As one of the important food products in our country, the health detection of maize during its growing period has been an important problem in agricultural production. In this paper, the leaves of maize grown under the influence of different factors were taken as the research object, and the ASD spectrometer was used to collect the spectra of the leaves. The derivative (D) of the original spectral data was processed, and the compressed sensing (CS) was introduced to solve the phenomenon that the spectral data after derivative approach to 0 infinitely, the iterative re-weighted least squares (IRLS) data reconstruction method is used to restore the spectral data. Then competitive adaptive re-weighted sampling (CARS) was used to extract the spectral features, and the multi-layer perceptron (MLP) was used to extract the spectral features, in order to identify the factors affecting the poor growth of crops. The accuracy of the D-CS-CARS-MLP model generated in this experiment can be as high as 99%, and the model can be used to identify a variety of factors. After verification, the D-CS-CARS-MLP model has good stability and precision, which provides a new idea and method for monitoring the healthy growth of vegetation.