Plant disease detection and classification techniques
Plant disease identification and classification leverage the capabilities of computer vision, a subset of Artificial Intelligence (AI), allowing machines to interpret and analyze real-world images like the human visual system. This technique, combined with advancements in Machine Learning (ML) and its more sophisticated branch, Deep Learning (DL), shows considerable promise in enhancing the precision of disease detection and classification in plants.
Employed in diverse sectors including medical diagnostics, surveillance, and agribusiness, computer vision systems utilize a series of well-defined procedural steps starting from image capture, followed by various image processing tasks such as resizing, filtering, segmentation, feature extraction, and selection. The final step involves applying ML or DL algorithms to accurately detect and classify diseases based on the identified features or symptoms, significantly improving disease management in agriculture.
Ref:
Kumar R, Chug A, Singh AP, Singh D. A systematic analysis of machine learning and deep learning based approaches for plant leaf disease classification: a Review. J Sensors. 2022. https://doi.org/10.1155/2022/3287561.
Tiwari V, Joshi RC, Dutta MK. Dense convolutional neural networks based multiclass plant disease detection and classification using leaf images. Ecol Inform. 2021;63: 101289. https://doi.org/10.1016/j.ecoinf.2021.101289.
Shoaib M, et al. An advanced deep learning models-based plant disease detection: a review of recent research. Front Plant Sci. 2023;14:1–22. https://doi.org/10.3389/fpls.2023.1158933.
Ahmed I, Yadav PK. A systematic analysis of machine learning and deep learning-based approaches for identifying and diagnosing plant diseases. Sustain Oper Comput. 2023;4:96–104. https://doi.org/10.1016/j.susoc.2023.03.001.
Dhiman P, Kaur A, Balasaraswathi VR, Gulzar Y, Alwan AA, Hamid Y. Image acquisition, preprocessing and classification of citrus fruit diseases: a systematic literature review. Sustainability. 2023;15(12):9643. https://doi.org/10.3390/su15129643.
Last updated