Forecasting of Wheat Diseases: Insights, Methods and Challenges

Kaur, Jaspal; Bala, Ritu; Singh, Rajesh

doi:10.1007/978-981-16-4134-3_2

Cited by 3 publications

References 224 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

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

Delfani,

Thuraga,

Banerjee

et al. 2024

Precision Agric

View full text Add to dashboard Cite

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.

show abstract

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

Delfani,

Thuraga,

Banerjee

et al. 2024

Precision Agric

View full text Add to dashboard Cite

show abstract

An IoT-based Smart Grains Warehouse Monitoring System

Arun,

Manju,

Balamurugan

et al. 2024

AIP Conference Proceedings

View full text Add to dashboard Cite

Disease Management in Regenerative Cropping in the Context of Climate Change and Regulatory Restrictions

Newton,

Creissen,

Erreguerena

et al. 2024

Annual Review of Phytopathology

View full text Add to dashboard Cite

Regenerative agriculture as a term and concept has gained much traction over recent years. Many farmers are convinced that by adopting these principles they will be able to address the triple crisis of biodiversity loss, climate change, and food security. However, the impact of regenerative agriculture practices on crop pathogens and their management has received little attention from the scientific community. Significant changes to cropping systems may result in certain diseases presenting more or less of a threat. Shifts in major diseases may have significant implications regarding optimal integrated pest management (IPM) strategies that aim to improve profitability and productivity in an environmentally sensitive manner. In particular, many aspects of regenerative agriculture change risk levels and risk management in ways that are central to effective IPM. This review outlines some of the challenges, gaps, and opportunities in our understanding of appropriate approaches for managing crop diseases in regenerative cropping systems.

show abstract

Forecasting of Wheat Diseases: Insights, Methods and Challenges

Cited by 3 publications

References 224 publications

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

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

An IoT-based Smart Grains Warehouse Monitoring System

Disease Management in Regenerative Cropping in the Context of Climate Change and Regulatory Restrictions

Contact Info

Product

Resources

About