Optimized convolutional neural network model for plant species identification from leaf images using computer vision

In recent works of computer science, especially in the fields of image processing and pattern recognition techniques with machine learning, considerable focus is given to plant taxonomy which enhances the abilities of people to recognize plant species. This paper presents a method that analyzes colo...

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Vydané v:International journal of speech technology Ročník 26; číslo 1; s. 23 - 50
Hlavní autori: Reddy, Satti R. G., Varma, G. P. Saradhi, Davuluri, Rajya Lakshmi
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: New York Springer US 01.03.2023
Springer Nature B.V
Predmet:
Artificial Intelligence
Artificial neural networks
Classification
Color imagery
Computer science
Computer vision
Deep learning
Engineering
Image processing
Machine learning
Networks
Neural networks
Pattern recognition
Plants (botany)
Signal,Image and Speech Processing
Social Sciences
Taxonomy
Flavia
Convolutional Neural Network
Plant village
Leaf snap
Plant species identification
UCI
Swedish
ISSN:1381-2416, 1572-8110
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Shrnutí:In recent works of computer science, especially in the fields of image processing and pattern recognition techniques with machine learning, considerable focus is given to plant taxonomy which enhances the abilities of people to recognize plant species. This paper presents a method that analyzes color images of leaves using a type of Convolutional Neural Network to recognize plant species. The proposed Neural Network consists of four convolutional layers followed by two Fully-Connected layers and a final soft-max layer to offer a feature representation for different plant species. Four max-pooling layers are performed over a 2 × 2 pixel window with stride 2. Results on five plant datasets viz. Leaf snap (52 plant species), UCI leaf (40 plant species), PlantVillage (38 plant species), Flavia (32 plant species) and Swedish (15 plant species) are tabulated that demonstrate the remarkable performance of the proposed deep neural network when compared to the state of art methods.
Bibliografia:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:1381-2416
1572-8110
DOI:10.1007/s10772-021-09843-x