https://doi.org/10.4081/jae.2024.1559
An active-optical reflectance sensor in-field testing for the prediction of winter wheat harvest metrics
Published: 16 February 2024
Abstract Views: 491
PDF: 164
SUPPLEMENTARY MATERIAL: 42
HTML: 2
SUPPLEMENTARY MATERIAL: 42
HTML: 2
Publisher's note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.
Authors
The ambition of this study was to justify the possibility of wheat trait prediction using a normalized difference vegetation index (NDVI) from a newly developed Plant-O-Meter sensor. Acquired data from Plant-O-Meter was matched with GreenSeeker’s, which was designated as a reference. The experiment was carried out in the field during the 2022 growing season at the long-term experimental field. The experimental design included five different winter wheat genotypes and 20 different NPK fertilizer treatments. The GreenSeeker sensor always gave out NDVI values that were higher than those of the Plant-O-Meter by, on average, 0.029 (6.36%). The Plant-O-Meter sensor recorded similar NDVI values (94% of the variation is explained, P<0.01). The Plant-O-Meter’s NDVIs had a higher CV for different wheat varieties and different sensing dates. For almost all varieties, GreenSeeker exceeded Plant-O-Meter in predicting yields for the early (March 21st) and late (June 6th) growing seasons. NDVIGreenSeeker data improved yield modeling performance by an average of 5.1% when compared to NDVIPlant-O-Meter; in terms of plant height prediction, NDVIGreenSeeker was 3% more accurate than NDVIPlant-O-Meter and no changes in spike length prediction were found. A compact, economical and user-friendly solution, the Plant-O-Meter, is straightforward to use in wheat breeding programs as well as mercantile wheat production.
Alvar-Beltrán J., Fabbri C., Verdi L., Truschi S., Dalla Marta A., Orlandini S. 2020. Testing Proximal Optical Sensors on Quinoa Growth and Development. Remote Sens. 12:1958.
Ang Y., Norasma N., Ya C., Roslin N., Ismail M., Che’Ya N. 2020. Rice Chlorophyll Content Monitoring using Vegetation Indices from Multispectral Aerial Imagery. Pertanika J. Sci. Technol. 28:779-95.
Bannari A., Morin D., Bonn F., Huete A.R. 1995. A review of vegetation indices. Remote Sens. Rev. 13:95-120.
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., Shanahan J.S. 2018. Active-Optical Reflectance Sensing Corn Algorithms Evaluated over the United States Midwest Corn Belt. Agron. J. 110:2552-65.
Cao Q., Miao Y., Li F., Gao X., Liu B., Lu D., Chen X. 2017. Developing a new Crop Circle active canopy sensor-based precision nitrogen management strategy for winter wheat in North China Plain. Precision Agric. 18:2-18.
Crain J., Ortiz-Monasterio I., Raun B. 2012. Evaluation of a Reduced Cost Active NDVI Sensor for Crop Nutrient Management. J. Sens. 2012:1-10.
Curran P.J., West S.G., Finch J.F. 1996. The Robustness of Test Statistics to Nonnormality and Specification Error in Confirmatory Factor Analysis. Psychol. Methods. 1:14.
Eitel J.U.H., Long D.S., Gessler P.E., Hunt E.R., Brown D.J. 2009. Sensitivity of ground-based remote sensing estimates of wheat chlorophyll content to variation in soil reflectance. Soil Sci. Soc. Am. J. 73:1715-23.
Fertilizers Price Index. n.d. Available from: https://ycharts.com/indicators/fertilizers_index_world_bank (accessed 10.31.22).
Huete A.R. 1987. Soil-dependent spectral response in a developing plant canopy1. Agron. J. 79:61-8.
Jaćimović G. 2012. Optimization of mineral nutrition of wheat, depending on weather conditions during the year (Ph.D. Thesis). University of Novi Sad, Serbia.
Jin X., Liu S., Baret F., Hemerlé M., Comar A. 2017. Estimates of plant density of wheat crops at emergence from very low altitude UAV imagery. Remote Sens. Environ. 198:105-14.
Jovanović M., Pavić D., Mesaroš M., Stankov U., Pantelić M., Armenski T., Dolinaj D., Popov S., Ćosić Đ., Popović L., Frank A., Crnojević V. 2013. Water shortage and drought monitoring in Bačka region (Vojvodina, North Serbia) – setting-up measurement stations network. Geogr. Pannonica. 17:114-24.
Kerry R., Oliver M.A., Frogbrook Z.L. 2010. Sampling in Precision Agriculture, in: Oliver M.A. (eds.), Geostatistical Applications for Precision Agriculture. Springer Netherlands, Dordrecht. pp. 35-63.
Kitić G., Tagarakis A., Cselyuszka N., Panić M., Birgermajer S., Sakulski D., Matović J. 2019. A new low-cost portable multispectral optical device for precise plant status assessment. Comput. Electron. Agric. 162:300-08.
Laurent A., Heaton E., Kyveryga P., Makowski D., Puntel L.A., Robertson A.E., Thompson L., Miguez F. 2022. A yield comparison between small-plot and on-farm foliar fungicide trials in soybean and maize. Agron. Sustain. Dev. 42:86.
Lawrence H.G., Yule I.J. 2007. Estimation of the in????field variation in fertiliser application. New Zeal. J. Agric. Res. 50:25-32.
Long-term Field Experiments in Europe. n.d. Available from: https://lte.bonares.de/lte-details/153/ (accessed 11.6.22).
Lu J., Miao Y., Shi W., Li J., Yuan F. 2017. Evaluating different approaches to non-destructive nitrogen status diagnosis of rice using portable RapidSCAN active canopy sensor. Sci. Rep. 7:14073.
Magney T.S., Eitel J.U.H., Huggins D.R., Vierling L.A. 2016. Proximal NDVI derived phenology improves in-season predictions of wheat quantity and quality. Agric. For. Meteorol. 2016:46-60.
Oglesby C., Fox A.A.A., Singh G., Dhillon J. 2022. Predicting inseason corn grain yield using optical sensors. Agronomy. 12:2402.
Panek E., Gozdowski D. 2020. Analysis of relationship between cereal yield and NDVI for selected regions of Central Europe based on MODIS satellite data. Remote Sens. App. Soc. Environ. 17:100286.
Parish R.L. 2002. Rate setting effects on fertilizer spreader distribution patterns. Appl. Eng. Agric. 18.
Raper T.B., Varco J.J., Hubbard K.J. 2013. Canopy-based normalized difference vegetation index sensors for monitoring cotton nitrogen status. Agron. J. 105:1345-54.
Raun W.R., Solie J.B., Johnson G.V., Stone M.L., Mullen R.W., Freeman K.W., Thomason W.E., Lukina E.V. 2002. Improving nitrogen use efficiency in cereal grain production with optical sensing and variable rate application. Agron. J. 94:815-20.
Raun W.R., Solie J.B., Martin K.L., Freeman K.W., Stone M.L., Johnson G.V., Mullen R.W. 2005. Growth stage, development, and spatial variability in corn evaluated using optical sensor readings. *Contribution from the Oklahoma Agricultural Experiment Station and the International Maize and Wheat Improvement Center (CIMMYT). J. Plant Nutr. 28:173-82.
Saleem M., 2022. Possibility of utilizing agriculture biomass as a renewable and sustainable future energy source. Heliyon. 8:e08905.
Sankaran S., Espinoza C.Z., Hinojosa L., Ma X., Murphy K. 2019. High-throughput field phenotyping to assess irrigation treatment effects in quinoa. Agrosyst. Geosci. Environ. 2:180063.
Sharma L., Bu H., Denton A., Franzen D., 2015. Active-optical sensors using red NDVI compared to red edge NDVI for Prediction of corn grain yield in North Dakota, U.S.A. Sensors 15:27832-53.
Statistical yearbook of Republic of Serbia. 2022. Statistical Office of the Republic of Serbia, Belgrade.
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. Springer Int. Pub. Cham. pp. 305-25.
Varinderpal-Singh, Kunal, Kaur R., Mehtab-Singh, Mohkam-Singh, Harpreet-Singh, Bijay-Singh. 2022. Prediction of grain yield and nitrogen uptake by basmati rice through in-season proximal sensing with a canopy reflectance sensor. Precision Agric. 23:733-47.
Vig J.R., Walls F.L. 2000. A review of sensor sensitivity and stability. Proceedings of the 2000 IEEE/EIA International Frequency Control Symposium and Exhibition (Cat. No.00CH37052), Kansas City, MO, USA. pp. 30-3.
World Agricultural Production. 2022. Foreign Agricultural Service/USDA.
World Reference Base (WRB). 2014. World Reference Base for soil resources 2014: international soil classification system for naming soils and creating legends for soil maps. Available from: https://www.fao.org/3/i3794en/I3794en.pdf
Xia T., Miao Y., Wu D., Shao H., Khosla R., Mi G. 2016. Active optical sensing of spring maize for in-season diagnosis of nitrogen status based on Nitrogen Nutrition Index. Remote Sens. 8:605.
Yin H., Cao Y., Marelli B., Zeng X., Mason A.J., Cao C. 2021. Soil Sensors and Plant Wearables for Smart and Precision Agriculture. Adv. Mater. 33:2007764.
Yiqing C., Moore K., Pellegrini A., Elhaddad A., Lessel J., Townsend C., Solak H., Semret N. 2017. Crop yield predictions - high resolution statistical model for intra-season forecasts applied to corn in the US. Curr. Biol. 7:R126.
Yue J., Yang G., Tian Q., Feng H., Xu K., Zhou C. 2019. Estimate of winter-wheat above-ground biomass based on UAV ultrahigh-ground-resolution image textures and vegetation indices. ISPRS J. Photogram. Remote Sens. 150:226-44.
How to Cite
Kostić, M. M. (2024) “An active-optical reflectance sensor in-field testing for the prediction of winter wheat harvest metrics”, Journal of Agricultural Engineering, 55(1). doi: 10.4081/jae.2024.1559.
Copyright (c) 2024 the Author(s)
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
PAGEPress has chosen to apply the Creative Commons Attribution NonCommercial 4.0 International License (CC BY-NC 4.0) to all manuscripts to be published.
Similar Articles
- Timothy Denen Akpenpuun, Qazeem Opeyemi Ogunlowo, Wook-Ho Na, Prabhat Dutta, Anis Rabiu, Misbaudeen Aderemi Adesanya, Mohammadreza Nariman, Ezatullah Zakir, Hyeon Tae Kim, Hyun-Woo Lee, Dynamic neural network modeling of thermal environments of two adjacent single-span greenhouses with different thermal curtain positions , Journal of Agricultural Engineering: Vol. 55 No. 2 (2024)
- Daniela Lovarelli, Elisabetta Riva, Gabriele Mattachini, Marcella Guarino, Giorgio Provolo, Assessing the effect of barns structures and environmental conditions in dairy cattle farms monitored in Northern Italy , Journal of Agricultural Engineering: Vol. 52 No. 4 (2021)
- Ernest Ekow Abano, Livingston Kobina Sam-Amoah, Ato Bart-Plange, Variation in ultrasonic frequency and time as pre-treatments to air-drying of carrot , Journal of Agricultural Engineering: Vol. 43 No. 4 (2012)
- Andrea Setti, Giulio Castelli, Lorenzo Villani, Roberto Ferrise, Elena Bresci, Modelling the impacts of water harvesting and climate change on rainfed maize yields in Senegal , Journal of Agricultural Engineering: Vol. 54 No. 3 (2023)
- Eva Audrey Yessito Houessou-Dossou, John Mwangi Gathenya, Mugwima Njuguna, Zachary Abiero Gariy, Andrea Petroselli, Comparative analysis of flood and rainfall frequency in the ungauged sub-watersheds of Kakia and Esamburumbur in Narok town, Kenya, using the EBA4SUB rainfall-runoff model , Journal of Agricultural Engineering: Vol. 53 No. 2 (2022)
- Yusuf Uzun, Mehmet Resit Tolun, Halil Tanyer Eyyuboglu, Filiz Sarı, An intelligent system for detecting Mediterranean fruit fly [Medfly; Ceratitis capitata (Wiedemann)] , Journal of Agricultural Engineering: Vol. 53 No. 3 (2022)
- Federico Preti, Tommaso Letterio, Shallow landslide susceptibility assessment in a data-poor region of Guatemala (Comitancillo municipality) , Journal of Agricultural Engineering: Vol. 46 No. 3 (2015)
- Stefano Melzi, Edoardo Sabbioni, Michele Vignati, Maurizio Cutini, Massimo Brambilla, Carlo Bisaglia, Eugenio Cavallo, Multibody model of fruit harvesting trucks: comparison with experimental data and rollover analysis , Journal of Agricultural Engineering: Vol. 49 No. 2 (2018)
- Ren Hiraoka, Yuya Aoyagi, Kazuki Kobayashi, Automatic travelling of agricultural support robot for a fruit farm. Verification of effectiveness of real-time kinematic-global navigation satellite system and developed a simulator for specification design , Journal of Agricultural Engineering: Vol. 54 No. 1 (2023)
- Vincenzo Bagarello, Vito Ferro, Dennis Flanagan, Predicting plot soil loss by empirical and process-oriented approaches. A review , Journal of Agricultural Engineering: Vol. 49 No. 1 (2018)
<< < 1 2 3 4 5 6 7 8 9 10 > >>
You may also start an advanced similarity search for this article.
