APPLICATION OF ARTIFICIAL INTELLIGENCE FOR IRRIGATION MANAGEMENT: A SYSTEMATIC REVIEW

ABSTRACT A literature review on artificial intelligence in irrigation management was performed, using the Systematic Literature Review (SLR) method with explicit search criteria. More than 45,000 complete titles in 130 reference bases were consulted at once. A total of 38 primary studies were selected, which formed the basis of this review. The findings showed increasing use of Artificial Neural Networks (ANN) fed with climate and soil sensor data for irrigation management solutions. ANNs have been the most popular choice for solutions that require machine learning techniques. Fuzzy-logic-based technologies stood out in Decision Support Systems (SSD). Hybrid neuro-fuzzy approaches manage the best aspects contained in each of the two techniques (ANN and fuzzy logic). Moreover, autonomous wireless and networked sensors have been the most often used. Good chances of developing solutions for irrigation management point to the growing application of ANN-based machine learning, Support Vector Machine (SVM), and Random Forests techniques, using wireless sensor networks and computer vision with remote sensing images.

摘要 本研究采用系统综述（Systematic Literature Review, SLR）方法，设定明确的检索标准，开展了灌溉管理领域人工智能应用的综述研究。本次研究一次性检索了130个文献数据库中的45000余条完整标题记录，最终筛选出38项原始研究，作为本次综述的核心依据。研究结果显示，以气候与土壤传感器数据作为输入的人工神经网络（Artificial Neural Networks, ANN）在灌溉管理解决方案中的应用愈发广泛；对于需要机器学习技术的灌溉方案而言，人工神经网络已是最主流的选型。基于模糊逻辑的技术在决策支持系统（Decision Support Systems, SSD）中表现突出；混合神经模糊方法则整合了人工神经网络与模糊逻辑两种技术的最优特性。此外，自主式无线联网传感器已是当前应用最为广泛的设备。结合无线传感器网络与遥感影像计算机视觉技术、采用基于人工神经网络的机器学习、支持向量机（Support Vector Machine, SVM）以及随机森林技术的灌溉管理解决方案，拥有广阔的发展前景。