Method for predicting potential maximum photosynthetic capacity of plants based on characteristic wavelengths

Abstract

The invention discloses a method for predicting potential photosynthetic capacity of plants based on characteristic wavelengths. The method comprises the following steps: setting culture environmentswith the same temperature, humidity and CO2 concentration under six illumination gradients, randomly selecting plant leaves as experimental samples when plants under different illumination treatmentsare different, and respectively measuring dark fluorescence parameters and visible-near infrared reflection spectra of the plant leaves as sample data; rejecting abnormal samples by adopting a Monte Carlo method, and randomly dividing a training set and a test set according to a ratio of 4: 1; extracting characteristic wavelengths by adopting a correlation coefficient method and a continuous projection method; establishing a plant potential maximum photosynthetic capability prediction model by taking the reflection spectrum corresponding to the characteristic wavelength as an input and takingthe plant potential maximum photosynthetic capability as an output and utilizing a radial basis function neural network optimized by a genetic algorithm. The potential maximum photosynthetic capacityof the plant is predicted, and a theoretical basis is provided for rapid, lossless and low-cost monitoring of the potential maximum photosynthetic capacity of the plant.

Description

Technical field [0001] The invention belongs to the technical field of intelligent agriculture, and relates to the study of the maximum potential photosynthetic capacity of plants, in particular to a method for predicting the potential maximum photosynthetic capacity of plants based on characteristic wavelengths. Background technique [0002] Photosynthesis is a way of plant material accumulation, which directly affects plant growth and fruit yield. Plant photosynthesis is not only affected by plant growth environment, but also by its physiological state. There are obvious differences in the chlorophyll content and light energy utilization efficiency of plant leaves under different physiological conditions, which directly affect their photosynthetic rate. The light energy absorbed by plants is consumed in three forms: photosynthesis, chlorophyll fluorescence and heat. Therefore, chlorophyll fluorescence can reflect plant photosynthesis. At present, chlorophyll fluorescence tec...

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However, the current research mostly uses vegetation index modeling, which contains limited reflectance spectral informa

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