Monitoring of tomato plant health through convolutional neural networks computer engineering

Authors

  • Shivani Shinde S PG–Computer Engineering Student, Department of Computer Engineering, SND College of Engineering and Research Center Yeola, Dist Nashik, MS India
  • Umesh B Pawar Professor and Head, Department of Computer Engineering, SND College of Engineering and Research Center Yeola, Dist Nashik, MS India
  • Ramesh P Daund PG Coordinator and System Admin, Department of Computer Engineering, SND College of Engineering and Research Center Yeola, Dist Nashik, MS India
  • Ravindra Pandit Asst. Professor, Department of Computer Engineering, SND College of Engineering and Research Center Yeola, Dist Nashik, MS India

DOI:

https://doi.org/10.48165/jefa.2024.19.02.37

Keywords:

Convolutional neural networks (CNNS), open-source algorithms, early disease detection, plant leaf diseases

Abstract

 

The study conducted with the aim of providing an in-depth understanding of the state-of-the-art technologies, their strengths, limitations, and potential areas for improvement proposes a comprehensive exploration of methodologies for the early identification of tomato plant leaf diseases, emphasizing the integration of advanced image processing techniques, convolutional neural networks (CNNs) and open-source algorithms. The culmination of this survey contributes to the development of a dependable, secure, and precise framework tailored to the specificities of tomato plant diseases. The insights derived are poised to inform and guide future research endeavours, offering a holistic perspective on the advancements in early disease detection and predictive mechanisms within the realm of agricultural practices. 

Downloads

Download data is not yet available.

References

S. Ahmed, M. B. Hasan, T. Ahmed, M. R. Sony, and M. H. Kabir, “Less is more: Lighter and faster deep neural architecture for tomato leaf disease classification,” IEEE Access, vol. 10, p. 68868–68884, 2022.

A. Saeed, A. A. Abdel-Aziz, A. Mossad, M. A. Abdelhamid, A. Y. Alkhaled, and M. Mayhoub, “Smart detection of tomato leaf diseases using transfer learning-based convolutional neural networks,” Agriculture, vol. 13, no. 1, p. 139, 2023.

R. Raja Kumar, J. Athimoolam, A. Appathurai, and S. Rajendiran, “Novel segmentation and classification algorithm for detection of tomato leaf disease,” Concurrency and Computation: Practice and Experience, vol. 35, no. 12, 2023.

S. H. Lee, H. Go¨eau, P. Bonnet, and A. Joly, “New perspectives on plant disease characterization based on deep learning,” Computers and Electronics in Agriculture, vol. 170, p. 105220, 2020.

H. Nazki, S. Yoon, A. Fuentes, and D. S. Park, “Unsupervised image translation using adversarial networks for improved plant disease recognition,” Computers and Electronics in Agriculture, vol. 168, p. 105117, 2020.

K. P. Ferentinos, “Deep learning models for plant disease detection and diagnosis,” Computers and Electronics in Agriculture, vol. 145, p. 311–318, 2018.

M. Agarwal, S. K. Gupta, and K. Biswas, “Development of efficient cnn model for tomato crop disease identification,” Sustainable Computing: Informatics and Systems, vol. 28, p. 100407, 2020.

P. Wspanialy and M. Moussa, “A detection and severity estimation system for generic diseases of tomato greenhouse plants,” Computers and Electronics in Agriculture, vol. 178, p. 105701, 2020.

Pawar, U.B., Bhirud, S.G., Kolhe, S.R. (2020). Light Scattering Study on Protocols and Simulators Used in Automotive Application(s). In: Iyer, B., Deshpande, P., Sharma, S., Shiurkar, U. (eds) Computing in Engineering and Technology. Advances in Intelligent Systems and Computing, vol 1025. Springer, Singapore. https://doi.org/10.1007/978-981-32-9515-5_16

A. Ali, “Plantvillage dataset,” Sep 2019

Downloads

Published

2024-07-02

Issue

Vol. 19 No. 2 (2024): Journals Journal of Eco-friendly Agriculture (July)

Section

Articles

How to Cite

Shinde S, S., Pawar, U.B., Daund, R.P., & Pandit, R. (2024). Monitoring of tomato plant health through convolutional neural networks computer engineering. Journal of Eco-Friendly Agriculture, 19(2), 458–463. https://doi.org/10.48165/jefa.2024.19.02.37
Crossref
Scopus
Google Scholar
Europe PMC