Artificial intelligence and machine learning are two promising areas in computer science, automation and robotics. Artificial intelligence describes the capability of a machine to imitate intelligent human behavior and mimic “cognitive” functions, such as learning and problem solving. Machine learning is an application of artificial intelligence, based on the idea that a machine (e.g., computer, micro-controller) can learn from data and identify patterns in data. That process can eliminate human intervention and errors. Computers programmed to “learn” from data, can learn by recognizing patterns, and adjust to new inputs to accomplish specific tasks (e.g., self-driving cars, object detection and classification); this process is described as machine learning.
In agriculture, recent technological advances in sensors, mechatronics, robotics, big data analytics, and artificial intelligence have enabled the development and implementation of remote sensing technologies for plant, fruit, weed, pest, and disease identification and management. These technologies integrated with precise manipulators can provide a unique opportunity for developing intelligent agricultural systems for precision applications and could potentially revolutionize the specialty crop industry.
The use of remote sensing systems like satellite imagery, unmanned aerial vehicles (UAVs), and ground-based platforms together with big data analytics and artificial intelligence can improve production management, optimize agrochemical applications, increase profit, and reduce environmental impact. Artificial neural networks and deep learning are increasingly used in remote sensing, machine vision, and robotics. For example, image-based pattern recognition systems have been developed for pest and disease detection, target-based spray, robotic harvest, etc. Deep convolutional neural networks (CNNs) are the most widely used deep learning approach for image recognition. These methods and smart technologies have achieved dramatic improvements in many domains and have attracted considerable interest of both academic and industrial communities. Robotic sprayers and harvesters can relieve the current stress on labor due to shortages of workers, improve crop production, and could transform the specialty crop industry.
This Research Topic focuses on the recent advances and latest technical developments in artificial intelligence applications for specialty crops management. We invite researchers to contribute original research articles, review articles, as well as opinion papers, and papers on perspectives and on novel methods in the areas of machine and computer vision, big data analytics, automation and robotics, machine learning, deep and transfer learning, reinforcement learning, and so on, with respect to plant science.
Contributions will cover, but are not limited to, the following:
• Remote sensing, machine and computer vision for automated data acquisition systems, robotic spraying, pruning and harvesting, and so on
• Precision agriculture technologies
• Digital and smart agriculture and machinery
• Decision support systems and crop modeling
• Supply chain and logistics optimization.
Artificial intelligence and machine learning are two promising areas in computer science, automation and robotics. Artificial intelligence describes the capability of a machine to imitate intelligent human behavior and mimic “cognitive” functions, such as learning and problem solving. Machine learning is an application of artificial intelligence, based on the idea that a machine (e.g., computer, micro-controller) can learn from data and identify patterns in data. That process can eliminate human intervention and errors. Computers programmed to “learn” from data, can learn by recognizing patterns, and adjust to new inputs to accomplish specific tasks (e.g., self-driving cars, object detection and classification); this process is described as machine learning.
In agriculture, recent technological advances in sensors, mechatronics, robotics, big data analytics, and artificial intelligence have enabled the development and implementation of remote sensing technologies for plant, fruit, weed, pest, and disease identification and management. These technologies integrated with precise manipulators can provide a unique opportunity for developing intelligent agricultural systems for precision applications and could potentially revolutionize the specialty crop industry.
The use of remote sensing systems like satellite imagery, unmanned aerial vehicles (UAVs), and ground-based platforms together with big data analytics and artificial intelligence can improve production management, optimize agrochemical applications, increase profit, and reduce environmental impact. Artificial neural networks and deep learning are increasingly used in remote sensing, machine vision, and robotics. For example, image-based pattern recognition systems have been developed for pest and disease detection, target-based spray, robotic harvest, etc. Deep convolutional neural networks (CNNs) are the most widely used deep learning approach for image recognition. These methods and smart technologies have achieved dramatic improvements in many domains and have attracted considerable interest of both academic and industrial communities. Robotic sprayers and harvesters can relieve the current stress on labor due to shortages of workers, improve crop production, and could transform the specialty crop industry.
This Research Topic focuses on the recent advances and latest technical developments in artificial intelligence applications for specialty crops management. We invite researchers to contribute original research articles, review articles, as well as opinion papers, and papers on perspectives and on novel methods in the areas of machine and computer vision, big data analytics, automation and robotics, machine learning, deep and transfer learning, reinforcement learning, and so on, with respect to plant science.
Contributions will cover, but are not limited to, the following:
• Remote sensing, machine and computer vision for automated data acquisition systems, robotic spraying, pruning and harvesting, and so on
• Precision agriculture technologies
• Digital and smart agriculture and machinery
• Decision support systems and crop modeling
• Supply chain and logistics optimization.