Integrative approaches in modern agriculture: IoT, ML and AI for disease forecasting amidst climate change
Article
Article Title | Integrative approaches in modern agriculture: IoT, ML and AI for disease forecasting amidst climate change |
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ERA Journal ID | 5325 |
Article Category | Article |
Authors | Delfani, Payam, Thuraga, Vishnukiran, Banerjee, Bikram and Chawade, Aakash |
Journal Title | Precision Agriculture |
Journal Citation | 25, pp. 2589-2613 |
Number of Pages | 25 |
Year | 2024 |
Publisher | Springer |
Place of Publication | United States |
ISSN | 1385-2256 |
1573-1618 | |
Digital Object Identifier (DOI) | https://doi.org/10.1007/s11119-024-10164-7 |
Web Address (URL) | https://link.springer.com/article/10.1007/s11119-024-10164-7 |
Abstract | Plant disease forecasting models, driven by concurrent data and advanced technologies, are reliable tools for accurate prediction of disease outbreaks in achieving sustainable and productive agricultural systems. Optimal integration of Internet of Things (IoTs), machine learning (ML) techniques and artificial intelligence (AI), further augment the capabilities of these models in empowering farmers with proactive disease control measures towards modern agriculture manifested by efficient resource management, reduced diseases and higher crop yields. This article summarizes the role of disease forecasting models in crop management, emphasizing the advancements and applications of AI and ML in disease prediction, challenges and future directions in the field via (a) The technological foundations and need for validation testing of models, (b) The advancements in disease forecasting with the importance of high-quality publicly available data and (c) The challenges and future directions for the development of transparent and interpretable open-source AI models. Further improvement of these models needs investment in continuous innovative research with collaboration and data sharing among agricultural stakeholders. |
Contains Sensitive Content | Does not contain sensitive content |
ANZSRC Field of Research 2020 | 409901. Agricultural engineering |
Byline Affiliations | Swedish University of Agricultural Sciences, Sweden |
School of Surveying and Built Environment | |
Centre for Crop Health |
https://research.usq.edu.au/item/z7x64/integrative-approaches-in-modern-agriculture-iot-ml-and-ai-for-disease-forecasting-amidst-climate-change
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