A Highly Effective Deep Learning Tool for Identifying Plant Leaves
DOI:
https://doi.org/10.7160/aol.2025.170401How to Cite
Abstract
This work addresses pattern recognition in the agronomic domain, with a particular emphasis on identifying plant leaves using an adaptive neural network technique. We introduce a tool designed for two primary groups: botany researchers and a broader range of scientists applying it to plant identification and classification. We delve into the capabilities of Deep Learning, focusing on generalization abilities that enable accurate predictions on unseen data, which is essential for handling the variation in leaf shapes, sizes, and structures across species. The implementation details of these neural networks are described, including data preprocessing, network architecture design, training strategies, and evaluation techniques to ensure robustness and reliability in real-world applications.References
Aanis, A., Dharmendra, S. and Aly, E. (2023) "A survey on using deep learning techniques for plant disease diagnosis and recommendations for development of appropriate tools", Smart Agricultural Technology, Vol. 3., p. 100083. ISSN 2666-0159. DOI: https://doi.org/10.1016/j.atech.2022.100083
Ahmed, A. A. and Gopireddy, H. R. (2021) "A Mobile-Based System for Detecting Plant Leaf Diseases Using Deep Learning", AgriEngineering, Vol. 3, No. 3, pp. 478-493. ISSN 2673-1906 DOI: https://doi.org/10.3390/agriengineering3030032
Alqahtani, M., Nawaz, Y., Nazir, T., Javed, A., Jeribi, F. and Tahir, A. (2023) "An improved deep learning approach for localization and recognition of plant leaf diseases", Expert Systems with Applications, Vol. 230, p. 120717. ISSN 0957-4174 DOI: https://doi.org/10.1016/j.eswa.2023.120717
Chad, D., Tyr, W., Siyuan, C., Ethan, L., Jason, Y., Michael, A., Rebecca, J. and Hod, L. (2017) "Automated Identification of Northern Leaf Blight-Infected Maize Plants from Field Imagery Using Deep Learning", Phytopathology, Vol. 107, No. 11. ISSN 0031-949X DOI: https://doi.org/10.1094/PHYTO-11-16-0417-R
Guoqiang, Z., Lina, W., Xiao, L. and Junyu, D. (2016) "An overview on data representation learning: From traditional feature learning to recent deep learning", The Journal of Finance and Data Science, Vol. 2, No. 4, pp. 265-278. ISSN 2405-9188 DOI: https://doi.org/10.1016/j.jfds.2017.05.001
Jackulin, C. and Murugavalli, S. (2022) "A comprehensive review on detection of plant disease using machine learning and deep learning approaches", Measurement: Sensors, Vol. 24. p. 100441. ISSN 2665-9099 DOI: https://doi.org/10.1016/j.measen.2022.100441
Konstantinos, P. F. (2018) "Deep learning models for plant disease detection and diagnosis", Computers and Electronics in Agriculture, Vol. 145, pp. 311-318. ISSN 0168-1699 DOI: https://doi.org/10.1016/j.compag.2018.01.00
Lee, S. H., Chan, C. S., Mayo, S. J. and Remagnino, P. (2017) "How deep learning extracts and learns leaf features for plant classification", Pattern Recognition, Vol. 71, pp. 1-13. ISSN 0031-3203 DOI: https://doi.org/10.1016/j.patcog.2017.05.015
Li, W., Sun, Y., Li, J., Wang, H. and Zhang, C. (2023) "A multi-scale feature fusion convolutional neural network for high-performance plant leaf recognition", Computers and Electronics in Agriculture, Vol. 214, p. 108294. ISSN 0168-1699 DOI: https://doi.org/10.1016/j.compag.2023.108294
Mufeng, T., Tommaso, S., Beren, M., Yuhang, S., Thomas, L. and Rafal, B. (2023) "Recurrent predictive coding models for associative memory employing covariance learning", PLoS Computational Biology, Vol. 19, No. 3, p. e1010719. ISSN 1553-7358 DOI: https://doi.org/10.1371/journal.pcbi.1010719
Nazir, T., Iqbal, M. M., Jabbar, S., Hussain, A. and Albathan, M. (2023) "EfficientPNet —An Optimized and Efficient Deep Learning Approach for Classifying Disease of Potato Plant Leaves", Agriculture, Vol. 13, No. 4, p. 841. ISSN 2227-7072 DOI: https://doi.org/10.3390/agriculture13040841
Wani, M. A., Ahmad Bhat, M. A., Saduf, F. and Asif Iqbal, K. A. (2019) "Basics of Supervised Deep Learning", In: Advances in Deep Learning, Chapter 2, pp. 13-29. ISBN-13 978-9811367939 DOI: https://doi.org/10.1007/978-981-13-6794-6_2








