Role of Artificial Intelligence in Plant Disease Detection: A Review
DOI:
https://doi.org/10.70788/ern.2.1.2025.11Keywords:
Machine Learning, Deep Learning, Hyperspectral ImagingAbstract
Agriculture is the backbone of human civilization, playing a vital role in sustaining national economies. Enhancing food production is crucial for mitigating hunger and ensuring global food security. However, plant diseases caused by fungi, bacteria, viruses, and nematodes pose a significant threat to agricultural productivity, resulting in substantial economic losses worldwide. Effective crop protection and improvement of crop quality and quantity are inextricably linked to plant disease management. Accurate identification and detection of plant diseases are essential for devising strategic control measures. Traditional disease detection methods have proven inadequate, with delayed diagnosis and inaccurate results hindering effective disease control. Recent advances in Artificial Intelligence (AI) have revolutionized plant disease detection, enabling rapid and accurate identification of diseases across vast areas. This review aims to investigate the efficacy of automated plant disease detection using Machine Learning (ML), Deep Learning (DL), and Hyperspectral Imaging. By leveraging these cutting-edge technologies, we can develop more accurate and reliable disease detection systems, ultimately contributing to enhanced crop yields and global food security.
References
• Adam, E., Deng, H., Odindi, J., Abdel-Rahman, E. M., & Mutanga, O. (2017). Detecting the early stage of phaeosphaeria leaf spot infestations in maize crop using in situ hyperspectral data and guided regularized random forest algorithm. Journal of Spectroscopy, 2017(1), 6961387.
• Abbas, M., Adil, M., Ehtisham-ul-Haque, S., Munir, B., Yameen, M., Ghaffar, A., … & Iqbal, M. (2018). Vibrio fischeri bioluminescence inhibition assay for ecotoxicity assessment: a review. Science of the Total Environment, 626, 1295-1309.
• Adhikari, P., Oh, Y., & Panthee, D. R. (2017). Current status of early blight resistance in tomato: an update. International journal of molecular sciences, 18(10), 2019.
• Arnal, A., Waleckx, E., Rico-Chavez, O., Herrera, C., & Dumonteil, E. (2019). Estimating the current burden of Chagas disease in Mexico: A systematic review and meta-analysis of epidemiological surveys from 2006 to 2017. PLoS neglected tropical diseases, 13(4), e0006859.
• Chen, H., Lan, Y., Fritz, B. K., Hoffmann, W. C., & Liu, S. (2021). Review of agricultural spraying technologies for plant protection using unmanned aerial vehicle (UAV). International Journal of Agricultural and Biological Engineering, 14(1), 38-49.
• Delalieux, S., Van Aardt, J. A. N., Keulemans, W., Schrevens, E., & Coppin, P. (2007). Detection of biotic stress (Venturia inaequalis) in apple trees using hyperspectral data: Non-parametric statistical approaches and physiological implications. European Journal of Agronomy, 27(1), 130-143.
• Fuentes‐Pardo, A. P., & Ruzzante, D. E. (2017). Whole‐genome sequencing approaches for conservation biology: Advantages, limitations and practical recommendations. Molecular ecology, 26(20), 5369-5406.
• Howard, A. G. (2017). Mobilenets: Efficient convolutional neural networks for mobile vision applications. arXiv preprint arXiv:1704.04861.
• Karpyshev, P., Ilin, V., Kalinov, I., Petrovsky, A., & Tsetserukou, D. (2021, January). Autonomous mobile robot for apple plant disease detection based on cnn and multi-spectral vision system. In 2021 IEEE/SICE international symposium on system integration (SII) (pp. 157-162). IEEE.
• Karadağ, C., Yoldemir, T., Demircan Karadağ, S., & Turgut, A. (2020). The effects of endometrioma size and bilaterality on ovarian reserve. Journal of Obstetrics and Gynaecology, 40(4), 531-536.
• Kaur, V., Kumar, M., Kumar, A., Kaur, K., Dhillon, V. S., & Kaur, S. (2018). Pharmacotherapeutic potential of phytochemicals: Implications in cancer chemoprevention and future perspectives. Biomedicine & Pharmacotherapy, 97, 564-586.
• [12] LeCun, Y., Bottou, L., Bengio, Y., & Haffner, P. (1998). Gradient-based learning applied to document recognition. Proceedings of the IEEE, 86(11), 2278-2324.
• Li, C., & Wang, M. (2024). Pest and disease management in agricultural production with artificial intelligence: Innovative applications and development trends. Advances in Resources Research, 4(3), 381-401.
• Liu, B., Zhang, Y., He, D., & Li, Y. (2017). Identification of apple leaf diseases based on deep convolutional neural networks. Symmetry, 10(1), 11.
• Murtaza, G., Riaz, S., Zafar, M., Ahsan Raza, M., Kaleem, I., Imran, H., … & Bashir, S. (2024). Examining the growing challenge: Prevalence of diabetes in young adults. Medicine International, 5(1), 2.
• Mahlein, A. K. (2016). Plant disease detection by imaging sensors–parallels and specific demands for precision agriculture and plant phenotyping. Plant disease, 100(2), 241-251.
• Pandey, A., & Dubey, S. (2017). Evaluations of brinjal germplasm for resistance to fusarium wilt disease. Int J Sci Res Publ, 7(7), 435-438.
• Radoglou-Grammatikis, P., Sarigiannidis, P., Lagkas, T., & Moscholios, I. (2020). A compilation of UAV applications for precision agriculture. Computer Networks, 172, 107148.
• Sarangdhar, A. A., & Pawar, V. R. (2017, April). Machine learning regression technique for cotton leaf disease detection and controlling using IoT. In 2017 international conference of electronics, communication and aerospace technology (ICECA) (Vol. 2, pp. 449-454). IEEE.
• Singh, R. P., Singh, A. K., & Dixit, P. S. Ameliorate chemical quality through different pruning time, pruning intensity and bagging of fruit in Mrig Bahar guava cv. Sardar (L-49).
• Vishnoi, V. K., Kumar, K., & Kumar, B. (2021). Plant disease detection using computational intelligence and image processing. Journal of Plant Diseases and Protection, 128, 19-53.
• Wang, J., Feng, L., Palmer, P. I., Liu, Y., Fang, S., Bösch, H., & Xia, C. (2022). Reply to: The size of the land carbon sink in China. Nature, 603(7901), E10-E12.
Downloads
Published
Issue
Section
License
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
Commons Attribution-NonCommercial-NoDerivatives 4.0 International License
