A recognition method of multispectral images of soybean canopies based on neural network

Multispectral images of soybean canopies can reflect plant physiological information and growth status effectively, which is of great significance for soybean high-quality breeding, scientific cultivation, and fine management. At present, it is uneven of the gray level difference of the soybean multispectral images occurred in the leaf edge, and is also small of the gray level difference between the target and the background, resulting in inaccurate recognition of the soybean canopies from the multispectral images. Thus, a multispectral images' recognition method of soybean canopies was proposed based on the neural network. First, the method of Gaussian smoothing filter was used to preprocess the raw soybean multispectral images (green light, near infrared, red light, red edge, and visible light), which maintained the leaf edge details of the soybean multi spectral image. Second, the feedforward neural network model was established to extract the canopy region in the soybean multispectral images. In addition, image morphology operation was used to improve the recognition effects of the soybean canopy. Finally, four quantitative indexes were used to evaluate the experimental results. The results showed that the average effective segmentation rate of the proposed method was 91.69%, the under segmentation rate was reduced by 33.34%, and the over-segmentation rate was reduced by 48.43%. The difference between the pixel average entropy of the proposed method and the standard canopy image was only 0.2295. The research results can provide not only reliable data for further analysis of physiological and ecological indexes of the soybean canopy, but also technical support for multispectral image recognition of other crop canopies.

Automated summary

This research proposes a neural network-based method for recognizing soybean canopy in multispectral images. By preprocessing the raw images and using image morphology operation, the canopy regions in the images are effectively extracted with minimal difference compared to the standard canopy image.