LK-1820 In-situ Dynamic Phenotype Monitoring System for Plant Stomata

              LK-1820 In-situ Dynamic Phenotype Monitoring System for Plant Stomata

Stomata are the main outlet for water to be discharged from the body to the outside during transpiration, and also the main channel for photosynthesis and respiration to exchange gas with the outside world, which affects the processes of photosynthesis, respiration and transpiration of plants, and influences the global carbon and water cycle.

LK-1820 In-situ Dynamic Phenotype Monitoring System for Plant Stomata is a device for dynamic phenotypic monitoring of stomata in living plants, featuring real-time imaging, simultaneous analysis, convenient operation, etc. It can achieve 24-hour round-the-clock observation of stomatal behaviours and real-time image analysis, and provides a low-cost, fully automated, high-throughput phenotyping solution for phenotyping of plant stomata in response to drought and other adversities under the microscopic field of view. In vivo non-destructive testing programme. The system is based on modern Internet of Things (IoT) technology, coupled with multi-camera stomatal continuous monitoring terminals, and develops deep learning image analysis algorithms, which can realise large-scale, multi-species cluster deployment of stomatal monitoring terminals. It is widely used in crops such as wheat, rice, corn, cotton and oilseed rape; and forest trees such as ginkgo, sycamore and peach trees.

Technical Specifications

• Power: 40W;
• Weight: 5Kg
• Microscope size: 12cm long, 3.6cm wide
• Operating temperature: -10℃~40℃;
• Relative humidity: 40%~80% non-condensing;
• Sampling frequency: 5~30 frames/sec;
• Operating range: single-leaf scale;
• data format: AVI, MP4 and other formats;
• resolution: 1600×1200, 1280×1024dpi
• Monitorable species: crops such as wheat, rice, maize, cotton and oilseed rape; forest trees such as ginkgo, sycamore and peach tree

Analysis metrics

• Total number of stomata: the number of all stomata in the image identified by machine vision based on the YOLO algorithm
• Number of open stomata: the number of open stomata identified by machine vision based on the YOLO algorithm.
• number of closed stomata: number of closed stomata identified by machine vision based on the YOLO algorithm.
• Stomatal (guard cell) length: average length of guard cells in the field of view.
• stomatal (guard cell) width: average height of guard cells in the field of view
• Individual stomatal area: the true stomatal area (without defence cells) based on the semantic segmentation algorithm.
• Total stomatal area: the sum of all stomatal areas in the field of view (excluding guard cells) based on the semantic segmentation algorithm
• stomatal opening and closing velocity: the rate of change of stomatal opening area per unit time

Application

Stomatal Observation Cluster in Wheat Tolerant Cultivation Experimental Field, Nanjing Agricultural University, China