Journal of Bio-Environment Control (생물환경조절학회지)

The Korean Society for Bio-Environment Control (한국생물환경조절학회)
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Estimation of Sweet Pepper Crop Fresh Weight with Convolutional Neural Network

합성곱 신경망을 이용한 온실 파프리카의 작물 생체중 추정

  • Moon, Taewon (Department of Agriculture, Forestry and Bioresources (Horticultural Science and Biotechnology), Seoul National University) ;
  • Park, Junyoung (Department of Agriculture, Forestry and Bioresources (Horticultural Science and Biotechnology), Seoul National University) ;
  • Son, Jung Eek (Department of Agriculture, Forestry and Bioresources (Horticultural Science and Biotechnology), Seoul National University)
  • 문태원 (서울대학교 농림생물자원학부 원예생명공학전공 대학원) ;
  • 박준영 (서울대학교 농림생물자원학부 원예생명공학전공 대학원) ;
  • 손정익 (서울대학교 농림생물자원학부 원예생명공학전공)
  • Received : 2020.09.04
  • Accepted : 2020.09.14
  • Published : 2020.10.31
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Abstract

Various studies have been attempted to estimate and measure the fresh weight of crops. However, no studies have used raw images of sweet peppers to estimate fresh weight. Recently, image processing research using convolution neural network (CNN) that can use raw data is increasing. In this study, the crop fresh weight was estimated by using the images of sweet peppers as inputs of CNN. The experiment was performed in a greenhouse growing sweet pepper (Capsicum annuum L.). The fresh weight, the output of the CNN, was regressed based on the data collected through destructive investigation. The highest coefficient of determination (R2) of the trained CNN was 0.95. The estimated fresh weight showed a very similar trend to the actual measured value.

작물의 생체중을 추정하기 위해 다양한 연구가 시도되었지만, 이미지를 활용하여 생체중을 추정한 예는 없었다. 최근 합성곱 신경망을 사용한 이미지 처리 연구가 늘고 있으며, 합성곱 신경망은 미가공 데이터를 그대로 사용할 수 있다. 본 연구에서는 합성곱 신경망을 이용하여 미가공 데이터 상태인 특정 시점의 파프리카 이미지를 입력으로 작물의 생체중을 추정하도록 학습하였다. 실험은 파프리카(Capsicum annuum L.)를 재배하는 온실에서 수행하였다. 합성곱 신경망의 출력값인 생체중은 파괴조사를 통해 수집한 데이터를 기반으로 회귀 분석하였다. 학습된 합성곱 신경망의 결정 계수(R2)의 최고값은 0.95로 나타났다. 생체중 추정값은 실제 측정값과 매우 유사한 경향성을 보여주었다.

Keywords

References

  1. Abadi M., A. Agarwal, P. Barham, E. Brevdo, Z. Chen, C. Citro, G.S. Corrado, A. Davis, J. Dean, M. Devin, and et al. 2016. Tensorflow: Large-scale machine learning on heterogeneous distributed systems. arXiv Preprint arXiv: 1603.04467.
  2. Abramoff M.D., P.J. Magalhaes, and S.J. Ram. 2004. Image processing with Image J. Biophotonics Int. 11:36-42.
  3. Anwar S.M., M. Majid, A. Qayyum, M. Awais, M. Alnowami, and M.K. Khan. 2018. Medical image analysis using convolutional neural networks: a review. J. Med. Syst. 42:226. https://doi.org/10.1007/s10916-018-1088-1
  4. Berman M.E., and T.M. DeJong. 1996. Water stress and crop load effects on fruit fresh and dry weights in peach (Prunus persica). Tree Physiology 16:859-864. https://doi.org/10.1093/treephys/16.10.859
  5. Chang A., A. Dai, T. Funkhouser, M. Halber, M. Niesner, M. Savva, S. Song, A. Zeng, Y. Zhang. 2017. Matterport3D: Learning from RGB-D data in indoor environments. arXiv Preprint arXiv:1709.06158.
  6. Cho Y.Y., S. Oh, M.M. Oh, and J.E. Son. 2007. Estimation of individual leaf area, fresh weight, and dry weight of hydroponically grown cucumbers (Cucumis sativus L.) using leaf length, width, and SPAD value. Sci. Hortic. 111:330-334. https://doi.org/10.1016/j.scienta.2006.12.028
  7. Dadhwal V.K. 2003. Crop growth and productivity monitoring and simulation using remote sensing and GIS. In M.V.K. Sivakumar, P.S. Roy, K. Harmsen, S.K. Saha, eds, Satellite Remote Sensing and GIS Applications in Agricultural Meteorology. WMO, Switzerland. p. 263-289.
  8. He K., X. Zhang, S. Ren, and J. Sun. 2016. Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition p. 770-778.
  9. Huang P., L. de-Bashan, T. Crocker, J.W. Kloepper, and Y. Bashan. 2017. Evidence that fresh weight measurement is imprecise for reporting the effect of plant growth-promoting (rhizo) bacteria on growth promotion of crop plants. Biol. Fertil. Soils 53:199-208. https://doi.org/10.1007/s00374-016-1160-2
  10. Koppula H.S., R. Gupta, and A. Saxena. 2013. Learning human activities and object affordances from RGB-D videos. Int. J. Rob. Res. 32:951-970. https://doi.org/10.1177/0278364913478446
  11. Kingma D., and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv Preprint arXiv:1412.6980v9.
  12. Krizhevsky A. I. Sutskever, and G.E. Hinton. 2012. Imagenet classification with deep convolutional neural networks. In: Proceedings of Advances in Neural Information Processing Systems, December, Lake Tahoe, NV, USA, 1097-1105.
  13. Lee J.W., and J.E. Son. 2019. Nondestructive and continuous fresh weight measurements of bell peppers grown in soilless culture systems. Agronomy 9:652. https://doi.org/10.3390/agronomy9100652
  14. Marcelis L.F.M., and L.C. Ho. 1999. Blossom-end rot in relation to growth rate and calcium content in fruits of sweet pepper (Capsicum annuum L.). J. Exp. Bot. 50:357-363. https://doi.org/10.1093/jxb/50.332.357
  15. McCann M.T., K.H. Jin, and M. Unser. 2017. Convolutional neural networks for inverse problems in imaging: A review. IEEE Signal Process. Mag. 34:85-95. https://doi.org/10.1109/MSP.2017.2739299
  16. Mokhtarpour H., C.B. Teh, G. Saleh, A.B. Selamat, M.E. Asadi, and B. Kamkar. 2010. Non-destructive estimation of maize leaf area, fresh weight, and dry weight using leaf length and leaf width. Commun. Biometry Crop Sci. 5:19-26.
  17. Mnih V., K. Kavukcuoglu, D. Silver, A.A. Rusu, J. Veness, M.G. Bellemare, A. Graves, M. Riedmiller, A.K. Fidjeland, G. Ostrovski, and et al. 2015. Human-level control through deep reinforcement learning. Nature 518:529-533. https://doi.org/10.1038/nature14236
  18. Picon A., A. Alvarez-Gila, M. Seitz, A. Ortiz-Barredo, J. Echazarra, and A. Johannes. 2019. Deep convolutional neural networks for mobile capture device-based crop disease classification in the wild. Comput. Electron. Agric. 161: 280-290. https://doi.org/10.1016/j.compag.2018.04.002
  19. Quinonero-Candela J., C.E. Rasmussen, and C.K. Williams. 2007. Approximation methods for Gaussian process regression. In B. Leon, C. Olivier, D.C. Dennis, W. Jason, eds, Large-scale kernel machines. MIT Press, US, pp 203-223.
  20. Seginer I. 1997. Some artificial neural network applications to greenhouse environmental control. Comput. Electron. Agric. 18:167-186. https://doi.org/10.1016/S0168-1699(97)00028-8
  21. Shen W., M. Zhou, F. Yang, D. Yu, D. Dong, C. Yang, Y. Zang, and J. Tian. 2017. Multi-crop convolutional neural networks for lung nodule malignancy suspiciousness classification. Pattern Recognit. 61:663-673. https://doi.org/10.1016/j.patcog.2016.05.029
  22. Shi P.J., L. Chen, C. Hui, and H.D. Grissino-Mayer. 2016. Capture the time when plants reach their maximum body size by using the beta sigmoid growth equation. Ecol. Modell. 320:177-181. https://doi.org/10.1016/j.ecolmodel.2015.09.012
  23. Simonyan K., and A. Zisserman. 2014. Very deep convolutional networks for large-scale image recognition. arXiv Preprint arXiv:1409.1556.
  24. South P.F., A.P. Cavanagh, H.W. Liu, and D.R. Ort. 2019. Synthetic glycolate metabolism pathways stimulate crop growth and productivity in the field. Science 363:6422.
  25. Rawat W. and Z. Wang. 2017. Deep convolutional neural networks for image classification: A comprehensive review. Neural Comput. 29:2352-2449. https://doi.org/10.1162/neco_a_00990