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.

However, deep learning and intelligent sensor research 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.

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 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.

Potential topics include but are not limited to the following:

• Sensors and applications in farming practices
• Sensors for food science
• The response of animals, plants, and microorganisms to environmental change by sensor technologies
• Sensors and applications in the natural environment, including land, air, and water
• Global and regional observation on the earth environment (such as carbon cycle and urban heat island effect) and its technical challenges
• Using remote sensing data to monitor and predict climate change
• Environmental monitoring, modelling, and impact analysis using multi-sensors
• Deep learning by sensor technologies for agricultural and environmental monitoring
• Big sensor data in agricultural and environmental monitoring
• Emerging IoT-based sensor applications in smart farming
• Software engineering techniques for agricultural and environmental monitoring
• Sensing artificial intelligence systems for agricultural production, transportation, and storage