📅 08 August 2023
DOI: 10.33633/jcta.v1i1.8877
Plant Diseases Classification based Leaves Image using Convolutional Neural Network
Journal of Computing Theories and Applications
Universitas Dian Nuswantoro
📄 Abstract
Plant disease is one of the problems in the world of agriculture. Early identification of plant diseases can reduce the risk of loss, so automation is needed to speed up identification. This study proposes a custom-designed convolutional neural network (CNN) model for plant disease recognition. The proposed CNN model is not complex and lightweight, so it can be implemented in model applications. The proposed CNN model consists of 12 CNN layers, which consist of eight layers for feature extraction and four layers as classifiers. Based on the experimental results of a plant disease dataset consisting of 38 classes with a total of 87,867 image records. The proposed model can get high performance and not overfitting, with 97%, 98%, 97% and 97%, respectively, for accuracy, precision, recall and f1-score. The performance of the proposed model is also better than some popular pre-trained models, such as InceptionV3 and MobileNetV2. The proposed model can also work well when implemented in mobile applications.
🔖 Keywords
#Agricultural technology; Ligh convolutional neural network; Image Recognition; Plant Diseases Classification; Transfer learning recognition
ℹ️ Informasi Publikasi
Tanggal Publikasi
08 August 2023
Volume / Nomor / Tahun
Volume 1, Nomor 1, Tahun 2023
📝 HOW TO CITE
Imanulloh, Satrio Bagus; Muslikh, Ahmad Rofiqul; Setiadi, De Rosal Ignatius Moses, "Plant Diseases Classification based Leaves Image using Convolutional Neural Network," Journal of Computing Theories and Applications, vol. 1, no. 1, Aug. 2023.
ACM
ACS
APA
ABNT
Chicago
Harvard
IEEE
MLA
Turabian
Vancouver
Download Citation
28
Sitasi
Cek Google Scholar
📚 References & Citations
Artikel ini telah dikutip oleh 28 publikasi lainnya.