CNN Approaches for Classification of Indian Leaf Species Using Smartphones

被引:13
|
作者
Vilasini, M. [1 ]
Ramamoorthy, P. [2 ]
机构
[1] Kra Inst Engn & Technol, Coimbatore 641407, Tamil Nadu, India
[2] Adithiya Inst Engn & Technol, Coimbatore 641107, Tamil Nadu, India
来源
CMC-COMPUTERS MATERIALS & CONTINUA | 2020年 / 62卷 / 03期
关键词
Deep learning; CNN; classification; transfer learning; prewitt edge detection; SUPPORT VECTOR MACHINE; IMAGE TEXTURE; EDGE-DETECTION; RECOGNITION; SEGMENTATION; IDENTIFICATION; VEGETATION; FEATURES; PATTERN; SYSTEM;
D O I
10.32604/cmc.2020.08857
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Leaf species identification leads to multitude of societal applications. There is enormous research in the lines of plant identification using pattern recognition. With the help of robust algorithms for leaf identification, rural medicine has the potential to reappear as like the previous decades. This paper discusses CNN based approaches for Indian leaf species identification from white background using smartphones. Variations of CNN models over the features like traditional shape, texture, color and venation apart from the other miniature features of uniformity of edge patterns, leaf tip, margin and other statistical features are explored for efficient leaf classification.
引用
收藏
页码:1445 / 1472
页数:28
相关论文
共 50 条
  • [1] Leaf species and disease classification using multiscale parallel deep CNN architecture
    Russel, Newlin Shebiah
    Selvaraj, Arivazhagan
    NEURAL COMPUTING & APPLICATIONS, 2022, 34 (21): : 19217 - 19237
  • [2] Leaf species and disease classification using multiscale parallel deep CNN architecture
    Newlin Shebiah Russel
    Arivazhagan Selvaraj
    Neural Computing and Applications, 2022, 34 : 19217 - 19237
  • [3] Species classification from hyperspectral leaf information using machine learning approaches
    Song, Guangman
    Wang, Quan
    ECOLOGICAL INFORMATICS, 2023, 76
  • [4] Rice Leaf Diseases Classification Using CNN With Transfer Learning
    Ghosal, Shreya
    Sarkar, Kamal
    2020 IEEE CALCUTTA CONFERENCE (CALCON), 2020, : 230 - 235
  • [5] Automated Freshwater Fish Species Classification using Deep CNN
    Deka J.
    Laskar S.
    Baklial B.
    Journal of The Institution of Engineers (India): Series B, 2023, 104 (03) : 603 - 621
  • [6] EnConv: enhanced CNN for leaf disease classification
    M. Thanjaivadivel
    C. Gobinath
    J. Vellingiri
    S. Kaliraj
    J. S. Femilda Josephin
    Journal of Plant Diseases and Protection, 2025, 132 (1)
  • [7] Plant Species Classification Using Leaf Shape And Texture
    Zhang, Hang
    Yanne, Paul
    Liang, Shangsong
    2012 INTERNATIONAL CONFERENCE ON INDUSTRIAL CONTROL AND ELECTRONICS ENGINEERING (ICICEE), 2012, : 2025 - 2028
  • [8] Fungi affected fruit leaf disease classification using deep CNN architecture
    Gaikwad S.S.
    Rumma S.S.
    Hangarge M.
    International Journal of Information Technology, 2022, 14 (7) : 3815 - 3824
  • [9] Paddy Leaf Diseases Image Classification using Convolution Neural Network (CNN) Technique
    Zainorzuli, Siti Maisarah
    Abdullah, Syahrul Afzal Che
    Abidin, Husna Zainol
    Ruslan, Fazlina Ahmat
    19TH IEEE STUDENT CONFERENCE ON RESEARCH AND DEVELOPMENT (SCORED 2021), 2021, : 333 - 338
  • [10] TREE SPECIES CLASSIFICATION USING LEAF AND TREE TRUNK IMAGES
    Itakura, Kenta
    Hata, Teruhito
    Hosoi, Fumiki
    IGARSS 2020 - 2020 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM, 2020, : 4339 - 4342