Plant Identification in a Combined Imbalanced Leaf Dataset
P.Pandi selvi
, A.Parameshwari
Convolutional Neural Network, Plant identification, Segmentation, Classification
Plant identification has applications in ethnopharmacology and agriculture. Since leaves are one of the distinguishable features of a plant, they are routinely used for identification. Recent developments in deep learning have made it possible to accurately identify the majority of samples in five publicly available leaf datasets. However, each dataset captures the images in a highly controlled environment. This paper evaluates the performance of Efficient Net and several other convolutional neural network (CNN) architectures when applied to a combination of the Leaf Snap, Middle European Woody Plants 2014, Flavia, Swedish, and Folio datasets. To normalize the impact of imbalance resulting from combining the original datasets, the authors used oversampling, under sampling, and transfer learning techniques to construct an end-to-end CNN classifier. Emphasis is placed greater on metrics appropriate for a diverse-imbalanced dataset rather than stressing high performance on any one of the original datasets. A model from Efficient Net’s family of CNN models achieved a highly accurate F-score of 0.9861 on the combined dataset.
"Plant Identification in a Combined Imbalanced Leaf Dataset", IJSDR - International Journal of Scientific Development and Research (www.IJSDR.org), ISSN:2455-2631, Vol.8, Issue 4, page no.2636 - 2640, April-2023, Available :https://ijsdr.org/papers/IJSDR2304406.pdf
Volume 8
Issue 4,
April-2023
Pages : 2636 - 2640
Paper Reg. ID: IJSDR_205593
Published Paper Id: IJSDR2304406
Downloads: 000347205
Research Area: Science
Country: Madurai, Tamilnadu, India
ISSN: 2455-2631 | IMPACT FACTOR: 9.15 Calculated By Google Scholar | ESTD YEAR: 2016
An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 9.15 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator
Publisher: IJSDR(IJ Publication) Janvi Wave
