About this Research Topic
The wide applications of plant sensors and imaging in agriculture have promoted the process of automation, intelligence, and industrialization of agricultural production. Plant sensor and imaging (including optical sensors, temperature sensor, humidity sensor, position sensor, etc.) are ideal for accurate perception, and time-series phenotyping in precision agriculture. The accurate perception and time-series phenotyping could timely access to environmental information and plant growth status, such as environmental temperature, humidity, light intensity, leaf area, and angle. Furthermore, the classification, recognition, and prediction of the plant will be realized by combining with advanced technologies.
With the development of science and technology and the improvement of sensor manufacturing technology, more and more high-precision sensors are applied in agriculture. Especially with the development of deep learning, the study of the advanced technologies using sensors to perform plant monitoring, plant identification and classification, and analysis and prediction of plant growth and development will reduce the probability of subjective error judgment and improves agricultural management and production efficiency. For example, a fruit location method was proposed to improve fruit picking efficiency and reduce mechanical damage.
Plant sensors and imaging is a section of Frontiers in Physics that publishes original research on the field of agricultural physics. And the aim of this section is to explore novel and groundbreaking ideas of plant sensors for imaging, detection, recognition, and segmentation in agricultural physics, which could be beneficial in the research areas of theory, experimentation, applications, and data analysis for plant sensors and imaging. And the section welcomes submissions including but not limited to:
• Spectroscopic imaging in agriculture;
• Optical imaging and processing for plants;
• Hybrid techniques (tracking control/imaging in agriculture, path planning/imaging in agriculture, imaging in agriculture/image processing);
• Sensor detection and forecasting ;
• Crop imaging and classification;
• Detection and prevention of plant disease ;
• Application of time-series imagery in analysis and prediction of plant growth and development.
Research on theoretical, experimental, and computational techniques are all encouraged. The section is also open for submissions from Frontiers in Physiology.
With the development of science and technology and the improvement of sensor manufacturing technology, more and more high-precision sensors are applied in agriculture. Especially with the development of deep learning, the study of the advanced technologies using sensors to perform plant monitoring, plant identification and classification, and analysis and prediction of plant growth and development will reduce the probability of subjective error judgment and improves agricultural management and production efficiency. For example, a fruit location method was proposed to improve fruit picking efficiency and reduce mechanical damage.
Plant sensors and imaging is a section of Frontiers in Physics that publishes original research on the field of agricultural physics. And the aim of this section is to explore novel and groundbreaking ideas of plant sensors for imaging, detection, recognition, and segmentation in agricultural physics, which could be beneficial in the research areas of theory, experimentation, applications, and data analysis for plant sensors and imaging. And the section welcomes submissions including but not limited to:
• Spectroscopic imaging in agriculture;
• Optical imaging and processing for plants;
• Hybrid techniques (tracking control/imaging in agriculture, path planning/imaging in agriculture, imaging in agriculture/image processing);
• Sensor detection and forecasting ;
• Crop imaging and classification;
• Detection and prevention of plant disease ;
• Application of time-series imagery in analysis and prediction of plant growth and development.
Research on theoretical, experimental, and computational techniques are all encouraged. The section is also open for submissions from Frontiers in Physiology.
Keywords: Agricultural physics, Spectroscopic imaging, Image processing, Detection, Classification
Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.
