks ◽  
Plant Species Identification

scholarly journals Towards Plant Species Identification in Complex Samples: A Bioinformatics Pipeline for the Identification of Novel Nuclear Barcode Candidates

PLoS ONE ◽  
2016 ◽  
Vol 11 (1) ◽  
pp. e0147692 ◽  
Author(s):  
Alexandre Angers-Loustau ◽  
Mauro Petrillo ◽  
Valentina Paracchini ◽  
Dafni M. Kagkli ◽  
Patricia E. Rischitor ◽  
...  
Keyword(s):  
Species Identification ◽  
Plant Species ◽  
Bioinformatics Pipeline ◽  
Complex Samples ◽  
Plant Species Identification

Mobile Vision for Plant Biometric System

2020 ◽  
pp. 773-791
Author(s):  
Shitala Prasad
Keyword(s):  
Ubiquitous Computing ◽  
Species Identification ◽  
Plant Species ◽  
Expert Knowledge ◽  
Biometric System ◽  
Plant Identification ◽  
Significant Growth ◽  
Wide Range ◽  
Plant Species Identification ◽  
Research Analysis

In human's life plant plays an important part to balance the nature and supply food-&-medicine. The traditional manual plant species identification method is tedious and time-consuming process and requires expert knowledge. The rapid developments of mobile and ubiquitous computing make automated plant biometric system really feasible and accessible for anyone-anywhere-anytime. More and more research are ongoing to make it a more realistic tool for common man to access the agro-information by just a click. Based on this, the chapter highlights the significant growth of plant identification and leaf disease recognition over past few years. A wide range of research analysis is shown in this chapter in this context. Finally, the chapter showed the future scope and applications of AaaS and similar systems in agro-field.

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