SpringerLink
Log in

In-field Experiments for Performance Evaluation of a New Low-Cost Active Multispectral Crop Sensor

  • Chapter
  • First Online:
Information and Communication Technologies for Agriculture—Theme I: Sensors

Part of the book series: Springer Optimization and Its Applications ((SOIA,volume 182))

Abstract

Recent developments in agricultural technologies have made available for use by the farmers a variety of sensors and sensing services. Remote sensing has become particularly popular especially after the release of free satellite images form several vendors across the globe. In addition, the use of unmanned aerial systems (UAS) equipped with diverse optical sensors is getting very popular for field scouting and map** applications in agriculture since the unmanned aerial vehicles (UAV) have become cost-affordable to almost any farmer. To many farmers, the UAVs equipped with optical sensing systems seem like hi-tech toys which can offer detailed insight of in-field hotspots. However, most satellite and UAV derived observations are based on passive sensing systems and require high level data pre-processing before used in the field. Therefore, the data processing requirements work as a constraint for most farmers, while the limitations of the passive sensing systems that are affected by the weather and atmospheric conditions, make them unpractical when on-the-go farming applications, such as variable rate spraying or fertilizing, are needed. During the past decades, active proximal sensing has been increasingly used to provide information about canopy properties and take real-time decisions in a large range of crops. Numerous proximal sensing instruments have been developed and are commercially available. However, there are several limitations in the use of most of these devices, such as high complexity in the operation and data processing, high cost, poor accuracy, etc., that work as barriers in the adoption of these devices by small and medium size farms. Therefore, there is still room for new advancements in the development of new more cost effective and farmer friendly proximal sensing solutions. In this study a new low cost, active multispectral optical device named Plant-O-Meter was tested in real conditions comparing it with the well-proven GreenSeeker handheld device. The latter sensor is a widely used commercial canopy sensor well-accepted both by the farmers and the scientific community. It was selected as a reference sensor in the study as it works using the same operating principles, is relatively low cost and has similar measuring characteristics to the Plant-O-Meter. The study took place at two experimental fields cultivated with maize (Zea mays L.) using a randomized complete block design with three replications. Nitrogen (N) fertilization rate experiments were set in order to create variations in canopy development, vigor and greenness across the fields, providing the ability to compare sensors’ detectability and other performance characteristics in simulated field conditions. Thus, a wide range of sensor readings, from very low to very high, was expected. Treatments included five nitrogen (N) fertilization rates (0, 50, 100, 150 and 200 kg of N ha−1) applied during sowing. Three maize hybrids were scanned for Normalized Difference Vegetation Index (NDVI) using both Plant-O-Meter and GreenSeeker sensors at V4, V6 and V8 growth stages. During full maturity, the central part of each plot was hand-harvested for grain (two middle rows 6 m long). Based on the present findings, the optimum timing for scanning using GreenSeeker or Plant-O-Meter was between V7 and V8 stage. Measuring within this growth stage window good estimation of end-of-season yield was achieved. In addition, the overall results indicated that NDVI obtained using GreenSeeker were quite similar to the NDVI measured by the Plant-O-Meter showing an almost 1:1 relationship. These results indicate that Plant-O-Meter exhibits strong potential for accurate plant canopy measurements and for real time variable rate fertilization applications in maize.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution

Subscribe and save

Springer+ Basic
EUR 32.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or Ebook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 99.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 129.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free ship** worldwide - see info
Hardcover Book
USD 129.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free ship** worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  1. FAOstat (2020) ‘Production quantities of Maize. Average for the years 1961 - 2018’. http://www.fao.org/faostat/en/#data/QC/visualize. (Date accessed 18/02/2020).

  2. Hammer, G. L., Dong, Z., McLean, G., Doherty, A., Messina, C., Schussler, J., Zinselmeier, C., Paszkiewicz, S. and Cooper, M. (2009) ‘Can Changes in Canopy and/or Root System Architecture Explain Historical Maize Yield Trends in the U.S. Corn Belt?’, Crop Sci. 49, pp. 299–312. https://doi.org/10.2135/cropsci2008.03.0152.

    Article  Google Scholar 

  3. Shapiro, C. A. and Wortmann, C. S. (2006) ‘Corn response to nitrogen rate, row spacing and plant density in Eastern Nebraska’, Agron. J., 98, pp. 529–535.

    Article  Google Scholar 

  4. Ladha, K.J., Pathak, H., Krupnik, T.J., Six, J. and van Kessel, C. (2005) ‘Efficiency of Fertilizer nitrogen in cereal production: Retrospects and prospects’, Adv. Agron., 87, pp. 85–156.

    Article  Google Scholar 

  5. Tagarakis, A. C. and Ketterings, Q. M. (2018) ‘Proximal sensor-based algorithm for variable rate nitrogen application in maize in northeast U.S.A.’, Computers and Electronics in Agriculture, 145, pp. 373-378. https://doi.org/10.1016/j.compag.2017.12.031.

    Article  Google Scholar 

  6. Raun, W., and Johnson, G. (1999). ‘Improving nitrogen use efficiency for cereal production’, Agronomy Journal, 91, pp. 357–363.

    Google Scholar 

  7. López-Bellido, R. and López-Bellido, L. (2001) ‘Efficiency of nitrogen in wheat under Mediterranean conditions: Effect of tillage, crop rotation and N fertilization’, Field Crop. Res., 71, pp. 31–46.

    Google Scholar 

  8. Setiyono, T. D., Yang, H., Walters, D. T., Dobermann, A., Ferguson, R. B., Roberts, D. F., Lyon, D. J., Clay, D. E. and Cassaman, K. G. (2011) ‘Maize-N: A decision tool for nitrogen management in maize’, Agron. J.,103, pp. 1276–1283.

    Article  Google Scholar 

  9. Gemtos, T., Fountas, S., Tagarakis, A. and Liakos, V. (2013) ‘Precision agriculture application in fruit crops:experience in handpicked fruits’, Procedia Technology. 8, pp. 324–332.

    Google Scholar 

  10. International Society of Precision Agriculture – ISPA (2018) ‘Official definition of Precision Agriculture’, https://www.ispag.org/about/definition (accessed 28 January 2020).

  11. Robert, P., Rust, R. and Larson, W. (1994) ‘Site-specific Management for Agricultural Systems’, Proceedings of the 2nd International Conference on Precision Agriculture, 1994, Madison, WI.

    Google Scholar 

  12. Khosla, R. (2008) ‘The 9th International Conference on Precision Agriculture opening ceremony presentation’, July 20-23rd, 2008. ASA/CSSA/SSSA.

    Google Scholar 

  13. Mulla, D. J. (2013) ‘Twenty five years of remote sensing in precision agriculture: Key advances and remaining knowledge gaps’, Biosystems Engineering, Special Issue: Sensing in Agriculture, pp. 358–371. https://doi.org/10.1016/j.biosystemseng.2012.08.009.

    Book  Google Scholar 

  14. Yang, C., Everitt, J. H., Du, Q., Luo, B. and Chanussot, J. (2013) ‘Using High-Resolution Airborne and Satellite Imagery to Assess Crop Growth and Yield Variability’. Proceedings of the IEEE, 101 (3), pp. 582-592. https://doi.org/10.1109/JPROC.2012.2196249.

    Article  Google Scholar 

  15. Raun, W. R., Solie, J. B., Johnson, G. V., Stone, M. L., Mullen, R. W., Freeman, K. W., Thomason, W. E, and Lukina, E. V. (2002) ‘Improving nitrogen use efficiency in cereal grain production with optical sensing and variable rate application’, Agron. J. 94:815–820. https://doi.org/10.2134/agronj2002.8150.

  16. Tagarakis, A. C., Ketterings, Q. M., Lyons, S. and Godwin, G. (2017) ‘Proximal sensing to estimate yield of brown midrib forage sorghum’, Agronomy Journal, 109(1), pp. 107–114. https://doi.org/10.2134/agronj2016.07.0414.

    Article  Google Scholar 

  17. Auernhammer, H. (2001) ‘Precision farming — the environmental challenge’, Computers and Electronics in Agriculture, 30 (1–3), pp. 31–43.

    Google Scholar 

  18. Tagarakis, A., Liakos, V., Fountas, S., Koundouras, S. and Gemtos, T. A. (2013) Management zones delineation using fuzzy clustering techniques in grapevines. Precision Agriculture, 14, pp. 18–39.

    Google Scholar 

  19. Oberti, R., Marchi, M., Tirelli, P., Calcante, A., Iriti, M. and Borghese, A. N. (2014) ‘Automatic detection of powdery mildew on grapevine leaves by image analysis: Optimal view angle range to increase the sensitivity’, Computers and Electronics in Agriculture, 104, pp. 1-8.

    Article  Google Scholar 

  20. Whetton, R., Waine, T., Mouazen, A. (2017) ‘Optimising configuration of a hyperspectral imager for on-line field measurement of wheat canopy’, Biosystems Engineering, 155, pp. 84-95.

    Article  Google Scholar 

  21. Fitzgerald, G. J. (2010) ‘Characterizing vegetation indices derived from active and passive sensors‘, International Journal of Remote Sensing, 31:16, pp. 4335-4348. https://doi.org/10.1080/01431160903258217.

    Article  Google Scholar 

  22. Oerke, E.C., Mahlein, A.K. and Steiner, U. (2014) ‘Proximal sensing of plant diseases’ In: Gullino, M.L., Bonants, P.J.M. (eds) ‘Detection and diagnostics of plant pathogens’, Springer, Dordrecht, p.p. 55–68. https://doi.org/10.1007/978-94-017-9020-8_4.

  23. Aschbacher, J. and Milagro-Pérez, M. P. (2012) ‘The European Earth monitoring (GMES) programme: Status and perspectives’, Remote Sensing of Environment, 120, pp. 3-8. https://doi.org/10.1016/j.rse.2011.08.028.

    Article  Google Scholar 

  24. Woodcock, C. E., Allen, R., Anderson, M., Belward, A., Bindschadler, R., Cohen, W., Gao, F., Goward, S. N., Helder, D., Helmer, E., Nemani, R., Oreopoulos, L., Schott, J., Thenkabail, P. S., Vermote, E. F., Vogelmann, J., Wulder, M. A. and Wynne, R. (2008) ‘Free access to Landsat imagery’, Science, 320, pp. 1011. https://doi.org/10.1126/science.320.5879.1011a.

    Article  Google Scholar 

  25. Jackson, R. D. (1986) ‘Remote Sensing of Biotic and Abiotic Plant Stress’, Annual review of Phytopathology, 24, pp. 265–287. https://doi.org/10.1146/annurev.py.24.090186.001405.

    Article  Google Scholar 

  26. Shanahan, J. F., Kitchen, N. R., Raun, W. R. and Schepers, J. S. (2008) ‘Responsive in-season nitrogen management for cereals’, Computers and Electronics in Agriculture, 61, pp. 51-62. https://doi.org/10.1016/j.compag.2007.06.006.

    Article  Google Scholar 

  27. Solari, F., Shanahan, J., Ferguson, R. B., Schepers, J. S. and Gitelson, A. A. (2008) ‘Active sensor reflectance measurements to corn nitrogen status and yield potential’, Agronomy Journal, 100, pp. 571–579. https://doi.org/10.2134/agronj2007.0244.

    Article  Google Scholar 

  28. Girma, K., Holtz, S. L., Arnall, D. B., Fultz, L. M., Hanks, T. L., Lawles, K. D., Mack, C. J., Owen, K. W., Reed, S. D., Santillano, J., Walsh, O., White, M. J. and Raun, W. R. (2007). ‘Weather, fertilizer, previous year grain yield and fertilizer response level affect ensuing year grain yield and fertilizer response of winter wheat’, Agronomy Journal, 99, pp. 1607–1614.

    Article  Google Scholar 

  29. Kostić, M., Rakić, D., Savin, L., Dedović, N. and Simikić, M. (2016) ‘Application of an original soil tillage resistance sensor in spatial prediction of selected soil properties’, Computers and Electronics in Agriculture, 127, pp. 615–624. https://doi.org/10.1016/j.compag.2016.07.027.

    Article  Google Scholar 

  30. Magney, S. T., Eitel, J. U. H., Huggins, D. R. and Vierling, L. A. (2016) ‘Proximal NDVI derived phenology improves in-season predictions of wheat quantity and quality’, Agricultural and Forest Meteorology, 217, pp. 46–60. https://doi.org/10.1016/j.agrformet.2015.11.009.

    Article  Google Scholar 

  31. Zecha, C. W., Peteinatos, G. G., Link, J. and Claupein, W. (2018) ‘Utilisation of ground and airborne optical sensors for nitrogen level identification and yield prediction in wheat’, Agriculture, 8(6) pp. 79. https://doi.org/10.3390/agriculture8060079.

  32. Bean, G. M., Kitchen, N. R., Camberato, J. J., Ferguson, R. B., Fernandez, F. G., Franzen, D. W., Laboski, C. A. M., Nafziger, E. D., Sawyer, J. E., Scharf, P. C., Schepers, J. and Shanahan, J. S. (2018) ‘Active-optical reflectance sensing corn algorithms evaluated over the United States Midwest corn belt’, Agronomy Journal, 110, pp. 2552–2565.

    Article  Google Scholar 

  33. Tagarakis, A. C. and Ketterings, Q. M. (2017) ‘In-season estimation of corn yield potential using proximal sensing’, Agronomy Journal, 109(4), pp. 1323–1330. https://doi.org/10.2134/agronj2016.12.0732.

    Article  Google Scholar 

  34. Rouse, J. W., Haas, R. H., Schell, J. A. and Deering, D. W. (1973) ‘Monitoring vegetation systems in the Great Plains with ERTS’, NASA. Goddard Space Flight Center 3d ERTS-1 Symp., 1, pp. 309–317.

    Google Scholar 

  35. Hatfield, J. L., Gitelson, A. A., Schepers, J. S. and Walthall, C. L.(2008) ‘Application of spectral remote sensing for agronomic decisions’, Agronomy Journal, 100, pp. 117–131. https://doi.org/10.2134/agronj2006.0370c.

    Article  Google Scholar 

  36. Wang, R., Cherkauer, K. A. and Bowling, L. C. (2016) ‘Corn Response to Climate Stress Detected with Satellite-Based NDVI Time Series’, Remote Sensing, 8(4), pp. 269. https://doi.org/10.3390/rs8040269.

  37. Moges, S. M., Girma, K., Teal, R. K., Freeman, K. W., Zhang, H. and Arnall, D. B. (2007) ‘In-season estimation of grain sorghum yield potential using a hand-held optical sensor’, Arch. of Agron. and Soil Sci., 53(6), pp. 617–628. https://doi.org/10.1080/03650340701597251.

    Article  Google Scholar 

  38. Raun, W. R., Solie, J. B., Martin, K. L., Freeman, K. W., Stone, M. L., Johnson, G. V. and Mullen, R. W. (2005) ‘Growth stage, development, and spatial variability in corn evaluated using optical sensor readings’, J. Plant Nutr., 28, pp. 173–182. https://doi.org/10.1081/PLN-200042277.

  39. Raun,W. R., Johnson, G.V., Stone, M.L., Solie, J.B., Lukina, E.V., Thomason, W.E., and Schepers, J.S. (2001) ‘In-season prediction of potential grain yield in winter wheat using canopy reflectance’, Agronomy Journal, 93, pp. 131–138.

    Google Scholar 

  40. Teal, R. K., Tubana, B., Girma, K., Freeman, K. W., Arnall, D. B., Walsh, O. and Raun, W. R. (2006) In-season prediction of corn grain yield potential using normalized difference vegetation index’, Agron. J., 98, pp. 1488–1494. https://doi.org/10.2134/agronj2006.0103.

    Article  Google Scholar 

  41. Lukina, E. V., Freeman, K. W., Wynn, K. J., Thomason, W. E., Mullen, R. W., Stone, M. L., Solie, J. B., Klatt, A. R., Johnson, G. V., Elliott, R. L. and Raun, W. R. (2001) ‘Nitrogen fertilization optimization algorithm based on in-season estimates of yield and plant nitrogen uptake’, Journal of Plant Nutrition, 24(6), pp. 885-898. https://doi.org/10.1081/PLN-100103780.

    Article  Google Scholar 

  42. Ritchie, S. W., Hanway, J. J. and Benson, G. O. (1997) ‘How a Corn Plant Develops’, Special Report No. 48, Iowa State University Cooperative Extension Service: Ames, IA, USA, 1997.

    Google Scholar 

  43. Rogers, N. G. (2016) ‘Sensor Based Nitrogen Management for Corn Production in Coastal Plain Soils’, All Theses. 2579.

    Google Scholar 

  44. Adamsen, F.J., Pinter Jr., P.J., Barnes, E.M., LaMorte, R.L., Wall, G.W., Leavitt, S.W. and Kimball, B.A. (1999) ‘Measuring wheat senescence with a digital camera’, Crop Science, 39, pp. 719-724. https://doi.org/10.2135/cropsci1999.0011183X003900030019x.

    Article  Google Scholar 

  45. Helman, D., Bonfil, D. J. and Lensky, I. M. (2019) ‘Crop RS-Met: A biophysical evapotranspiration and root-zone soil water content model for crops based on proximal sensing and meteorological data’, Agricultural Water Management, 211, pp. 210–219. https://doi.org/10.1016/j.agwat.2018.09.043.

    Article  Google Scholar 

  46. Stone, M. L., Solie, J. B., Raun, W. R., Whitney, R. W., Taylor, S. L. and Ringer, J. D. (1996) ‘Use of spectral radiance for correcting in-season fertilizer nitrogen deficiencies in winter wheat’, Trans. ASAE 39, pp. 1623–1631. https://doi.org/10.13031/2013.27678.

  47. Kim, Y., Huete, A., Miura, T. and Jiang, Z. (2010) ‘Spectral compatibility of vegetation indices across sensors: band decomposition analysis with Hyperion data’, Journal of Applied Remote Sensing 4(1) 043520. https://doi.org/10.1117/1.3400635.

    Article  Google Scholar 

  48. Yao, X., Yao, X., Jia, W., Tian, Y., Ni, J., Cao, W. and Zhu, Y. (2013) ‘Comparison and intercalibration of vegetation indices from different sensors for monitoring above-ground plant nitrogen uptake in winter wheat’, Sensors, 13(3), pp. 3109-3130. https://doi.org/10.3390/s130303109.

    Article  Google Scholar 

  49. Belic, M., Manojlivic, M., Nesic, L., Ciric, V., Vasin, J., Benka, P. and Seremesic S. (2013) ‘Pedo-Ecological significance of Soil Organic Carbon stock in South-Eastern Pannonian basin’, Carpathian Journal of Earth and Environmental Sciences, 8 (1), pp. 171 – 178.

    Google Scholar 

  50. Altermann, M., Rinklebe, J., Merbach, I., Körschens, M., Langer, U. and Hofmann, B. (2005) ‘Chernozem—Soil of the Year 2005’, J. Plant Nutr. Soil Sci. 2005, 168, pp. 725–740. https://doi.org/10.1002/jpln.200521814.

    Article  Google Scholar 

  51. Tremblay N., Wang Z., Ma, B. L., Belec, C. and Vigneault, P. (2009) ‘A comparison of crop data measured by two commercial sensors for variable-rate nitrogen application’, Precision Agriculture, 10, pp. 145-161. https://doi.org/10.1007/s11119-008-9080-2.

    Article  Google Scholar 

  52. Kitić, G., Tagarakis, A., Cselyuszka, N., Panić, M., Birgermajer, S., Sakulskia, D. and Matović, J. (2019) ‘A new low-cost portable multispectral optical device for precise plant status assessment’, Computers and Electronics in Agriculture, 162, pp. 300–308.

    Google Scholar 

  53. Johnson, G. V. and Raun, W. R. (2003) ‘Nitrogen response index as a guide to fertilizer management’, Journal of Plant Nutrition, 26, pp. 249–262.

    Article  Google Scholar 

Download references

Acknowledgments

This study was supported by the project “Development of the device for measurement and map** of nitrogen as the most important parameter in sustainable agriculture”, contract no. 114-451-2794/2016-03, funded by Provincial Secretariat for Higher Education and Scientific Research of Autonomous Province of Vojvodina, Republic of Serbia and by the project “Improvement of the quality of tractors and mobile systems with the aim of increasing competitiveness and preserving soil and environment”, contract no. TR-31046, funded by the Ministry of Education, Science and Technological Development of the Republic of Serbia.

Author information

Authors and Affiliations

  1. BioSense Institute, University of Novi Sad, Novi Sad, Serbia

    Aristotelis C. Tagarakis, Natasa Ljubičić, Bojana Ivošević, Goran Kitić & Miloš Pandžić

  2. Institute for Bio-Economy and Agri-Technology (iBO), Centre for Research and Technology Hellas (CERTH), Thessaloniki, Greece

    Aristotelis C. Tagarakis

  3. Faculty of Agriculture, University of Novi Sad, Novi Sad, Serbia

    Marko Kostić

Authors
  1. Aristotelis C. Tagarakis
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Marko Kostić
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Natasa Ljubičić
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Bojana Ivošević
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Goran Kitić
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Miloš Pandžić
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Aristotelis C. Tagarakis .

Editor information

Editors and Affiliations

  1. Institute for Bio-economy and Agri-technology (iBO), Maria Laura Delle Monache, Department of Civil and Environmental Engineering, University of California, Berkeley, Centre for Research and Technology Hellas (CERTH), Thessaloniki, Greece

    Dionysis D. Bochtis

  2. Institute for Bio-economy and Agri-technology (iBO), Maria Laura Delle Monache, Department of Civil and Environmental Engineering, University of California, Berkeley, Centre for Research and Technology Hellas (CERTH), Thessaloniki, Greece

    Maria Lampridi

  3. Department of Geography, Harokopio University of Athens, Athens, Greece

    George P. Petropoulos

  4. Department of Agricultural and Biological Engineering, University of Florida, Immokalee, FL, USA

    Yiannis Ampatzidis

  5. Department of Industrial and Systems Engineering, University of Florida, Gainesville, FL, USA

    Panos Pardalos

Rights and permissions

Reprints and permissions

Copyright information

© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Tagarakis, A.C., Kostić, M., Ljubičić, N., Ivošević, B., Kitić, G., Pandžić, M. (2022). In-field Experiments for Performance Evaluation of a New Low-Cost Active Multispectral Crop Sensor. In: Bochtis, D.D., Lampridi, M., Petropoulos, G.P., Ampatzidis, Y., Pardalos, P. (eds) Information and Communication Technologies for Agriculture—Theme I: Sensors. Springer Optimization and Its Applications, vol 182. Springer, Cham. https://doi.org/10.1007/978-3-030-84144-7_13

Download citation

Publish with us

Policies and ethics

Search

Navigation