In situ dynamic phenotype monitoring system for plant stomata

In situ dynamic phenotype monitoring system for plant stomataIntroduction:

1.1 Description and purpose

Stomata are the main outlet for water to be excreted from the body to the outside during plant transpiration, and also the main channel for photosynthesis, respiration, and gas exchange with the outside world. They affect the processes of photosynthesis, respiration, and transpiration in plants and affect the global carbon water cycle.

In situ dynamic phenotype monitoring system for plant stomataIt is a device used for monitoring the dynamic phenotype of stomata in living plants. It has the characteristics of real-time imaging, synchronous analysis, and convenient operation. It can achieve 24-hour uninterrupted observation of stomatal behavior and real-time image analysis, providing a low-cost, fully automatic, high-throughput non-destructive testing solution for the phenotype analysis of plant stomata in response to stress such as drought in the micro field. The system is based on modern Internet of Things technology, coupled with multiple machine position continuous monitoring terminals for stomata, and develops deep learning image analysis algorithms to achieve cluster deployment of large-scale and multi species stomata monitoring terminals.

1.2 Technical features:

Power: 40W;

Weight: 5Kg;

Microscope size: 12cm in length and 3.6cm in width;

Working temperature: -10 ℃~40 ℃;

Relative humidity: 40%~80% without condensation;

Sampling frequency: 5-30 frames per second;

Scope of assignment: Single leaf scale;

Data formats: AVI, MP4, and other formats;

Resolution: 1600 × 1200, 1280 × 1024dpi;

Monitored species: crops such as wheat, rice, corn, cotton, and rapeseed; Ginkgo, wutong, peach trees and other trees.

Analysis indicators:

1.3 Customer Cases

Observation cluster of stomata in wheat waterlogging tolerant cultivation experimental field at Nanjing Agricultural University

Equipment related articles:

1. Sun Zhuangzhuang, Jiang Dong *, Cai Jian, Wang Xiao, Zhou Qin, Huang Mei, Dai Tingbo, Cao Weixing Automatic recognition and counting technology of stomata in monocotyledonous crop leaves [J]. Journal of Agricultural Engineering, 2019 35(23).

2. Zhuangzhuang Sun, Yunlin Song, Qing Li, Jian Cai, Qin Zhou, Mei Huang, Dong Jiang*. An integrated method for tracking and monitoring stomata dynamics from microscope videos . Plant Phenomics, 2021, 9835961.

3. In situ determination of stomatal dynamic behavior in wheat elucidates drought priming mechanisms，Under review.

4. Revealing circadian rhythm of stomatal movement based on video data in situ and deep learning in wheat ((Triticum aestivum L.)，Preparation.