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Smart Agriculture ›› 2020, Vol. 2 ›› Issue (1): 111-120.doi: 10.12133/j.smartag.2020.2.1.202001-SA001

• Topic--Agricultural Remote Sensing and Phenotyping Information Acquisition Analysis • Previous Articles     Next Articles

Recognition method for corn nutrient based on multispectral image and convolutional neural network

Wu Gang1, Peng Yaoqi1, Zhou Guangqi1, Li Xiaolong1, Zheng Yongjun1(), Yan Haijun2   

  1. 1.College of Engineering, China Agricultural University, Beijing 100083, China
    2.College of Water Resources & Civil Engineering, China Agricultural University, Beijing 100083, China
  • Received:2020-01-03 Revised:2020-03-08 Online:2020-03-30
  • corresponding author:  Zheng YongjunEmail: zyj@cau.edu.cn
  • About author:Wu Gang,Email:wugang@cau.edu.cn
  • Supported by:
    National Key Research and Development Program of China(2017YFD0201502)


Excessive application of water and fertilizer not only causes resources serious waste of, but also causes serious environmental pollution. The implementation of precision irrigation and fertilization can effectively reduce nutrient loss and environmental pollution, save irrigation water and improve the utilization rate of water and fertilizer resources, which is one of the important ways to promote the sustainable development of agriculture. The use of the integrated water-fertilizer equipment can effectively improve the utilization rate of water-fertilizer resources, but it is necessary to know the nutritional status of crops and water-fertilizer demand before operation. To acquire the information by hand-held measuring instruments, there are some disadvantages, such as poor timeliness and high labor intensity. In response to the above problems, this study took the common corn crop as an example, used the DJI Phantom III drone to carry RedEdge-M multispectral camera to collect multispectral images of corn crops over the fields, and measured nitrogen and moisture content of corn plants by YLS-D series plant nutrition tester. Based on this information, the collected images were divided into 3 levels, each level contains 530 five channel images (2650 single channel images), including 480 five channel images (2400 single channel images) in the training set and 50 five channel images (250 single channel images) in the verification set, and a method of identifying the nutritional status of corn crops based on convolutional neural network was proposed. Based on the TensorFlow deep learning framework, ResNet18 convolution neural network model was constructed. By entering color image data and five-channel multispectral image data into the model, the nutritional status recognition model of corn plant suitable for color image and multispectral image was trained, and the experimental results showed that the trained model could be used to recognize the multispectral images of corn, and the nutritional status of corn, topdressing guidance and GPS information could be outputted, the correct rate of the recognition color image model in the verification set was 84.7%. The correct rate of identifying multispectral image model in the verification set was 90.5%, the average time of model training was 4.5h, and the average time of recognizing a five channel image is 3.56 seconds, which can detect the nutritional status of corn crops quickly and undamaged, and provides a theoretical and technical basis for the accuracy of the application of water fertilizer in intelligent agriculture.

Key words: intelligent agriculture, convolution neural network, multispectral image, corn crop, nutritional recognition method

CLC Number: