Estimation of corn yield based on hyperspectral imagery and convolutional neural network

Wei Yang, Tyler J Nigon, Ziyuan Hao, Gabriel Dias Paiao, Fabián G. Fernández, David Mulla, Ce Yang

Research output: Contribution to journalArticlepeer-review

52 Scopus citations


Corn is an important food crop in the world, widely distributed in many countries because of its excellent environmental adaptability. Moreover, corn is an important feed source for animal production and it is an indispensable raw material for many different industries. With increasing human population and decreasing arable land, there is an increased focus on increasing yield of corn. Convolutional neural network (CNN) analysis can be used for non-destructive yield prediction and is well suited for classification and feature extraction. The overall objective of this experiment was to use hyperspectral imagery to train a CNN classification model to estimate corn grain yield. High resolution hyperspectral imagery was captured at five corn growth stages - V5 (five leaves with visible leaf collars), V8 (eight leaves with visible leaf collars), V10 (ten leaves with visible leaf collars), V12 (12 leaves with visible leaf collars), and R2 (blister stage). Hyperspectral imagery was denoised using the wavelet analysis method, then was used to train and validate the CNN model. The spectral information reflecting the internal characteristics and the spatial information provided by the color image (red, green and blue bands extracted from hyperspectral image) reflecting the external characteristics of corn growth are extracted for modelling and verification. The results show that the spectral and color image-based integrated CNN model has a classification accuracy of 75.50%. In contrast, the accuracy of a one-dimensional CNN model based only on spectral information or a two-dimensional CNN model based only on color image information were 60.39% and 32.17%, respectively. The integrated CNN model (spectral information plus color image information) is better than results of the individual one-dimensional CNN or two-dimensional CNN models. In addition. The Kappa coefficient of integrated CNN model is 0.69, which indicates a high consistency of classification. Comprehensive use of spectral information and color image information, which represent information about the inner and outer corn canopy can provide more accurate corn yield prediction than one-dimensional or two-dimensional CNN models.

Original languageEnglish (US)
Article number106092
JournalComputers and Electronics in Agriculture
StatePublished - May 1 2021

Bibliographical note

Funding Information:
This study was partially supported by NSF/USDA National Robotics Initiative grant INT (1924800) of USA and National Natural Science Foundation of China (31801265).

Publisher Copyright:
© 2021 Elsevier B.V.


  • CNN
  • Corn yield
  • Deep learning theory
  • Hyperspectral Image


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