Towards Sustainable Desert Agriculture: An AI-Driven UAV Intelligence Framework for Precision Monitoring and Resource Optimization

Abstract

Abstract There are many challenges faced by desert agriculture like water scarcity, extreme temperatures, poor soil conditions and sand encroachment(sand dunes). Traditional farming methods worked exceptionally well in deserts for thousands of years. But now due to modern megacities projects, climate shifts and unpredictable weather patterns these methods cannot keep up with modern planetary pressures. Modern advanced technologies like AI and precision agriculture along with drones provide new opportunities for intelligent crop monitoring and resource management in arid environments. In this research we propose a simulation-based UAV swarm framework for sustainable desert agriculture using virtual UAV agents, synthetic crop imagery, and AI-driven analysis. We generated a synthetic crop monitoring dataset using publicly available crop disease images combined with environmental stress augmentation techniques to emulate desert farming conditions such as dehydration and heat stress. The virtual UAV swarm collects image-based sensor information from different regions of the simulated field. We created a synthetic desert farming stress dataset to emulate challenging environmental conditions including moderate and severe crop stress scenarios. When integrated within the UAV monitoring framework, the proposed approach achieved complete field coverage, a stress detection rate of 100%, and an average prediction confidence of 98.9%. The achieved results showed clearly that our proposed simulated implementation is suitable to support intelligent crop monitoring, stress detection, and resource-efficient agricultural management in desert environments.

Plant phenotyping relevance

仮想UAV群、合成作物画像、AI解析を統合した作物ストレス検出・監視フレームワークが研究の中心であり、植物のストレス状態を画像から推定するフェノタイピング手法として扱える。

Code and data availability

The paper is a simulation-based UAV swarm study using a synthetic dataset derived from PlantVillage images with custom desert-stress augmentations and a fine-tuned ResNet18. No author-deposited dataset, code, model checkpoint, or supplement with a public URL is described; the only public input (PlantVillage) has no URL

No evidence-backed public reproduction asset is currently recorded.