2023
DOI: 10.3390/plants12142642
|View full text |Cite
|
Sign up to set email alerts
|

Plant-CNN-ViT: Plant Classification with Ensemble of Convolutional Neural Networks and Vision Transformer

Abstract: Plant leaf classification involves identifying and categorizing plant species based on leaf characteristics, such as patterns, shapes, textures, and veins. In recent years, research has been conducted to improve the accuracy of plant classification using machine learning techniques. This involves training models on large datasets of plant images and using them to identify different plant species. However, these models are limited by their reliance on large amounts of training data, which can be difficult to ob… Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
1
1
1
1

Citation Types

0
3
0
2

Year Published

2023
2023
2024
2024

Publication Types

Select...
9
1

Relationship

0
10

Authors

Journals

citations
Cited by 28 publications
(5 citation statements)
references
References 23 publications
0
3
0
2
Order By: Relevance
“…The Y-Net architecture features a dual-branch input, integrating a ResNet-based CNN [41, 42]. The dual input of Y-Net involves data augmentation or enrichment and pre-training through a novel two-stage training approach (Fig.…”
Section: Discussionmentioning
confidence: 99%
“…The Y-Net architecture features a dual-branch input, integrating a ResNet-based CNN [41, 42]. The dual input of Y-Net involves data augmentation or enrichment and pre-training through a novel two-stage training approach (Fig.…”
Section: Discussionmentioning
confidence: 99%
“…However, the network may not be able to perform effectively on all subsequent tasks contingent on the pretext task and dataset. Because the orientation of objects is not as rigorously practical to handle in remote sensing datasets as in object-centric datasets, the prediction of random rotations of an image would not perform particularly well on such a dataset [41].…”
Section: Predictive Ssl Paradigmsmentioning
confidence: 99%
“…Namun ketika sistem ini diuji dibawah kondisi lapangan yang sebenarnya, kinerjanya menurun tajam. Hal ini disebabkan karena perbedaan gambar laboratorium yang bersih tanpa latar belakang, dengan gambar yang dikumpulkan langsung di lapangan yang memiliki fitur latar belakang yang kompleks, termasuk batang, buah, tanah, dan daun sekitarnya [7] [8]. Batasan lain dari dataset Flavia dan Swedish Leaf seperti jumlah spesies tumbuhan yang terbatas dan sampel per spesies, serta variasi format dan kualitas gambar, juga dapat berkontribusi pada penurunan kinerja jaringan saraf dalam kondisi lapangan yang sebenarnya.…”
Section: Pendahuluanunclassified