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In this study, the effects of fresh and aged solid and liquid animal manure applied at different doses on plant growth were investigated using the vegetation indices derived from the images obtained from an unmanned aerial vehicle (UAV). For this purpose, the fresh weight of wheat plants were determined during the grain filling period for each of the treatment. In addition, Normalized Difference Vegetation Index (NDVI), Normalized Difference Red Edge Index (NDRE) and Red Edge Chlorophyll Index (CIRE) and the Soil-Adjusted Vegetation Index (SAVI) derived from the UAV images were calculated for the different treatments. The study was carried out in the research and application fields of the Agricultural Faculty in Harran University Osmanbey campus. The images were taken with an UAV mounted a camera capable of recording reflection in the red (R-660 nm), green (G-550 nm), near infrared (NIR-790 nm) and red edge (RedEdge-735 nm) spectrum. The results showed that the plant fresh weight and all vegetation indices increased significantly with the increase in application doses. The fresh plant weight in low doses was similar to the control plots where neither manure nor a fertilizer applied. However, average plant index values at low doses were higher than the plant index values of the control plot. The most reliable result (R2= 0.70 and P<0.01) in the estimation of wheat wet weight at grain filling period was obtained with the regression model obtained using NDVI values. The fresh weight of wheat plants in this period could be estimated with 70% accuracy using a quadratic polynomial equation. The results showed that plant growth can be monitored and yield estimation can be successfully carried out using an UAVs with low altitude flight capability and high resolution camera.

Unmanned aerial vehicle, wheat, vegetation index, NDVI, NRDE, CIRE, SAVI

Kaynakça Anonim. (2021). Meteoroloji Genel Müdürlüğü, İllere ait Mevsim Normalleri. https://www.mgm.gov.tr/veridegerlendirme/il-ve-ilceler-istatistik.aspx?m=SANLIURFA Erişim tarihi. 10 Ekim 2021. Barnes, E.M.; Clarke, T.R.; Richards, S.E.; Colaizzi, P.D.; Haberland, J.; Kostrzewski, M.; Waller, P.M.; Choi, C.Y.; Riley, E.; Thompson, T.L.; et al. Coincident detection of crop water stress, nitrogen status and canopy density using ground–based multispectral data. In Proceedings of the International Conference on Precision Agriculture and Other Resource Management, Bloomington, MN, USA, 16–19 July 2000. Çimen, M. (2016). Mühendislik verilerinde tek örnek için parametrik ve parametrik olmayan testler. İstanbul Aydın Üniversitesi Dergisi, 8(29), 67-77. Debaeke, P., Rouet, P., & Justes, E. (2006). Relationship between the normalized SPAD index and the nitrogen nutrition index: application to durum wheat. Journal of plant nutrition, 29(1), 75-92. Dogan, H. M., & Kılıç, O. M. (2013). Modelling and mapping some soil surface properties of Central Kelkit Basin in Turkey by using Landsat-7 ETM+ images. International journal of remote sensing, 34(15), 5623-5640. Fu, Z., Jiang, J., Gao, Y., Krienke, B., Wang, M., Zhong, K., ... & Liu, X. (2020). Wheat growth monitoring and yield estimation based on multi-rotor unmanned aerial vehicle. Remote Sensing, 12(3), 508. Gitelson, A.A.; Andrés, V.; Verónica, C.; Rundquist, D.C.; Arkebauer, T.J. Remote estima–tion of canopy chlorophyll content in crops. Geophys. Res. Lett. 2005, 32, 93–114. Guan, S., Fukami, K., Matsunaka, H., Okami, M., Tanaka, R., Nakano, H., ... & Takahashi, K. (2019). Assessing correlation of high-resolution NDVI with fertilizer application level and yield of rice and wheat crops using small UAVs. Remote Sensing, 11(2), 112. Han, J., Zhang, Z., Cao, J., Luo, Y., Zhang, L., Li, Z., & Zhang, J. (2020). Prediction of winter wheat yield based on multi-source data and machine learning in China. Remote Sensing, 12(2), 236. Hassan, M. A., Yang, M., Rasheed, A., Yang, G., Reynolds, M., Xia, X., ... & He, Z. (2019). A rapid monitoring of NDVI across the wheat growth cycle for grain yield prediction using a multi-spectral UAV platform. Plant science, 282, 95-103. Huete, A.R. A (1988). Soil-adjusted vegetation index (SAVI). Remote Sens. Environ, 25, 295–309. Hunt Jr, E. R., & Daughtry, C. S. (2018). What good are unmanned aircraft systems for agricultural remote sensing and precision agriculture?. International journal of remote sensing, 39(15-16), 5345-5376. Kross, A., McNairn, H., Lapen, D., Sunohara, M., & Champagne, C. (2015). Assessment of RapidEye vegetation indices for estimation of leaf area index and biomass in corn and soybean crops. International Journal of Applied Earth Observation and Geoinformation, 34, 235-248. Manfreda, S., McCabe, M. F., Miller, P. E., Lucas, R., Pajuelo Madrigal, V., Mallinis, G., ... & Toth, B. (2018). On the use of unmanned aerial systems for environmental monitoring. Remote sensing, 10(4), 641. Maresma, Á., Ariza, M., Martínez, E., Lloveras, J., & Martínez-Casasnovas, J. A. (2016). Analysis of vegetation indices to determine nitrogen application and yield prediction in maize (Zea mays L.) from a standard UAV service. Remote Sensing, 8(12), 973. Olson, D., Chatterjee, A., Franzen, D. W., & Day, S. S. (2019). Relationship of drone-based vegetation indices with corn and sugarbeet yields. Agronomy Journal, 111(5), 2545-2557. Olson, D., Chatterjee, A., Franzen, D. W., & Day, S. S. (2019). Relationship of drone-based vegetation indices with corn and sugarbeet yields. Agronomy Journal, 111(5), 2545-2557. Öztürk, H. 2021. Harran Ovasında tarla ölçeğinde mesafeye bağlı değişimin jeoistatiksel yöntemlerle belirlenmesi. Yüksek Lisans Tezi. Harran Üniversitesi, Fen Bilimleri Enstitüsü. YÖK Tez No: 682490. s. 78. Pix4D. (2017). Pix4Dmapper 4.1 Used Manual. Lausane. Schut, A. G., Traore, P. C. S., Blaes, X., & Rolf, A. (2018). Assessing yield and fertilizer response in heterogeneous smallholder fields with UAVs and satellites. Field Crops Research, 221, 98-107. Tucker, C.J. (1979). Red and photographic infrared linear combinations for monitoring vegetation. Remote Sensing of Environment 8: 127–150. Uyaner, M., Karaşahin, M., Bilici, M., Topal, İ., Yakışır, E., & Kelleş, R. (2020). Mini İnsansız Hava Aracının Tahıl Islah Parsellerinde Verim Tahmininde Kullanılabilirliği. Bahri Dağdaş Bitkisel Araştırma Dergisi, 9(2), 280-294. Yue, J., Feng, H., Li, Z., Zhou, C., & Xu, K. (2021). Mapping winter-wheat biomass and grain yield based on a crop model and UAV remote sensing. International Journal of Remote Sensing, 42(5), 1577-1601. Yue, X., Hu, Y., Zhang, H., & Schmidhalter, U. (2020). Evaluation of both SPAD reading and SPAD index on estimating the plant nitrogen status of winter wheat. International Journal of Plant Production, 14(1), 67-75. Wang, H., Mortensen, A. K., Mao, P., Boelt, B., & Gislum, R. (2019). Estimating the nitrogen nutrition index in grass seed crops using a UAV-mounted multispectral camera. International Journal of Remote Sensing, 40(7), 2467-2482. Zarco-Tejada, P. J., Miller, J. R., Morales, A., Berjón, A., & Agüera, J. (2004). Hyperspectral indices and model simulation for chlorophyll estimation in open-canopy tree crops. Remote sensing of environment, 90(4), 463-476. Zeng, L., Peng, G., Meng, R., Man, J., Li, W., Xu, B., ... & Sun, R. (2021). Wheat Yield Prediction Based on Unmanned Aerial Vehicles-Collected Red–Green–Blue Imagery. Remote Sensing, 13(15), 2937. Zhou, X., Kono, Y., Win, A., Matsui, T., & Tanaka, T. S. (2021). Predicting within-field variability in grain yield and protein content of winter wheat using UAV-based multispectral imagery and machine learning approaches. Plant Production Science, 24(2), 137-151.

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