Jian Yin, Xinying Liu, Yanlong Miao, Yang Gao, Ruicheng Qiu, Man Zhang, Han Li, Minzan Li. Measurement and prediction of tomato canopy apparent[J]. International Journal of Agricultural and Biological Engineering, 2019, 12(5): 156-161. DOI: 10.25165/j.ijabe.20191205.4982
Citation: Jian Yin, Xinying Liu, Yanlong Miao, Yang Gao, Ruicheng Qiu, Man Zhang, Han Li, Minzan Li. Measurement and prediction of tomato canopy apparent[J]. International Journal of Agricultural and Biological Engineering, 2019, 12(5): 156-161. DOI: 10.25165/j.ijabe.20191205.4982

Measurement and prediction of tomato canopy apparent

  • Given the lack of technical conditions and research methods, instruments that can measure the canopy apparent photosynthetic rate have low precision and are rarely studied. Comparative studies on canopy apparent photosynthetic rate and single leaf photosynthetic rate are also relatively few. This study aims to measure and predict the canopy apparent photosynthetic rate of tomato. A canopy apparent photosynthetic rate measuring system, which was comprised of a wireless sensor network (WSN), an assimilation chamber, and a LI-6400XT photosynthetic rate instrument was established. The system was used to determine the greenhouse environmental parameters and CO2 absorptive capacity of the whole growth stage of tomato. A semi-closed assimilation chamber was designed as a side opening to conveniently measure the canopy apparent photosynthetic rate. WSN nodes were placed in the chamber to monitor environmental parameters, including air temperature, air humidity, and assimilation chamber temperature. A grid and pixel conversion method was used to measure the whole plant leaf areas of tomato. As a semi-closed measurement system, the assimilation chamber was used to calculate the canopy apparent photosynthetic rate. To conduct a comparative research on the canopy apparent photosynthetic rate and the single leaf photosynthetic rate, the LI-6400XT portable photosynthesis system was used to measure the single leaf photosynthetic rate, and the support vector machine was used to establish the prediction model of canopy apparent photosynthetic rate. The experimental results indicated that the correlation coefficients of the photosynthesis prediction model in the seeding and flowering stages were 0.9420 and 0.9226, respectively, showing the high accuracy of the SVM model.
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