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An Overview of Smart Irrigation Management for Improving Water Productivity under Climate Change in Drylands
by
Ahmed, Zeeshan
, Gui, Dongwei
, Murtaza, Ghulam
, Ali, Sikandar
, Yunfei, Liu
in
Agricultural production
/ agricultural productivity
/ Agriculture
/ agronomy
/ Arid lands
/ Arid zones
/ Artificial intelligence
/ Artificial neural networks
/ China
/ Climate change
/ climatic factors
/ Crop diseases
/ Deep learning
/ Developing countries
/ Drought
/ drylands
/ Earth surface
/ Expert systems
/ food insecurity
/ Food security
/ Food supply
/ Fuzzy logic
/ Global temperature changes
/ Hybrid systems
/ Irrigation
/ irrigation management
/ Irrigation scheduling
/ Irrigation systems
/ Irrigation water
/ Land degradation
/ LDCs
/ Machine learning
/ Moisture effects
/ Natural resources
/ Neural networks
/ Productivity
/ smart irrigation
/ Soil moisture
/ Soil properties
/ soil water
/ Sustainable development
/ sustainable development goals
/ Unmanned aerial vehicles
/ Vegetation
/ Water
/ Water conservation
/ Water management
/ Water scarcity
/ water shortages
/ Water use
/ Water use efficiency
/ Weather
2023
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An Overview of Smart Irrigation Management for Improving Water Productivity under Climate Change in Drylands
by
Ahmed, Zeeshan
, Gui, Dongwei
, Murtaza, Ghulam
, Ali, Sikandar
, Yunfei, Liu
in
Agricultural production
/ agricultural productivity
/ Agriculture
/ agronomy
/ Arid lands
/ Arid zones
/ Artificial intelligence
/ Artificial neural networks
/ China
/ Climate change
/ climatic factors
/ Crop diseases
/ Deep learning
/ Developing countries
/ Drought
/ drylands
/ Earth surface
/ Expert systems
/ food insecurity
/ Food security
/ Food supply
/ Fuzzy logic
/ Global temperature changes
/ Hybrid systems
/ Irrigation
/ irrigation management
/ Irrigation scheduling
/ Irrigation systems
/ Irrigation water
/ Land degradation
/ LDCs
/ Machine learning
/ Moisture effects
/ Natural resources
/ Neural networks
/ Productivity
/ smart irrigation
/ Soil moisture
/ Soil properties
/ soil water
/ Sustainable development
/ sustainable development goals
/ Unmanned aerial vehicles
/ Vegetation
/ Water
/ Water conservation
/ Water management
/ Water scarcity
/ water shortages
/ Water use
/ Water use efficiency
/ Weather
2023
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An Overview of Smart Irrigation Management for Improving Water Productivity under Climate Change in Drylands
by
Ahmed, Zeeshan
, Gui, Dongwei
, Murtaza, Ghulam
, Ali, Sikandar
, Yunfei, Liu
in
Agricultural production
/ agricultural productivity
/ Agriculture
/ agronomy
/ Arid lands
/ Arid zones
/ Artificial intelligence
/ Artificial neural networks
/ China
/ Climate change
/ climatic factors
/ Crop diseases
/ Deep learning
/ Developing countries
/ Drought
/ drylands
/ Earth surface
/ Expert systems
/ food insecurity
/ Food security
/ Food supply
/ Fuzzy logic
/ Global temperature changes
/ Hybrid systems
/ Irrigation
/ irrigation management
/ Irrigation scheduling
/ Irrigation systems
/ Irrigation water
/ Land degradation
/ LDCs
/ Machine learning
/ Moisture effects
/ Natural resources
/ Neural networks
/ Productivity
/ smart irrigation
/ Soil moisture
/ Soil properties
/ soil water
/ Sustainable development
/ sustainable development goals
/ Unmanned aerial vehicles
/ Vegetation
/ Water
/ Water conservation
/ Water management
/ Water scarcity
/ water shortages
/ Water use
/ Water use efficiency
/ Weather
2023
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An Overview of Smart Irrigation Management for Improving Water Productivity under Climate Change in Drylands
Journal Article
An Overview of Smart Irrigation Management for Improving Water Productivity under Climate Change in Drylands
2023
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Overview
Global drylands, covering about 41% of Earth’s surface and inhabited by 38% of the world’s population, are facing the stark challenges of water scarcity, low water productivity, and food insecurity. This paper highlights the major constraints to agricultural productivity, traditional irrigation scheduling methods, and associated challenges, efforts, and progress to enhance water use efficiency (WUE), conserve water, and guarantee food security by overviewing different smart irrigation approaches. Widely used traditional irrigation scheduling methods (based on weather, plant, and soil moisture conditions) usually lack important information needed for precise irrigation, which leads to over- or under-irrigation of fields. On the other hand, by using several factors, including soil and climate variation, soil properties, plant responses to water deficits, and changes in weather factors, smart irrigation can drive better irrigation decisions that can help save water and increase yields. Various smart irrigation approaches, such as artificial intelligence and deep learning (artificial neural network, fuzzy logic, expert system, hybrid intelligent system, and deep learning), model predictive irrigation systems, variable rate irrigation (VRI) technology, and unmanned aerial vehicles (UAVs) could ensure high water use efficiency in water-scarce regions. These smart irrigation technologies can improve water management and accelerate the progress in achieving multiple Sustainable Development Goals (SDGs), where no one gets left behind.
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