With the fast growth in the population, food consumption is also growing rapidly worldwide. Agriculture is already supplying about 17% more yield than it applied to just three decades ago. However, more than 800 million people around the world suffer from a lack of food security. Increasing agriculture or food production rapidly for meeting the growing food supply demands is not an easy task. Artificial Intelligence has emerged together with big data technologies and high-performance computing to create new opportunities to unravel, quantify, and understand data-intensive processes in agricultural operational environments. Numerous computing methods such as artificial neural networks. For accuracy analysis, numerous computing methods, such as artificial neural networks, decision trees, support vector machines, Bayesian belief networks, fuzzy systems, deep learning, extreme learning machine, etc. are the most commonly used methods. It is essential to promote research and development of artificial intelligence applications in the fields of environment, agriculture.
This Research Topic will focus on two primary goals: (1) Developing innovative artificial intelligence (AI) and/or stochastic-based techniques for environmental and agriculture modelling purposes and (2) establishing more accurate and efficient predictive models for prediction, optimization, and for the automation of the environmental/agricultural variables. These objectives will also enhance our understanding of environmental and agriculture problems associated with sustainable development in today’s rapidly globalizing and urbanizing world. Research studies focusing on complex and dynamic environmental/agricultural variables and implementing novel modelling approaches, developing new tools, or improving the existing predictive models are especially welcome.
We would like to encourage people to contribute their latest developments, ideas, and review articles on application of artificial intelligence in environment and agriculture sciences. Topics of interest include, but are not limited to, the following:
• Real Time Monitoring
• Machine Learning
• Climate Change
• Crop Management/Yield Prediction
• Flood Forecasting
• Intelligent Harvesting
• Quality Assessment
• Soil Management and Weather Prediction
• Water management
With the fast growth in the population, food consumption is also growing rapidly worldwide. Agriculture is already supplying about 17% more yield than it applied to just three decades ago. However, more than 800 million people around the world suffer from a lack of food security. Increasing agriculture or food production rapidly for meeting the growing food supply demands is not an easy task. Artificial Intelligence has emerged together with big data technologies and high-performance computing to create new opportunities to unravel, quantify, and understand data-intensive processes in agricultural operational environments. Numerous computing methods such as artificial neural networks. For accuracy analysis, numerous computing methods, such as artificial neural networks, decision trees, support vector machines, Bayesian belief networks, fuzzy systems, deep learning, extreme learning machine, etc. are the most commonly used methods. It is essential to promote research and development of artificial intelligence applications in the fields of environment, agriculture.
This Research Topic will focus on two primary goals: (1) Developing innovative artificial intelligence (AI) and/or stochastic-based techniques for environmental and agriculture modelling purposes and (2) establishing more accurate and efficient predictive models for prediction, optimization, and for the automation of the environmental/agricultural variables. These objectives will also enhance our understanding of environmental and agriculture problems associated with sustainable development in today’s rapidly globalizing and urbanizing world. Research studies focusing on complex and dynamic environmental/agricultural variables and implementing novel modelling approaches, developing new tools, or improving the existing predictive models are especially welcome.
We would like to encourage people to contribute their latest developments, ideas, and review articles on application of artificial intelligence in environment and agriculture sciences. Topics of interest include, but are not limited to, the following:
• Real Time Monitoring
• Machine Learning
• Climate Change
• Crop Management/Yield Prediction
• Flood Forecasting
• Intelligent Harvesting
• Quality Assessment
• Soil Management and Weather Prediction
• Water management
