Open Access
| Issue |
ITM Web Conf.
Volume 87, 2026
2nd International Conference on Computing Paradigms (ICCP-2026)
|
|
|---|---|---|
| Article Number | 01012 | |
| Number of page(s) | 8 | |
| DOI | https://doi.org/10.1051/itmconf/20268701012 | |
| Published online | 30 June 2026 | |
- L. L. Deng et al., “Application of agricultural insect pest detection and control map based on image processing analysis,” Journal of Intelligent & Fuzzy Systems, 2020. [Google Scholar]
- G. G. Kennedy et al., “Pest pressure relates to similarity of crops and native plants,” Proceedings of the National Academy of Sciences of the United States of America, 2022. [Google Scholar]
- B. M. Atta et al., “Application of fluorescence spectroscopy in wheat crop: Early disease detection and associated molecular changes,” Journal of Fluorescence, 2020. [Google Scholar]
- W. X. Bao et al., “Identification of wheat leaf diseases and their severity based on elliptical-maximum margin criterion metric learning,” Sustainable Computing: Informatics and Systems, 2021. [Google Scholar]
- V. Devisurya et al., “Early detection of major diseases in turmeric plant using improved deep learning algorithm,” Bulletin of the Polish Academy of Sciences: Technical Sciences, 2022. [Google Scholar]
- Z. C. Jiang et al., “Recognition of rice leaf diseases and wheat leaf diseases based on multi-task deep transfer learning,” Computers and Electronics in Agriculture, 2021. [Google Scholar]
- H. Wu et al., “Identification of wheat leaf rust resistance genes in Chinese wheat cultivars and the improved germplasms,” Plant Disease, 2020. [Google Scholar]
- R. D. Barman et al., “Comparison of convolution neural networks for smartphone image based real time classification of citrus leaf disease,” Computers and Electronics in Agriculture, 2020. [Google Scholar]
- K. Anitha et al., “Feature extraction and classification of plant leaf diseases using deep learning techniques,” CMC—Computers, Materials & Continua, 2022. [Google Scholar]
- A. Tursunov, J. Y. Choeh, and S. Kwon, “Age and gender recognition using a convolutional neural network with a specially designed multi-attention module through speech spectrograms,” Sensors, vol. 21, no. 17, Art. no. 5892, 2021. [Google Scholar]
- J. Zhao, Y. Fang, G. Chu, H. Yan, L. Hu, and L. Huang, “Identification of leaf-scale wheat powdery mildew (Blumeria graminis f. sp. Tritici) combining hyperspectral imaging and an SVM classifier,” Plants, vol. 9, no. 7, Art. no. 936, 2020. [Google Scholar]
- K. P. Panigrahi, H. Das, A. K. Sahoo, and S. C. Moharana, “Maize leaf disease detection and classification using machine learning algorithms,” in Progress in Computing, Analytics and Networking. Cham, Switzerland: Springer, 2020, pp. 659–669. [Google Scholar]
- K. Aurangzeb, F. Akmal, M. A. Khan, M. Sharif, and M. Y. Javed, “Advanced machine learning algorithm based system for crops leaf diseases recognition,” in Proc. IEEE 6th Conf. Data Science and Machine Learning Applications (CDMA), Riyadh, Saudi Arabia, Mar. 4–5, 2020. [Google Scholar]
- W. Ullah, K. Muhammad, I. Ul Haq, A. Ullah, S. Ullah Khattak, and M. Sajjad, “Splicing sites prediction of human genome using machine learning techniques,” Multimedia Tools and Applications, vol. 80, pp. 30439– 30460, 2021. [Google Scholar]
- A. Paul, S. Ghosh, A. K. Das, S. Goswami, S. D. Choudhury, and S. Sen, “A review on agricultural advancement based on computer vision and machine learning,” in Emerging Technology in Modelling and Graphics. Cham, Switzerland: Springer, 2020, pp. 567– 581. [Google Scholar]
- A. Wójtowicz, J. Piekarczyk, B. Czernecki, and H. Ratajkiewicz, “A random forest model for the classification of wheat and rye leaf rust symptoms based on pure spectra at leaf scale,” Journal of Photochemistry and Photobiology B: Biology, vol. 223, Art. no. 112278, 2021. [Google Scholar]
- L. C. Ngugi, M. Abelwahab, and M. Abo-Zahhad, “Recent advances in image processing techniques for automated leaf pest and disease recognition—A review,” Information Processing in Agriculture, vol. 8, no. 1, pp. 27–51, 2021. [Google Scholar]
- E. Nicholls, A. Ely, L. Birkin, P. Basu, and D. Goulson, “The contribution of small-scale food production in urban areas to the sustainable development goals: A review and case study,” Sustainability Science, vol. 15, no. 6, pp. 1585–1599, 2020. [CrossRef] [Google Scholar]
- R. Barretto, R. M. Buenavista, J. L. Rivera, S. Wang, P. V. Prasad, and K. Siliveru, “Teff (Eragrostis tef) processing, utilization and future opportunities: A review,” International Journal of Food Science & Technology, vol. 56, no. 6, pp. 3125–3137, 2020. [Google Scholar]
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.

