By
Sayed Abdulhayan,
Ahmed Uvais,
Fathimath Afreena,
Ifrath Begum,
Mariam Reema,
- Professor, P A College of Engineering, Mangalore, India
- Student, P A College of Engineering, Mangalore, India
- Student, P A College of Engineering, Mangalore, India
- Student, P A College of Engineering, Mangalore, India
- Student, P A College of Engineering, Mangalore, India
Abstract
Machine learning techniques, including traditional (shallow) ML, deep learning (DL), and augmented learning (AL), are being increasingly utilized for leaf disease classification. These methods involve feature extraction, data augmentation, and transfer learning to enhance model effectiveness and reduce the need for labeled data. The success of machine learning approaches in this domain hinges on the quality and quantity of data available. LeafGuard is a cutting-edge device with intelligent sensing systems for plant health monitoring and maintenance. LeafGuard provides a comprehensive solution for detecting and resolving plant health issues through the use of sophisticated sensors, machine learning algorithms, and real-time data processing. This article explores the features, advantages, and possible uses of LeafGuard in horticulture and agriculture, as well as its operating principles.
Keywords: Machine learning techniques. deep learning, augmented learning, including traditional (shallow) ML
How to cite this article: Sayed Abdulhayan, Ahmed Uvais, Fathimath Afreena, Ifrath Begum, Mariam Reema. Leafguard: Smart Plant Health Detection. Recent Trends in Electronics Communication Systems. 2024; ():-.
How to cite this URL: Sayed Abdulhayan, Ahmed Uvais, Fathimath Afreena, Ifrath Begum, Mariam Reema. Leafguard: Smart Plant Health Detection. Recent Trends in Electronics Communication Systems. 2024; ():-. Available from: https://journals.stmjournals.com/rtecs/article=2024/view=166922
References
- Qin J, Hu T, Yuan J, Liu Q, Wang W, Liu J, Guo L, Song G. Deep-Learning-Based Rice Phenological Stage Recognition. Remote Sensing. 2023 Jun 1;15(11):2891.
- Ganatra N, Patel A. Performance analysis of fine-tuned convolutional neural network models for plant disease classification. International Journal of Control and Automation. 2020 Jan;13(3):293-305.
- Kaur P, Harnal S, Tiwari R, Upadhyay S, Bhatia S, Mashat A, Alabdali AM. Recognition of leaf disease using hybrid convolutional neural network by applying feature reduction. Sensors. 2022 Jan 12;22(2):575.
- Guo Y, Zhang J, Yin C, Hu X, Zou Y, Xue Z, Wang W. Plant disease identification based on deep learning algorithm in smart farming. Discrete Dynamics in Nature and Society. 2020;2020(1):2479172.
- Abid HM, Khan N, Hussain A, Anis ZB, Nadeem M, Khalid N. Quantitative and qualitative approach for accessing and predicting food safety using various web-based tools. Food Control. 2024 Mar 25:110471.
- Kadir, L. E. Nugroho, A. Susanto and P. I. Santosa, “Neural network application on foliage plant identification,” International Journal of Computer Applications, vol. 29, no. 9, pp. 15-22, 2011, doi: 10.5120/3592-4981. preventive effects. Frontiers in Pain Resear 10.3389/fpain.2023.1247313.
- Rajani and M. N. Veena, “Study on Identification and Classification on Medicinal Plants,” International Journal of Advances in Science and Engineering, vol. 6, no. 2, pp. 13-17, 2018.
- Sabu, K. Sreekumar and R. R. Nair, “Recognition of ayurvedic medicinal plants from leaves: A computer vision approach,” 2017 Fourth International Conference on Image Information Processing (ICIIP), Shimla,India.
- H. Lee, C. S. Chan, P. Wilkin and P. Remagnino, “Deep-plant: Plant Identification with convolutional neural networks,” 2015 IEEE International Conference on Image Processing (ICIP), Quebec, Canada,2015,pp.99-113.
- S. Kumar and V. Talasila, “Recognition of medicinal plants based on its leaf features,” Systems Thinking Approach for Social Problems, vol. 327, 2015, pp. 99- 113, doi: 10.1007/978-81-322-2141-
8_9.
- S. Sainin, T. K. Ghazali and R. Alfred, “Malaysian medicinal plant leaf shape identification and classification,” Proceedings of the Knowledge Management International Conference (KMICe), Langkawi, Malaysia, 2014, pp. 1-6.
- Gao and X. Lin, “A study on the automatic recognition of medicinal plants,” 2012 2nd International Conference on Consumer Electronics, Communications and Networks (CECNet), Yichang, China,2012,pp. 101-103.
- Arun, W. R. S. Emmanuel, and D. C. Durairaj, “Texture feature extraction for identification of medicinal plants and comparison of different classifiers,” International Journal of Computer Applications, vol. 62, no. 12, pp. 1-9, 2013, doi: 5120/10129-
- A. F. Azlah, L. S. Chua, F. R. Rahmad, F. I. Abdullah and S. R. W. Alwi, “Review on Techniques for Plant Leaf Classification and Recognition,” Computers, vol. 8, no. 4,pp.1-22,2019,Art No.77,doi:10.3390/computers80442.
- Szegedy, V. Vanhoucke, S. Ioffe, J. Shlens and Z. Wojna, “Rethinking the Inception architecture for computer vision,” 2016 IEEE Conference on Computer Vision and PatternRecognition (CVPR),vol.1,2015,pp.2818-2826,doi: 10.1109/CVPR.2016.308
Ahead of Print Subscription Review Article
Recent Trends in Electronics Communication Systems
ISSN: 2393-8757
| Volume | |
| Received | June 18, 2024 |
| Accepted | July 20, 2024 |
| Published | August 14, 2024 |