Integrative approaches in modern agriculture: IoT, ML and AI for disease forecasting amidst climate change

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...

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Bibliographic Details
Published in:Precision agriculture 2024-10, Vol.25 (5), p.2589-2613
Main Authors: Delfani, Payam, Thuraga, Vishnukiran, Banerjee, Bikram, Chawade, Aakash
Format: Article
Language:English
Subjects:
Agricultural Science
Agriculture
Artificial intelligence
Atmospheric Sciences
Biomedical and Life Sciences
Chemistry and Earth Sciences
Climate change
Climate models
Climate prediction
Computer Science
Crop management
Crop yield
Disease control
Disease management
Farming systems
Forecasting
Internet of Things
Jordbruksvetenskap
Life Sciences
Machine learning
Physics
Plant diseases
Remote Sensing/Photogrammetry
Resource management
Review
Soil Science & Conservation
Statistics for Engineering
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Summary: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.
ISSN:1385-2256
1573-1618
1573-1618
DOI:10.1007/s11119-024-10164-7