Open Access
Issue
E3S Web Conf.
Volume 595, 2024
5th International Conference on Agribusiness and Rural Development (IConARD 2024)
Article Number 02006
Number of page(s) 11
Section Agricultural Technology and Smart Farming
DOI https://doi.org/10.1051/e3sconf/202459502006
Published online 22 November 2024
  1. S. Kurniawan, R. S. T. Putra, and …, “Android-Based Corn Plant Disease Diagnosis Application,” … Teknologi & Sains …, vol. 2, pp. 51–58, 2023, [Online]. Available: https://proceeding.unpkediri.ac.id/index.php/stains/article/download/2853/1988 [Google Scholar]
  2. M. Khoiruddin, A. Junaidi, and W. A. Saputra, “Classification of Rice Leaf Diseases Using Convolutional Neural Network,” Journal of Dinda: Data Science, Information Technology, and Data Analytics, vol. 2, no. 1, pp. 37–45, 2022, DOI: 10.20895/dinda.v2i1.341. [CrossRef] [Google Scholar]
  3. F. Ikorasaki and M. B. Akbar, “Detecting Corn Plant Disease with Expert System Using Bayes Theorem Method,” 2018 6th International Conference on Cyber and IT Service Management, CITSM 2018, no. Citsm, pp. 9–12, 2019, DOI: 10.1109/CITSM.2018.8674303. [Google Scholar]
  4. A. Hidayat, U. Darusalam, and I. Irmawati, “Detection of Disease on Corn Plants Using Convolutional Neural Network Methods,” Jurnal Ilmu Komputer dan Informasi, vol. 12, no. 1, p. 51, 2019, DOI: 10.21609/jiki.v12i1.695. [CrossRef] [Google Scholar]
  5. N. A. Utama, W. I. Triyani, S. Riyadi, and C. Damarjati, “Discrete Curvelet Transform Feature Extraction for Mangosteen Fruit Surface Damage Detection,” Emerging Information Science and Technology, vol. 5, no. 1, pp. 46–51, 2024, DOI: 10.18196/eist.v5i1.22602. [CrossRef] [Google Scholar]
  6. B. Mehlig, “Convolutional Networks,” Machine Learning with Neural Networks, pp. 141–156, 2021, DOI: 10.1017/9781108860604.008. [CrossRef] [Google Scholar]
  7. F. Adnan, M. J. Awan, A. Mahmoud, H. Nobanee, A. Yasin, and A. M. Zain, “EfficientNetB3-Adaptive Augmented Deep Learning (AADL) for Multi-Class Plant Disease Classification,” IEEE Access, vol. 11, no. July, pp. 85426–85440, 2023, DOI: 10.1109/ACCESS.2023.3303131. [CrossRef] [Google Scholar]
  8. H. Farman, J. Ahmad, B. Jan, Y. Shahzad, M. Abdullah, and A. Ullah, “Efficientnet- based robust recognition of peach plant diseases in field images,” Computers, Materials and Continua, vol. 71, no. 1, pp. 2073–2089, 2022, DOI: 10.32604/cmc.2022.018961. [CrossRef] [Google Scholar]
  9. C. Buiu, V. R. Dănăilă, and C. N. Răduţă, “MobileNetV2 ensemble for cervical precancerous lesions classification,” Processes, vol. 8, no. 5, 2020, DOI: 10.3390/PR8050595. [CrossRef] [Google Scholar]
  10. A. S. Ardiansyah and A. Nugroho, “Classification of Coffee Leaf Diseases Using MobileNetV2 Architecture,” Journal of Computer Science and Business, vol. 14, no. 1, pp. 66–73, 2023, DOI: 10.47927/jikb.v14i1.622. [Google Scholar]
  11. M. Akay et al., “Deep Learning Classification of Systemic Sclerosis Skin Using the MobileNetV2 Model,” IEEE Open J Eng Med Biol, vol. 2, pp. 104–110, 2021, DOI: 10.1109/OJEMB.2021.3066097. [Google Scholar]
  12. D. Putri Ayuni, Jasril, M. Irsyad, F. Yanto, and S. Sanjaya, “Data Augmentation in the Implementation of Convolutional Neural Network Efficientnet-B3 Architecture for Classification of Rice Leaf Diseases,” ZONAsi: Journal of Information Systems, vol. 5, no. 2, pp. 239–249, 2023, DOI: 10.31849/zn.v5i2.13874. [Google Scholar]
  13. B. Widianto, E. Utami, and D. Ariatmanto, “Identification of Corn Plant Diseases Based on Leaf Image Using Convolutional Neural Network,” Techno.Com, vol. 22, no. 3, pp. 599–608, 2023, DOI: 10.33633/tc.v22i3.8425. [CrossRef] [Google Scholar]
  14. A. B. Prakosa, Hendry, and R. Tanone, “Implementation of a Deep Learning Convolutional Neural Network (Cnn) Model on Corn Leaf Disease Images,” Jurnal Pendidikan Teknologi Informasi (JUKANTI), vol. 6, no. 1, pp. 107–116, 2023. [CrossRef] [Google Scholar]
  15. T. S. Winanto, C. Rozikin, and A. Jamaludin, “Performance Analysis of Transfer Learning Architecture for Identifying Leaf Diseases in Food Plants,” Journal of Applied Informatics and Computing, vol. 7, no. 1, pp. 68–81, 2023, DOI: 10.30871/jaic.v7i1.5991. [CrossRef] [Google Scholar]
  16. S. Aravind, S. Harini, and V. K. Kumar, “Cassava leaf disease classification using Deep Learning,” Volatiles & Essent. Oils, vol. 8, no. 5, pp. 9375–9389, 2021. [Google Scholar]
  17. M. I. Rosadi and M. Lutfi, “Identify Types of Corn Leaf Disease Using Deep Learning Pre-Trained Model,” Jurnal Explore IT!, vol. 13, no. 2, pp. 36–42, 2021, [Online]. Available: https://doi.org/10.35891/explorit [Google Scholar]
  18. R. Mawarni, R. Wulaningrum, and R. Helilintar, “Implementation of the CNN Method in Corn Disease Classification,” vol. 7, pp. 1256–1263, 2023. [Google Scholar]
  19. J. Cai et al., “Improved EfficientNet for corn disease identification,” Front Plant Sci, vol. 14, no. September, pp. 1–17, 2023, DOI: 10.3389/fpls.2023.1224385. [Google Scholar]
  20. Y. Gulzar, “Fruit Image Classification Model Based on MobileNetV2 with Deep Transfer Learning Technique,” Sustainability (Switzerland), vol. 15, no. 3, 2023, DOI: 10.3390/su15031906. [Google Scholar]
  21. B. Syamsuri and G. P. Kusuma, “Plant Disease Classification using Lite Pretrained Deep Convolutional Neural Network on Android Mobile Device,” International Journal of Innovative Technology and Exploring Engineering, vol. 9, no. 2, pp. 2796–2804, 2019, DOI: 10.35940/ijitee.b6647.129219. [CrossRef] [Google Scholar]
  22. E. Elfatimi, R. Eryigit, and L. Elfatimi, “Beans Leaf Diseases Classification Using MobileNet Models,” IEEE Access, vol. 10, pp. 9471–9482, 2022, DOI: 10.1109/ACCESS.2022.3142817. [CrossRef] [Google Scholar]
  23. A. Of, “Tomato Leaf Disease Detection Using Cutting-Edge Deep Learning,” vol. 66, no. 1, pp. 4320–4332, 2023. [Google Scholar]
  24. Ü. Atila, M. Uçar, K. Akyol, and E. Uçar, “Plant leaf disease classification using EfficientNet deep learning model,” Ecol Inform, vol. 61, no. October 2020, p. 101182, 2021, DOI: 10.1016/j.ecoinf.2020.101182. [CrossRef] [Google Scholar]

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