Crop nitrogen nutrition index estimation model evaluation method based on digital image

Abstract

The invention discloses a crop nitrogen nutrition index estimation model evaluation method based on a digital image, and the method comprises the steps: setting field plot tests with different nitrogen fertilizer application amounts, obtaining a crop canopy image, and determining the nitrogen nutrition index; segmenting the canopy image, and calculating image feature parameters; constructing a data set according to the nitrogen nutrition indexes and the image characteristic parameters, randomly grouping the data set, segmenting the data set into a training set and a test set, establishing a nitrogen nutrition index estimation model according to the test set, evaluating the established estimation model according to the training set, calculating model evaluation indexes, and performing statistical analysis on the model evaluation indexes, and screening an optimal crop nitrogen nutrition index estimation model. According to the method, the existing data set is subjected to scientific andmassive random grouping and inspection, so that the crop nitrogen nutrition diagnosis model can be evaluated more effectively, the influence of data set splitting on model performance evaluation is reduced, and the model performance evaluation is more stable.

Description

technical field [0001] The invention relates to a crop nitrogen nutrition index evaluation method, in particular to an evaluation method of a crop nitrogen nutrition index estimation model based on a digital image. Background technique [0002] Nitrogen is one of the important nutrients that affect crop growth. When the nitrogen fertilizer application rate is lower than the optimum value, increasing nitrogen fertilizer can effectively increase crop yield. However, excessive or unsuitable nitrogen fertilizer application will lead to problems such as delayed crop senescence and environmental pollution. Real-time estimation of nitrogen nutrition indicators for crops is helpful to clarify the nitrogen nutrition status of crops and formulate a reasonable fertilization plan. [0003] Digital cameras are the most common image acquisition devices in daily life. Acquiring RGB images is much less expensive than other spectroscopic instruments, and image coverage can be extended fro...

AI Technical Summary

Problems solved by technology

However, due to the difficulty in obtaining data from field experiments, the amount of data in most of these studies is limited, and the model evaluation process is easily disturbed by the selection of data sets and the modeling process. The data set segmentation method in the study will have a significant impact on the accuracy of model evaluation. , it is difficult to evaluate the pros and cons of a certain modeling method

Therefore, when estimating crop nitrogen nutrition indicators in different studies, different optimal estimation models are often selected, which reduces the reference significance of the research results and cannot accurately evaluate the generalization performance of the model.

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