Table 1 Current state of AI- and IoT-based agricultural research in Saudi Arabia.

From: AI-enabled smart farming framework for sustainable date palm cultivation in arid regions using machine learning and IoT integration

Aspect/Focus Area

Key Findings

Identified Gaps

References

Smart irrigation systems in KSA

IoT-enabled irrigation reduces water use by up to 30%.

Limited integration with AI-based predictive models.

914,

Date palm disease detection

CNNs accurately identify pest or fungal infections in palm leaves.

Datasets are image-only and region-specific.

15

Fruit quality assessment

DL models achieve > 95% accuracy for ripeness and defect detection.

Lack of field-level testing across Saudi regions.

11

Crop classification (RF, SVM)

RF and SVM are reliable for structured agricultural data in arid zones.

Require multimodal datasets (biometric + climatic).

18

Environmental monitoring

IoT sensors measure soil, humidity, and temperature effectively.

Weak integration with ML analytics for yield prediction.

12

UAV and remote sensing

Drones provide valuable imagery for palm health assessment.

Image-based systems lack real-time decision loops.

19

Water resource optimization

ML-based irrigation scheduling enhances efficiency and sustainability.

Absence of adaptive AI frameworks for Saudi farms.

14

Climate-smart agriculture

AI supports precision resource management and yield stability.

Few studies address desert microclimate variability.

1012,

IoT–AI integration

AI-driven IoT improves monitoring and automation.

End-to-end Saudi implementations are scarce.

20

Palm yield prediction

ML improves yield estimation based on temperature and humidity.

Lack of validated Saudi-specific datasets.

21

Edge computing in farms

Edge devices reduce latency for field-level inference.

Energy efficiency and network reliability remain challenges.

22

Smart farming frameworks

Proposed AI–IoT models show promise for real-time analytics.

Limited field trials and scalability in KSA.

23

Biometric–environmental fusion

Combining biometric and climate data enhances model accuracy.

Public datasets with both feature types are rare.

24

Sustainable water use

IoT–AI approaches optimize irrigation under scarcity.

Implementation cost and farmer training barriers exist.

25

Saudi-focused research gap

Smart farming recognized as Vision 2030 enabler.

Need for open datasets and integrated frameworks.

26

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