Data management challenges for artificial intelligence in plant and agricultural research

Area of Science:

• Plant Science
• Agricultural Technology
• Data Science
• Artificial Intelligence (AI)

Background:

• Artificial Intelligence (AI) is gaining traction in plant science but lacks routine, effective implementation.
• Integrating, comparing, and visualizing large, multi-dimensional datasets from diverse sources is crucial for novel food and agricultural technologies.
• Data science and AI hold promise for advancing crop and agronomic research.

Purpose of the Study:

• To summarize the reasons for increased interest in data science and AI within plant science.
• To identify and discuss eight key data management challenges impeding AI adoption in crop and agronomic research.
• To highlight the potential of Machine Learning (AI) in this domain.

Main Methods:

• Literature review and synthesis of current trends in AI and data science in plant science.
• Identification and critical analysis of data management challenges.
• Discussion of the implications for Machine Learning (AI) applications.

Main Results:

• The paper outlines the growing importance of AI in plant science.
• Eight significant data management challenges are identified and discussed.
• These challenges represent critical barriers to the effective use of AI, especially Machine Learning (AI), in agriculture.

Conclusions:

• Addressing the identified data management challenges is essential for unlocking the full potential of AI in plant science.
• Improved data integration, comparison, and visualization methods are needed.
• Overcoming these hurdles will facilitate the development of advanced AI-driven agricultural technologies.