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Artificial Intelligence To Improve Digital Agriculture

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Artificial Intelligence To Improve Digital Agriculture: Agriculture production systems face daunting challenges worldwide, including climate change, dwindling water supply for irrigation, increases in production costs, and an overall reduction in the farm workforce over the past several decades.

Besides, the most current issue, the COVID-19 pandemic, threatens the disruption of food production and supply systems everywhere.

These factors threaten the environmental and economic sustain-ability of current and future food supply systems.

While agriculture is always evolving, significant innovations will be needed to keep pace with persistent climate change.

The obvious question here is how to produce sufficient quality food for the fast-growing global population sustainably.

RELATED: The Role Of Artificial Intelligence In Future Technology

Agricultural research scientists have always been utilizing state-of-the-art technologies and exploring ways to integrate them into agriculture systems.

Dynamic crop simulation models have been useful tools for integrating diverse components of agriculture systems and allowing us to explore how those components function within the system.

Artificial Intelligence (AI) is recently gaining significant attention within agriculture disciplines because of its potential to leverage big data, which is now becoming easily accessible through the use of Unmanned Aircraft Systems (UAS).

UAS brings an unprecedented opportunity to enable advanced analytics for managing agricultural systems, thus improving the resiliency and efficiency of production systems.

In this article, we review current research on the use of remote sensing technology for sustainable agriculture.

We also discuss current challenges facing the adoption of UAS technologies, as well as future perspectives on its integration with spaceborne remote sensing data for national and global scale studies.

Unmanned Aerial Systems (UAS) as a foundation for digital agriculture

Deploying individual physical sensors is often costly and time-consuming.

Maintaining them in the field is also challenging as they frequently interfere with field operations such as tillage, planting, spraying, and harvesting.

Plants integrate genetics (G) and its surrounding environments (E) by responding to soil physical and chemical properties, moisture availability, biotic and abiotic factors, as well as management practices (M).

In this regard, plants can serve as field-based biological probes that may be assessed by sensors on-board UAS.

Traditional methods of collecting crop data often fail to capture in-field variations due to limited sampling size and are prone to a certain level of subjectivity.

To that end, UAS equipped with appropriate sensors can measure the time course of plant growth accurately, swiftly, and cost effectively. read full pdf here

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