Nowadays, agriculture and food production are increasingly being digitalized by using smart devices and intelligent software systems. This leads to an improvement in the economic, social, and environmental sustainability of agri-food value chain. In fact, digital technologies can offer powerful and efficient solutions to raise on-farm productivity, optimize environmental resource use, improve supply chain and logistics performances, reduce agri-food losses and waste, and increase competitiveness. This is possible by virtue of the availability of cost-effective, and highly interconnected hardware and software technologies, which collect, use, and analyze huge amounts of data. Consequently, information technology, decision support systems, the Internet of Things, robotics, and artificial intelligence applications play an essential role in modern business management.
The goal of this Research Topic is to implement and develop innovative solutions for different applications in agri-food systems using emergent and mature digital technologies, such as blockchain technology, artificial intelligence, big data analytics, machine learning algorithms, linked data, the Internet of Things, and computer vision. In particular, spectroscopic techniques and imaging analysis can be explored. Moreover, predictive models and/or artificial intelligence (AI) can be used to provide farmers with guidance about crop rotation, optimal planting times, harvesting times, soil management, water stress management, disease management, and yield. At last, image analysis can be applied to plant phenotyping, or monitoring food quality and origin.
This Research Topic will address topics relevant to the agri-food sector, from targeted measures on farms to integrated solutions aimed at organizational changes throughout the entire value chain. Articles that assess the sustainability implications (agronomic, environmental, economic, and social impacts or benefits) of the implementation of such technologies and innovations are also encouraged. This Research Topic welcomes original research articles and review articles, as well as theoretical and experimental research articles on the following themes:
• Image processing to assist precision agriculture practices, plant phenotyping, and breeding programs;
• Computer imaging systems, and multispectral and hyperspectral sensors for post-harvest monitoring and quality control;
• Classification and prediction of food quality using low-cost, non-destructive, and high-throughput screening spectroscopy;
• Development of intelligent algorithms for processing spectroscopic data.
Nowadays, agriculture and food production are increasingly being digitalized by using smart devices and intelligent software systems. This leads to an improvement in the economic, social, and environmental sustainability of agri-food value chain. In fact, digital technologies can offer powerful and efficient solutions to raise on-farm productivity, optimize environmental resource use, improve supply chain and logistics performances, reduce agri-food losses and waste, and increase competitiveness. This is possible by virtue of the availability of cost-effective, and highly interconnected hardware and software technologies, which collect, use, and analyze huge amounts of data. Consequently, information technology, decision support systems, the Internet of Things, robotics, and artificial intelligence applications play an essential role in modern business management.
The goal of this Research Topic is to implement and develop innovative solutions for different applications in agri-food systems using emergent and mature digital technologies, such as blockchain technology, artificial intelligence, big data analytics, machine learning algorithms, linked data, the Internet of Things, and computer vision. In particular, spectroscopic techniques and imaging analysis can be explored. Moreover, predictive models and/or artificial intelligence (AI) can be used to provide farmers with guidance about crop rotation, optimal planting times, harvesting times, soil management, water stress management, disease management, and yield. At last, image analysis can be applied to plant phenotyping, or monitoring food quality and origin.
This Research Topic will address topics relevant to the agri-food sector, from targeted measures on farms to integrated solutions aimed at organizational changes throughout the entire value chain. Articles that assess the sustainability implications (agronomic, environmental, economic, and social impacts or benefits) of the implementation of such technologies and innovations are also encouraged. This Research Topic welcomes original research articles and review articles, as well as theoretical and experimental research articles on the following themes:
• Image processing to assist precision agriculture practices, plant phenotyping, and breeding programs;
• Computer imaging systems, and multispectral and hyperspectral sensors for post-harvest monitoring and quality control;
• Classification and prediction of food quality using low-cost, non-destructive, and high-throughput screening spectroscopy;
• Development of intelligent algorithms for processing spectroscopic data.