International Journal of Contents

The Korea Contents Association (한국콘텐츠학회)
권호 표지
DOI QR코드

DOI QR Code

Automatic Counting of Rice Plant Numbers After Transplanting Using Low Altitude UAV Images

  • Reza, Md Nasim (Department of Rural and Biosystems Engineering Chonnam National University) ;
  • Na, In Seop (School of Electronics and Computer Engineering Chonnam National University) ;
  • Lee, Kyeong-Hwan (Department of Rural and Biosystems Engineering Agricultural Robotics and Automation Research Center Chonnam National University)
  • Received : 2017.03.14
  • Accepted : 2017.08.07
  • Published : 2017.09.28
Download PDF
⟨ Previous Next ⟩

Abstract

Rice plant numbers and density are key factors for yield and quality of rice grains. Precise and properly estimated rice plant numbers and density can assure high yield from rice fields. The main objective of this study was to automatically detect and count rice plants using images of usual field condition from an unmanned aerial vehicle (UAV). We proposed an automatic image processing method based on morphological operation and boundaries of the connected component to count rice plant numbers after transplanting. We converted RGB images to binary images and applied adaptive median filter to remove distortion and noises. Then we applied a morphological operation to the binary image and draw boundaries to the connected component to count rice plants using those images. The result reveals the algorithm can conduct a performance of 89% by the F-measure, corresponding to a Precision of 87% and a Recall of 91%. The best fit image gives a performance of 93% by the F-measure, corresponding to a Precision of 91% and a Recall of 96%. Comparison between the numbers of rice plants detected and counted by the naked eye and the numbers of rice plants found by the proposed method provided viable and acceptable results. The $R^2$ value was approximately 0.893.

Keywords

References

  1. S. K. Bhowmik, M. A. R. Sarkar, and F. Zaman, "Effect of Spacing and Number of Seedlings per Hill on the Performance of AUS Rice cv. NERICA 1 Under Dry Direct Seeded Rice (DDSR) System of Cultivation," J. Bangladesh Agril. Univ., vol. 10, no. 2, 2012, pp. 191-195.
  2. B. B. Adhikari, B. Mehera, and S. Haefele, "Impact of Rice Nursery Nutrient Management, Seeding Density and Seedling Age on Yield and Yield Attributes," American Journal of Plant Sciences, vol. 4, no. 12, 2013, pp. 146-155. https://doi.org/10.4236/ajps.2013.412A3017
  3. K. J. Ehsanullah, G. Asghar, M. Hussain, and M. Rafiq, "Effect of Nitrogen Fertilization and Seedling Density on Fine Rice Yield in Faisalabad, Pakistan," Soil Environ., vol. 31, no. 2, 2012, pp. 152-156.
  4. X. Q. Lin, D. F. Zhu, H. Z. Chen, S. H. Cheng, and N. Uphoff, "Effect of Plant Density and Nitrogen Fertilizer Rates on Grain Yield and Nitrogen Uptake of Hybrid Rice (Oryza sativa L.)," Journal of Agricultural Biotechnology and Sustainable Development, vol. 1, no. 2, 2009, pp. 44-55.
  5. M. M. A. Mondal, A. B. Puteh, M. R. Ismail, and M. Y. Rafii, "Optimizing Plant Spacing for Modern Rice Varieties," Int. J. Agric. Biol., vol. 15, no. 1, 2013, pp. 175-178.
  6. R. Habib and M. Bhat, "Agronomic Evaluation of Rice (Oryza sativa L.) for Plant Spacings and Seedlings per Hill Under Temperate Conditions," African Journal of Agricultural Research, vol. 8, no. 37, 2013, pp. 4650-4653. https://doi.org/10.5897/AJAR10.411
  7. B. S. Chauhan, V. P. Singh, A. Kumar, and D. E. Johnson, "Relations of Rice Seeding Rates to Crop and Weed Growth in Aerobic Rice," Field Crops Research, vol. 121, no. 1, 2011, pp. 105-115. https://doi.org/10.1016/j.fcr.2010.11.019
  8. T. Liu, W. Wu, W. Chen, C. M. Sun, X. Zhu, and W. Guo, "Automated Image-processing for Counting Seedlings in a Wheat Field," Precision Agriculture, vol. 17, no. 4, 2015, pp. 392-406.
  9. T. Sakamoto, A. A. Gitelson, A. L. Nguy-Robertson, T. J. Arkebauer, B. D. Wardlow, A. E. Suyker, S. B. Verma, and M. Shibayama, "An Alternative Method Using Digital Cameras for Continuous Monitoring of Crop Status," Agricultural and Forest Meteorology, vol. 154, 2012, pp. 113-126.
  10. J. Primicerio, S. F. Di Gennaro, F. Fiorillo, L. Genesio, E. Lugato, A. Matese, and F. P. Vaccari, "A Flexible Unmanned Aerial Vehicle for Precision Agriculture," Precision Agriculture, vol. 13, no. 4, 2012, pp. 517-523. https://doi.org/10.1007/s11119-012-9257-6
  11. A. Rango, A. Laliberte, J. E. Herrick, C. Winters, K. Havstad, C. Steele, and D. Browning, "Unmanned Aerial Vehicle-based Remote Sensing for Rangeland Assessment, Monitoring, and Management," Journal of Applied Remote Sensing, vol. 3, no. 1, 2009, 033542, doi:10.1117/1.3216822.
  12. R. P. Breckenridge, M. Dakins, S. Bunting, J. L. Harbour, and S. White, "Comparison of Unmanned Aerial Vehicle Platforms for Assessing Vegetation Cover in Sagebrush Steppe Ecosystems," Rangeland Ecology & Management, vol. 64, no. 5, 2011, pp. 521-532. https://doi.org/10.2111/REM-D-10-00030.1
  13. S. Harwin and A. Lucieer, "Assessing the Accuracy of Georeferenced Point Clouds Produced via Multi-view Stereopsis from Unmanned Aerial Vehicle (UAV) Imagery," Remote Sensing, vol. 4, no. 6, 2012, pp. 1573-1599. https://doi.org/10.3390/rs4061573
  14. D. Gomez-Candon, A. I. De Castro, and F. Lopez-Granados, "Assessing the Accuracy of Mosaics from Unmanned Aerial Vehicle (UAV) Imagery for Precision Agriculture Purposes in Wheat," Precision Agriculture, vol. 15, no. 1, 2014, pp. 44-56. https://doi.org/10.1007/s11119-013-9335-4
  15. V. Lebourgeois, A. Begue, S. Labbe, M. Houles, and J. F. Martine, "A Light-weight Multi-spectral Aerial Imaging System for Nitrogen Crop Monitoring," Precision Agriculture, vol. 13, no. 5, 2012, pp. 525-541. https://doi.org/10.1007/s11119-012-9262-9
  16. J. Torres-Sanchez, J. M. Pena, A. I. de Castro, and F. Lopez-Granados, "Multi-temporal Mapping of the Vegetation Fraction in Early-season Wheat Fields Using Images from UAV," Computers and Electronics in Agriculture, vol. 103, 2014, pp. 104-113. https://doi.org/10.1016/j.compag.2014.02.009
  17. J. M. Pena, J. Torres-Sanchez, A. I. de Castro, M. Kelly, and F. Lopez-Granados, "Weed Mapping in Early-season Maize Fields Using Object-based Analysis of Unmanned Aerial Vehicle (UAV) Images," PLoS ONE, vol. 8, no. 10, 2013, e77151, doi:10.1371/journal.pone.0077151.
  18. M. L. Guillen-Climent, P. J. Zarco-Tejada, J. A. J. Berni, P. R. J. North, and F. J. Villalobos, "Mapping Radiation Interception in Row-structured Orchards Using 3D Simulation and High-resolution Airborne Imagery Acquired from a UAV," Precision Agriculture, vol. 13, no. 4, 2012, pp. 473-500. https://doi.org/10.1007/s11119-012-9263-8
  19. M. A. Mansur, R. B. Mukhtar, and J. Al-Doksi, "The Usefulness of Unmanned Airborne Vehicle (UAV) Imagery for Automated Palm Oil Tree Counting," Researchjournali's Journal of Forestry, vol. 1, no. 1, 2014, pp. 1-12.
  20. F. Santoro, E. Tarantino, B. Figorito, S. Gualano, and A. M. D'Onghia, "A Tree Counting Algorithm for Precision Agriculture Tasks," International Journal of Digital Earth, vol. 6, no. 1, 2013, pp. 94-102. https://doi.org/10.1080/17538947.2011.642902
  21. D. G. Kumar and M. Padmaja, "A Novel Image Processing Technique for Counting the Number of Trees in a Satellite Image," European Journal of Applied Engineering and Scientific Research, vol. 1, no. 4, 2012, pp. 151-159.
  22. M. Kalapala, "Estimation of Tree Count from Satellite Imagery through Mathematical Morphology," International Journal of Advanced Research in Computer Science and Software Engineering, vol. 4, no. 1, 2014, pp. 490-495.
  23. R. S. Sarkate, N. V. Kalyankar, and P. B. Khanale, "Application of Computer Vision and Color Image Segmentation for Yield Prediction Precision," International Conference on Information Systems and Computer Networks (ISCON), 9-10 March, 2013, pp. 9-13.
  24. J. M. Pena-Barragana, M. Kelly, A. I. de-Castroa, and F. Lopez-Granadosa, "Object-based Approach for Crop Row Characterization in UAV Images for Site-specific Weed Management," Proceedings of the 4th GEOBIA, Rio de Janeiro, Brazil, May 7-9, 2012, pp. 426-430.
  25. J. C. Neto and J. I. Miranda, "A Genetic Algorithm for Citrus Tree Counting and Canopy Diameter Estimation," Anais XIV Simposio Brasileiro de Sensoriamento Remoto, Natal, Brasil, INPE, 25-30 April, 2009, pp. 6797-6804.
  26. G. Asmitaba and D. Pipalia, "Design an Algorithm to Detect and Count Small Size Object Using Digital Image Processing," International Journal of Advanced Research in Electrical, Electronics and Instrumentation Engineering, vol. 5, no. 5, 2016, pp. 3807-3812.
  27. D. D. Bhosale, "Use of Digital Image Processing for Grain Counting," International Journal of Advance Research in Computer Science and Management Studies, vol. 3, no. 3, 2015, pp. 6-9.
  28. C. Kanan and G. W. Cottrell, "Color-to-grayscale: Does the Method Matter in Image Recognition?," PLoS ONE, vol. 7, no. 1, 2012, e29740, doi: 10.1371/journal.pone.0029740.
  29. S. Shrestha, "Image Denoising Using New Adaptive Based Median Filters," Signal & Image Processing: An International Journal (SIPIJ), vol. 5, no. 4, 2014, pp. 1-13. https://doi.org/10.5121/sipij.2014.5401
  30. N. Bairwa and N. K. Agarwal, "Counting of Flowers Using Image Processing," International Journal of Engineering Research & Technology (IJERT), vol. 3, no. 9, 2014, pp. 775-779.
  31. H. Tulsani, S. Saxena, and N. Yadav, "Segmentation Using Morphological Watershed Transformation for Counting Blood Cells," International Journal of Computer Applications & Information Technology, vol. 2, no. 3, 2013, pp. 28-36.
  32. T. Acharya and A. K. Ray, Image processing: Principles and Applications, A John Wiley & Sons Inc., Publication, Hoboken, New Jersey, USA, 2005, ISBN-13978-0-471-71998-4.
  33. T. Saito and M. Rehmsmeier, "The Precision-recall Plot is More Informative than the ROC Plot When Evaluating Binary Classifiers on Imbalanced Datasets," PLoS ONE, vol. 10, no. 3, 2015, e0118432, doi:10.1371/journal.pone.0118432.
  34. J. Gul-Mohammed, I. Arganda-Carreras, P. Andrey, V. Galy, and T. Boudier, "A Genetic Classification-based Method for Segmentation of Nuclei in 3D Images of Early Embryos," BMC Bioinformatics, vol. 15, no. 9, 2014, pp. 1-12.