Guest Editors
Yuan Li, Shaanxi Normal University, China
Zhenxing Zhang, Northeast Normal University, China
Liyuan Zhang, Department of Civil and Environmental Engineering, Colorado State University, USA
Summary
The continuous advancements in design, performance, and application of sensors and sensing systems have had a strong impact on agricultural, environmental, and ecological engineering. The applications of sensors range from smaller scales, such as monitoring the processes of a plant cell, to larger scales, such as the global remote sensing survey of agriculture, grassland, and forests. Manufacturers of agricultural and environmental machinery are increasingly incorporating advanced sensor technology into their precision farming and environmental equipment, such as portable photosynthesis systems, soil carbon flux monitoring, time domain reflectometry, and soil salinity monitoring. Recent developments in Internet-of-Things (IoT) technology have enabled a new paradigm of smart farming to manage agricultural produce, land, and animals more effectively and efficiently. In addition, deep learning approaches make better use of big data and provide an end-to-end learning framework in which jointly learning feature transformations and classifiers via the back propagation technique makes their integration optimal. The detection of agricultural production and changes in the natural environment and the rapid development of the analysis and application technologies are continuing. As a result, this progress is expected to dramatically improve quality of life.
The applications of sensors in agricultural, environmental, and ecological engineering have been greatly improved. However, new sensing technology, such as deep learning and intelligent sensor is still in its infancy, and there are some technical difficulties to be resolved in the agricultural and environmental fields. There is a need to consider the concept of combining artificial intelligence with high-performance hardware in sustainable farming and smart environmental monitoring. To broaden the database, deep learning and big sensor data can also be used.
This Special Issue will emphasize the practical applications of sensors in each area of agricultural, environmental, and ecological engineering. The aim of this Special Issue is to solicit articles from academic and experts discussing their contribution to artificial intelligence and its applications for sustainable farming and smart environmental monitoring. This Special Issue will consider original research and review articles reporting theoretical, simulation, or experimental studies related to new sensing technology, such as deep learning, artificial intelligence, and big data by sensor technologies in agricultural and environmental monitoring. The target audiences are researchers and engineers in the agricultural and environmental fields who need to apply reliable sensing and artificial intelligence technologies.
Keywords
Sustainable farming, Smart environmental monitoring, sensors in agricultural, Sensors for food science, molecular biology by sensor technologies, deep learning and experimental botany, artificial intelligence and experimental botany
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