Tillage Machine Control Based on a Vision System for Soil Roughness and Soil Cover Estimation

  • Peter Riegler-NurscherEmail author
  • Johann Prankl
  • Markus Vincze
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11754)


Soil roughness and soil cover are important control variables for plant cropping. A certain level of soil roughness can prevent soil erosion, but to rough soil prevents good plant emergence. Local heterogeneities in the field make it difficult to get homogeneous soil roughness. Residues, like straw, influences the soil roughness estimation and play an important role in preventing soil erosion. We propose a system to control the tillage intensity of a power harrow by varying the driving speed and PTO speed of a tractor. The basis for the control algorithm is a roughness estimation system based on an RGB stereo camera. A soil roughness index is calculated from the reconstructed soil surface point cloud. The vision system also integrates an algorithm to detect soil cover, like residues. Two different machine learning methods for pixel-wise semantic segmentation of soil cover were implemented, an entangled random forest and a convolutional neural net. The pixel-wise classification of each image into soil, living organic matter, dead organic matter and stone allow for mapping of soil cover during tillage. The results of the semantic segmentation of soil cover were compared to ground truth labelled data using the grid method. The soil roughness measurements were validated using the manual sieve analysis. The whole control system was validated in field trials on different locations.


Stereo camera Soil roughness Soil cover Convolutional neural network 



The research leading to this work has received funding from the Lower Austrian government (WST3-T-140/002-2014). As well as from the Austrian Research Promotion Agency under the program “Bridge 1”.


  1. 1.
    Adam, K.M., Erbach, D.C.: Secondary tillage tool effect on soil aggregation. Trans. ASAE 35(6), 1771–1776 (1992)CrossRefGoogle Scholar
  2. 2.
    Kirchmeier, H., Geischeder, R., Demmel, M.: Tillage effect and requirements of rotaty harrows with different rotor geometries. Landtechnik 60(4), 196–197 (2005)Google Scholar
  3. 3.
    Currence, H., Lovely, W.: The analysis of soil surface roughness. Trans. ASAE 13, 710–714 (1970)CrossRefGoogle Scholar
  4. 4.
    Marinello, F., Pezzuolo, A., Gasparini, F., Arvidsson, J., Sartori, L.: Application of the Kinect sensor for dynamic soil surface characterization. Precis. Agric. 16, 601–612 (2015)CrossRefGoogle Scholar
  5. 5.
    Taconet, O., Ciarletti, V.: Estimating soil roughness indices on a ridge-and-furrow surface using stereo photogrammetry. Soil Tillage Res. 93, 64–76 (2007)CrossRefGoogle Scholar
  6. 6.
    Riegler, T., Rechberger, C., Handler, F., Prankl, H.: Image processing system for evaluation of tillage quality. Landtechnik 69(3), 125–130 (2014)Google Scholar
  7. 7.
    de Obade, V.P.: Review article: remote sensing, surface residue cover and tillage practice. J. Environ. Prot. 3, 211–217 (2012)CrossRefGoogle Scholar
  8. 8.
    Pforte, F., Wilhelm, B., Hensel, O.: Evaluation of an online approach for determination of percentage residue cover. Biosyst. Eng. 112, 121–129 (2012)CrossRefGoogle Scholar
  9. 9.
    Campillo, C., Prieto, M.H., Daza, C., Monino, M.J., Garcia, M.I.: Using digital images to characterize canopy coverage and light interception in a processing tomato crop. HortScience 43, 1780–1786 (2008)CrossRefGoogle Scholar
  10. 10.
    Kırcı, M., Güneş, E. O., Çakır, Y.: Vegetation measurement using image processing methods. In The Third International Conference on Agro-Geoinformatics (2014)Google Scholar
  11. 11.
    Guerrero, J.M., Pajares, G., Montalvo, M., Romeo, J., Guijarro, M.: Support Vector Machines for crop/weeds identification in maize fields. Expert Syst. Appl. 39(12), 11149–11155 (2012)CrossRefGoogle Scholar
  12. 12.
    Mortensen, A. K., Dyrmann, M., Karstoft, H., Nyholm Jørgensen, R., Gislum, R.: Semantic Segmentation of Mixed Crops using Deep Convolutional Neural Network. In: CIGR-AgEng Conference (2016)Google Scholar
  13. 13.
    Riegler-Nurscher, P., Prankl, J., Bauer, T., Strauss, P., Prankl, H.: A machine learning approach for pixel wise classification of residue and vegetation cover under field conditions. Biosyst. Eng. 169, 188–198 (2018)CrossRefGoogle Scholar
  14. 14.
    Paszke, A., Chaurasia, A., Kim, S., Culurciello, E.: Enet: a deep neural network architecture for real-time semantic segmentation. arXiv preprint arXiv:1606.02147 (2016)
  15. 15.
    Mehta, S., Rastegari, M., Caspi, A., Shapiro, L., Hajishirzi, H.: ESPNet: efficient spatial pyramid of dilated convolutions for semantic segmentation. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 552–568 (2018)CrossRefGoogle Scholar
  16. 16.
    Romera, E., Alvarez, J.M., Bergasa, L.M., Arroyo, R.: Erfnet: efficient residual factorized convnet for real-time semantic segmentation. IEEE Trans. Intell. Transp. Syst. (ITS) 19(1), 263–272 (2018)CrossRefGoogle Scholar
  17. 17.
    Milioto, A., C. Stachniss, C.: Bonnet: an open-source training and deployment framework for semantic segmentation in robotics using CNNs. In: Proceedings of the IEEE International Conference on Robotics & Automation (ICRA) (2019)Google Scholar
  18. 18.
    OpenCV library. Accessed 9 May 2019
  19. 19.
    PCL library. Accessed 9 May 2019
  20. 20.
    Chebrolu, N., Lottes, P., Schaefer, A., Winterhalter, W., Burgard, W., Stachniss, C.: Agricultural robot dataset for plant classification, localization and mapping on sugar beet fields. Int. J. Robot. Res. 36(10), 1045–1052 (2017)CrossRefGoogle Scholar
  21. 21.
    Sandri, R., Anken, T., Hilfiker, T., Sartori, L., Bollhalder, H.: Comparison of methods for determining cloddiness in seedbed preparation. Soil Tillage Res. 45, 75–90 (1998)CrossRefGoogle Scholar
  22. 22.
    Skovsen, S., et al.: Estimation of the botanical composition of clover-grass leys from RGB images using data simulation and fully convolutional neural networks. Sensors 17(12), 2930 (2017)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Peter Riegler-Nurscher
    • 1
    Email author
  • Johann Prankl
    • 1
  • Markus Vincze
    • 2
  1. 1.Josephinum ResearchWieselburgAustria
  2. 2.Automation and Control InstituteVienna University of TechnologyViennaAustria

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