Towards Smart Irrigation: A Literature Review on the Use of Geospatial Technologies and Machine Learning in the Management of Water Resources in Arboriculture

Abstract

Agriculture consumes an important ratio of the water reserve in irrigated areas. The improvement of irrigation is becoming essential to reduce this high water consumption by adapting supplies to the crop needs and avoiding losses. This global issue has prompted many scientists to reflect on sustainable solutions using innovative technologies, namely Unmanned Aerial Vehicles (UAV), Machine Learning (ML), and the Internet of Things (IoT). This article aims to present an overview of the use of these new technologies in the analysis of the water status of crops for better irrigation management, with an emphasis on arboriculture. The review demonstrated the importance of UAV-ML-IoT technologies. This contribution is due to the relevant information that can be collected from IoT sensors and extracted from UAV images through various sensors (RGB, multispectral, hyperspectral, thermal), and the ability of ML models to monitor and predict water status. The review in this paper is organized into four main sections: the use of UAV in arboriculture, UAV for irrigation management in arboriculture, IoT systems and irrigation management, and ML for data processing and decision-making. A discussion is presented regarding the prospects for smart irrigation using geospatial technologies and machine learning.

Evapotranspiration Acquired with Remote Sensing Thermal-Based Algorithms: A State-of-the-Art Review

“…However, these methods can be timeconsuming since in order to use properly a machine learning algorithm, it is necessary to train it with a different dataset, and once optimized, the ML algorithm is checked against a test dataset, different from the training and validation datasets. A new review study about this topic [238] is recommended to the reader for more detail.…”

Modeling Probabilistic-Based Reliability Analysis for Irrigation Water Supply Due to Uncertainties in Hydrological and Irrigation Factors

“…Thus, if further work could be carried out coupling the probabilistic decision-making algorithm integrated with the proposed RA_IWS_canal model, the planned irrigation water demands at various canals within a multi-canal irrigation zone could be determined. In addition to the reliability quantification, since this study reproduces a significant number of irrigation water supplies at all canals under the generated uncertainty factors for the model development, the resulting irrigation water allocation simulations would be adopted for training an artificial intelligence (AI) model (e.g., machine learning and artificial neural network) for efficiently allocating the irrigation water with high reliability and accuracy within an irrigation system, instead of a complicated network-structure water allocation simulation modeling [38][39][40].…”

Classification and Forecasting of Water Stress in Tomato Plants Using Bioristor Data

“…In particular, water status estimation and smart irrigation via machine learning techniques and for different plantations have been thoroughly studied and reviewed [23,24], also in the context of remote sensing and decision support systems [25]. For example, Ashutosh et al [10] try to estimate soil moisture by exploiting artificial neural networks on meteorological data to improve rice crop yield, while in [13] machine learning techniques are applied to an IoT-based smart irrigation system to predict water needs in various crops when considering soil moisture, air temperature, relative humidity, and ultraviolet radiation as input data.…”