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

Abstract

Sustainable agriculture in arid regions faces critical challenges due to water scarcity, high temperatures, and inefficient traditional farming practices. This study presents an AI-enabled smart farming framework for optimizing date palm (Phoenix dactylifera) cultivation through the integration of Machine Learning (ML) and Internet of Things (IoT) technologies. A structured multimodal dataset comprising biometric features palm height, trunk diameter, and leaf number, environmental parameters soil moisture, temperature, and humidity, and categorical attributes variety and health status was analyzed to classify palm health and support data-driven irrigation management. Four ML algorithms Random Forest (RF), Gradient Boosting Machine (GBM), Artificial Neural Network (ANN), and Support Vector Machine (SVM) were developed and optimized using grid search with five-fold cross-validation. Among them, the Random Forest model achieved the highest classification accuracy of 95.3%, demonstrating strong robustness for heterogeneous agricultural data. Feature importance analysis highlighted soil moisture, humidity, trunk diameter, and leaf number as key contributors to palm health prediction. The proposed AI-IoT framework enables real-time monitoring, predictive diagnostics, and automated decision support for sustainable water use and crop management, aligning with Saudi Vision 2030 objectives for technology-driven and resource-efficient agriculture.

Plant phenotyping relevance

ヤシの生体特徴から健康状態を分類する機械学習手法を開発・比較し、分類性能を評価しているため、植物状態推定が中心的な方法的貢献である。

Code and data availability

The paper analyzes a paper-specific Structured Palm Dataset (500 records of date palm biometric and environmental features) and ML models, but no public deposit, repository, or author URL is provided. The Data Availability Statement says the datasets are available from the corresponding author on reasonable request, so

No evidence-backed public reproduction asset is currently recorded.