TY - JOUR
T1 - Deep Learning-Based Plant-Image Classification Using a Small Training Dataset
AU - Batchuluun, Ganbayar
AU - Nam, Se Hyun
AU - Park, Kang Ryoung
N1 - Publisher Copyright:
© 2022 by the authors.
PY - 2022/9
Y1 - 2022/9
N2 - Extensive research has been conducted on image augmentation, segmentation, detection, and classification based on plant images. Specifically, previous studies on plant image classification have used various plant datasets (fruits, vegetables, flowers, trees, etc., and their leaves). However, existing plant-based image datasets are generally small. Furthermore, there are limitations in the construction of large-scale datasets. Consequently, previous research on plant classification using small training datasets encountered difficulties in achieving high accuracy. However, research on plant image classification based on small training datasets is insufficient. Accordingly, this study performed classification by reducing the number of training images of plant-image datasets by 70%, 50%, 30%, and 10%, respectively. Then, the number of images was increased back through augmentation methods for training. This ultimately improved the plant-image classification performance. Based on the respective preliminary experimental results, this study proposed a plant-image classification convolutional neural network (PI-CNN) based on plant image augmentation using a plant-image generative adversarial network (PI-GAN). Our proposed method showed the higher classification accuracies compared to the state-of-the-art methods when the experiments were conducted using four open datasets of PlantVillage, PlantDoc, Fruits-360, and Plants.
AB - Extensive research has been conducted on image augmentation, segmentation, detection, and classification based on plant images. Specifically, previous studies on plant image classification have used various plant datasets (fruits, vegetables, flowers, trees, etc., and their leaves). However, existing plant-based image datasets are generally small. Furthermore, there are limitations in the construction of large-scale datasets. Consequently, previous research on plant classification using small training datasets encountered difficulties in achieving high accuracy. However, research on plant image classification based on small training datasets is insufficient. Accordingly, this study performed classification by reducing the number of training images of plant-image datasets by 70%, 50%, 30%, and 10%, respectively. Then, the number of images was increased back through augmentation methods for training. This ultimately improved the plant-image classification performance. Based on the respective preliminary experimental results, this study proposed a plant-image classification convolutional neural network (PI-CNN) based on plant image augmentation using a plant-image generative adversarial network (PI-GAN). Our proposed method showed the higher classification accuracies compared to the state-of-the-art methods when the experiments were conducted using four open datasets of PlantVillage, PlantDoc, Fruits-360, and Plants.
KW - PI-CNN
KW - PI-GAN
KW - deep learning
KW - image augmentation
KW - plant image classification
UR - http://www.scopus.com/inward/record.url?scp=85137822093&partnerID=8YFLogxK
U2 - 10.3390/math10173091
DO - 10.3390/math10173091
M3 - Article
AN - SCOPUS:85137822093
SN - 2227-7390
VL - 10
JO - Mathematics
JF - Mathematics
IS - 17
M1 - 3091
ER -