Intelligent agriculture is the inevitable trend of future agriculture. As the brain of intelligent agriculture, advanced sensors determine the degree of agricultural environment and crop perception. However, an outstanding problem is the long-standing lack of effective sensing and monitoring tools in agriculture. Most information still relies on time-consuming and complex laboratory analysis and can only be off-line measured. Therefore, in-situ, on-line and sensitive methods for monitoring information about the agricultural environment and crops are urgently needed.
With the development of material science, manufacturing technology and spectroscopy, more and more fast and high-precision spectroscopic methods and sensors are applied in the agricultural environment, livestock breeding and crop. Especially with the development of spectral enhancement, image processing and deep learning techniques, the study of the advanced spectroscopic methods to rapidly and precisely detect heavy metals and nutrients in the soil, harmful gas, water pollutant and crop stress will reduce the probability of subjective error judgment and improves agricultural management and production efficiency.
Application of spectroscopy in an agricultural environment and livestock breeding is a research topic of Frontiers in Physics that publishes original research on the field of agricultural physics. And the aim of this research topic is to explore novel and groundbreaking spectroscopic methods to rapidly and in-situ detect information about the agricultural environment, livestock breeding and crop stress, which could be beneficial in the development of Intelligent agriculture. And the research topic welcomes submissions including but not limited to:
• Spectroscopic detection of nutrients and heavy metals in soil;
• Spectroscopic evaluation of crop growth and disease;
• Spectroscopic detection of pollutants in agricultural water;
• Laser absorption spectrum for detection harmful gas in livestock;
• Thermal infrared technology and machine vision in livestock breeding;
Intelligent agriculture is the inevitable trend of future agriculture. As the brain of intelligent agriculture, advanced sensors determine the degree of agricultural environment and crop perception. However, an outstanding problem is the long-standing lack of effective sensing and monitoring tools in agriculture. Most information still relies on time-consuming and complex laboratory analysis and can only be off-line measured. Therefore, in-situ, on-line and sensitive methods for monitoring information about the agricultural environment and crops are urgently needed.
With the development of material science, manufacturing technology and spectroscopy, more and more fast and high-precision spectroscopic methods and sensors are applied in the agricultural environment, livestock breeding and crop. Especially with the development of spectral enhancement, image processing and deep learning techniques, the study of the advanced spectroscopic methods to rapidly and precisely detect heavy metals and nutrients in the soil, harmful gas, water pollutant and crop stress will reduce the probability of subjective error judgment and improves agricultural management and production efficiency.
Application of spectroscopy in an agricultural environment and livestock breeding is a research topic of Frontiers in Physics that publishes original research on the field of agricultural physics. And the aim of this research topic is to explore novel and groundbreaking spectroscopic methods to rapidly and in-situ detect information about the agricultural environment, livestock breeding and crop stress, which could be beneficial in the development of Intelligent agriculture. And the research topic welcomes submissions including but not limited to:
• Spectroscopic detection of nutrients and heavy metals in soil;
• Spectroscopic evaluation of crop growth and disease;
• Spectroscopic detection of pollutants in agricultural water;
• Laser absorption spectrum for detection harmful gas in livestock;
• Thermal infrared technology and machine vision in livestock breeding;