Rice nitrogen nutrition estimation method based on canopy image feature derivation

Abstract

The invention discloses a rice nitrogen nutrition estimation method based on canopy image feature derivation. The method comprises the steps of acquiring a rice elongation stage canopy image in a unitarea of a field or in a plant with a certain number of holes above a rice canopy through imaging equipment; processing the canopy image by using a digital image processing method to obtain a rice canopy coverage (CC), using the rice canopy coverage as an image feature value, using feature derivation, expanding the image canopy coverage as an independent variable, and constructing a regression relational expression with the rice nitrogen nutrition index; and verifying the trained model by collecting more rice canopy coverage and rice nitrogen nutrition index samples. According to the method, the nitrogen nutrition of the rice is evaluated by utilizing the image feature values, the prediction precision and universality of the model can be effectively improved, and an effective means is provided for monitoring the growth vigor and nutrition of the rice.

Description

technical field [0001] The invention belongs to the field of rapid and non-destructive monitoring of crop growth status based on digital image technology, in particular to a method for estimating rice nitrogen nutrition based on canopy image feature derivation. Background technique [0002] Rice is the most important food crop in my country, and its yield formation is closely related to nutrient absorption. Nitrogen is one of the indispensable nutrient elements for rice growth and development. The amount of nitrogen fertilizer used in rice in my country accounts for 37% of the total amount of nitrogen fertilizer used in rice in the world, while the average nitrogen utilization rate is only 35%. Studies have shown that as the amount of nitrogen fertilizer used in rice production continues to rise, excessive nitrogen fertilizer input reduces nitrogen fertilizer nutrient use efficiency, and nitrogen fertilizer loss also directly and indirectly leads to a series of adverse envir...

AI Technical Summary

Problems solved by technology

However, image technology diagnosis mostly relies on prediction models of biophysical and chemical parameters. These prediction models are often established through semi-empirical relationships. The prediction accuracy of the model itself is difficult to improve and its universality is not strong.

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