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Research Article

Above-ground Biomass Wheat Estimation: Deep Learning with UAV-based RGB Images

ORCID Icon, ORCID Icon, , , &
Article: 2055392 | Received 10 Oct 2020, Accepted 16 Mar 2022, Published online: 26 Mar 2022

References

  • Agisoft, L. 2018. Agisoft photoscan user manual. Professional edition, version 1.4, 1, 124.
  • Ballesteros, R., J. F. Ortega, D. Hernandez, and M. A. Moreno. 2018. Onion biomass monitoring using UAV-based RGB imaging. Precision Agriculture 19 (5):840–2557.
  • Bendig, J., K. Yu, H. Aasen, A. Bolten, S. Bennertz, J. Broscheit, M. L. Gnyp, and G. Bareth. 2015. Combining UAV-based plant height from crop surface models, visible, and near infrared vegetation indices for biomass monitoring in barley. International Journal of Applied Earth Observation and Geoinformation 39:79–87.
  • Bergstra, J., D. Yamins, and D. Cox. 2013. Making a science of model search: Hyperparameter optimization in hundreds of dimensions for vision architectures. International Conference on Machine Learning 28: 115–23.
  • Brahimi, M., K. Boukhalfa, and A. Moussaoui. 2017. Deep learning for tomato diseases: Classification and symptoms visualization. Applied Artificial Intelligence 31 (4):299–315.
  • Brocks, S., and G. Bareth. 2018. Estimating barley biomass with crop surface models from oblique RGB imagery. Remote Sensing 10 (2):268.
  • Chai, T., and R. R. Draxler. 2014. Root mean square error (RMSE) or mean absolute error (MAE)? Geoscientific Model Development Discussions 7 (1):1525–34.
  • Chollet, F. et al. 2015. Keras: Deep learning library for theano and tensorflow. https://keras.io/getting_started/faq/#how-should-i-cite-keras
  • Fu, Y., G. Yang, J. Wang, X. Song, and H. Feng. 2014. Winter wheat biomass estimation based on spectral indices, band depth analysis and partial least squares regression using hyperspectral measurements. Computers and Electronics in Agriculture 100:51–59.
  • Gaso, D. V., A. G. Berger, and V. S. Ciganda. 2019. Predicting wheat grain yield and spatial variability at field scale using a simple regression or a crop model in conjunction with Landsat images. Computers and Electronics in Agriculture 159:75–83.
  • Gavrilov, A., A. Jordache, M. Vasdani, and J. Deng. 2018. Convolutional neural networks: Estimating relations in the ising model on overfitting. 2018 IEEE 17th International Conference on Cognitive Informatics & Cognitive Computing (ICCI*CC). Berkeley, CA, USA. doi:10.1109/ICCI-CC.2018.8482067.
  • Gopal, M., and Bhargavi. 2019. Performance evaluation of best feature subsets for crop yield prediction using machine learning algorithms. Applied Artificial Intelligence 33 (7):621–42.
  • Ighalo, J. O., C. A. Igwegbe, and A. G. Adeniyi. 2021. Multi-layer perceptron artificial neural network (MLP-ANN) prediction of biomass higher heating value (hhv) using combined biomass proximate and ultimate analysis data. Modeling Earth Systems and Environment 7 3 1–15.
  • Kamilaris, A., and F. X. Prenafeta-Boldú. 2018. Deep learning in agriculture: A survey. Computers and Electronics in Agriculture 147:70–90.
  • Khan, A., A. Sohail, U. Zahoora, and A. S. Qureshi. 2019. A survey of the recent architectures of deep convolutional neural networks. CoRR, abs/1901.06032.
  • Krizhevsky, A., I. Sutskever, and G. E. Hinton. 2017. ImageNet classification with deep convolutional neural networks (6th ed.). Communications of the ACM 60(6):84–90.
  • Long, J., E. Shelhamer, and T. Darrell. 2015. Fully convolutional networks for semantic segmentation. 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Boston, MA, USA.
  • Messinger, M., G. Asner, and M. Silman. 2016. Rapid assessments of Amazon forest structure and biomass using small unmanned aerial systems. Remote Sensing 8 (8):615.
  • Montgomery, D. C., and G. C. Runger. 2010. Applied statistics and probability for engineers. Nova Jersey, EUA.: John Wiley & Sons.
  • Nielsen, M. A. 2015. Neural networks and deep learning, vol. 25. San Francisco, CA, USA: Determination press.
  • Pedregosa, F., G. Varoquaux, A. Gramfort, V. Michel, B. Thirion, O. Grisel, M. Blondel, P. Prettenhofer, R. Weiss, V. Dubourg, et al. 2011. Scikit-learn: Machine learning in python. Journal of Machine Learning Research 12 (Oct):2825–30.
  • QGIS Development Team 2018. Qgis geographic information system. open source geospatial foundation project. http://qgis.osgeo.org.
  • Santos, M. S., J. P. Soares, P. H. Abreu, H. Araujo, and J. Santos. 2018. Cross- validation for imbalanced datasets: Avoiding overoptimistic and overfitting approaches [research frontier]. IEEE Computational Intelligence Magazine 13 (4):59–76.
  • Schreiber, L. V., J. G. A. Amorim, and A. Parraga. 2020. (2020) Brazilian wheat dataset. Mendeley Data V1. doi:10.17632/3ntkg88d4d.1
  • Van Rossum, G., and F. L. Drake Jr. 1995. Python tutorial. Amsterdam, Netherlands: Centrum voor Wiskunde en Informatica.
  • Wang, L., X. Zhou, X. Zhu, Z. Dong, and W. Guo. 2016. Estimation of biomass in wheat using random forest regression algorithm and remote sensing data. The Crop Journal 4 (3):212–19.
  • Willmott, C., and K. Matsuura. 2005. Advantages of the mean absolute error (MAE) over the root mean square error (RMSE) in assessing average model performance. Climate R Esearch 30:79–82.
  • Yue, J., G. Yang, C. Li, Z. Li, Y. Wang, H. Feng, and B. Xu. 2017. Estimation of winter wheat above-ground biomass using unmanned aerial vehicle-based snapshot hyperspectral sensor and crop height improved models. Remote Sensing 9 (7):708.
  • Zounemat-Kermani, M. 2014. Principal component analysis (pca) for estimating chlorophyll concentration using forward and generalized regression neural networks. Applied Artificial Intelligence 28 (1):16–29.