Mini İnsansız Hava Aracının Tahıl Islah Parsellerinde Fenotipik Seleksiyonda Kullanılabilirliği
Abstract
Islah çalışmalarının faydalılığını ortaya çıkarmak için verimli ve doğru fenotiplendirme gereklidir. Bitki ıslahı çalışmalarında yaşanılan tıkanıklıkların en önemli nedeni fenotiplendirmedir. Buna rağmen fenotipik seleksiyonda kullanılan mevcut metotlar hala yavaş, maliyetli, iş gücüne dayalı ve sıklıkla tahrip edici durumdadır. Bitki genomu ve çevresel (biyotik/abiyotik) interaksiyonların sonucu bitki fenotipi oluşmaktadır. Fenotiplemede bitki büyümesinin takibi, kanopi yapısı, fizyoloji, hastalık ve zararlılara karşı dayanıklılık ve verim gibi çok çeşitli bitki özelliklerinin ölçümü yapılmaktadır. Bu bağlamda görsel ve manuel ölçümlerden oluşan geleneksel metotlarla hızlı ve hassas fenotipleme yapmak ulaşılabilir bir sonuç değildir. Binlerce parsellerden oluşan ıslah programlarının başarıya ulaşabilmesi yüksek verimli fenotipleme (HTP) kullanımına bağlıdır. İnsansız hava araçlarının (İHA) hızlıca ve defalarca düşük maliyetlerle devreye alınabilmeleri, uçuş yükseklik ve zamanlarının kullanıcıya uygun ayarlanabilmeleri, yüksek çözünürlüklü görüntü alabilmeleri ve küçük ölçekli araştırmalarda kullanılabilmeleri gibi avantajları ile mini insansız hava araçları yüksek verimli fenotipik seleksiyon için bir fırsat oluşturmaktadır. Böylece çeşit geliştirme ve ıslah çalışmalarında süper genotip özellikleri belirlemede yaşanan engeller ortadan kaldırılabilecektir. Aynı zamanda bu çalışmalar bitki ıslahçıları ve agronomistler için yeni metot geliştirmeye yönelik fırsatlar oluşturacaktır. Bu çalışma tahıl ıslah çalışmaları önünde en büyük engellerden biri olan fenotipleme çalışmalarında yaşanan problemlerin ortadan kaldırılmasına ışık tutacaktır.
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Araus JL, Cairns JE (2014). Field High-Throughput Phenotyping: The New Crop Breeding Frontier. Trends Plant Science, 19: 52–61.
Bausch WC, Khosla R (2010). Quickbird Satellite Versus Ground-Based Multi-Spectral Datafor Estimating Nitrogen Status of Irrigated Maize. Precision Agriculture,11: 274–290.
Borrell AK, Van Oosterom EJ, Mullet JE, George-Jaeggli B, Jordan DR, Klein PE, Hammer GL (2014). Stay-Green Alleles Individually Enhance Grain Yield in Sorghum Under Drought By Modifying Canopy Development and Water Uptake Patterns. New Phytology, 203(3): 817–830.
Calderon R, Navas-Cortes JA, Lucena C, Zarco-Tejada PJ (2013). High-Resolution Airborne Hyperspectral and Thermal Imagery for Early Detection of Verticillium Wilt of Olive Using Fluorescence, Temperature and Narrow-Band Spectral Indices. Remote Sensing of Environment, 139: 231–245.
Chang J, Clay DA, Dalsted K, Clay S, O’Neill M (2003). Corn (Zea mays L.) Yield Prediction Using Multispectral and Multidate Reflectance. Agronomy Journal, 95: 1447–1453.
Chapman SC, Merz T, Chan A, Jackway P, Hrabar S, Dreccer MF (2014). Pheno-Copter: A Low-Altitude, Autonomous Remote-Sensing Grobotic HelicopterForHigh-Throughput Field-Based Phenotyping. Agronomy, 4: 279–301.
Deery D, Jimenez-Berni J, Jones H, Sirault X, Furbank R (2014). Proximal Remote Sensing Buggies and Potential Applications for Field-Based Phenotyping. Agronomy, 4: 349–379.
Gonzalez-Dugo V, Hernandez P, Solis I, Zarco-Tejada PJ (2015). Using High-Resolution Hyperspectral and Thermal Airborne Imagery to Assess Physiological Condition in The Context of Wheat Phenotyping. Remote Sensing, 7: 13586-13605.
Haghighattalab A, Gonzalez Perez L, Mondali S, Singh D, Schinstock D, Rutkoski J, Ortiz‑Monasterio I, Singh RP, Goodin D, Poland J (2016). Application of Unmanned Aerial Systems for High Throughput Phenotyping of Large Wheat Breeding Nurseries. Plant Methods, 1-15.
Holman FH, Riche AB,Michalski A, Castle M, Wooster MJ, Hawkesford MJ (2016). High Throughput Field Phenotyping of Wheat Plant Height and Growth Rate in Field Plot Trials Using Uav Based Remote Sensing. Remote Sensing, 8 (1031): 2-24.
Jannoura R, Brinkmann K, Uteau D, Bruns C, Joergensen RG (2015). Monitoring of Crop Biomass Using True Colour Aerial Photographs Taken From A Remote Controlled Hexacopter. Biosystem Engineering, 129: 341-351.
Jia L, Chen X, Zhang F, Buerkert A, Römheld B (2004). Use of Digital Camera To Assess Nitrogen Status of Winter Wheat In The Northern China Plain. Journal of Plant Nutrition, 27 (3): 441-450.
Jones HG, Serraj R, Loveys BR, Xiong L, Wheaton A, Price AH (2009). Thermal Infrared Imaging of Crop Canopies For The Remote Diagnosis and Quantificationof Plant Responses To Water Stress In The Field. Functional Plant Biology, 36: 978–989.
Khanna R, Moller M, Pfeifer J, Liebisch F, Walter A, Siegwart R (2015). Beyond Point Clouds-3d Mapping And Field Parameter Measurements Using Uavs. IEEE 20th Conference on Emerging Technologies and Factory Automation.
Khot LR, Zhang Q, Karkee M, Sankaran S, Lewis K (2014). Unmanned Aerial Systems in Agriculture: Part 1 (systems). WSU Extension, 1-5.
Lelong CCD, Burger P, Jubelin G, Roux B, Labbe S, Baret F (2008). Assessment of Unmanned Aerial Vehicles Imagery For Quantitative Monitoring of Wheat Crop in Small Plots. Sensors, 8: 3557-3585.
Li F, Gnyp ML, Jia L, Miao Y, Yu Z, Koppe W, Bareth G, Chen X, Zhang F (2008). Estimating N Status of Winter Wheat Using A Handheld Spectrometer in The North China Plain. Field Crop Research, 106: 77–85.
Li J, Zhang F, Qian X, Zhu Y, Shen G (2015). Quantification of Rice Canopy Nitrogen Balance Index With Digital Imagery From Unmanned Aerial Vehicle. Remote Sensing Letters, 6 (3): 183-189.
Lopes MS, Reynolds MP (2012). Stay-Green in Spring Wheat Can Be Determined By Spectral Reflectance Measurements (normalized difference vegetation index) Independently From Phenology. Journal of Experimental Botany, 63(10): 3789–3798.
Mefford BS (2014). Assessing Corn Water Stress Using Spectral Reflectance.Colorado State University, Department of Civil and Environmental Engineering, Master of Thesis (unprinted).
Nebiker S, Lack N, Abacherli M, Laderach S (2016). Light-Weight Multispectral Uav Sensors And Their Capabilities For Predicting Grain Yield And Detecting Plant Diseases, The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XLI-B1, 2016 XXIII ISPRS Congress, 12–19 July 2016, Prague, Czech Republic.
Neiff N, Dhliwayo T, Suarez EA, Burgueno J, Trachsel S (2015). Using AnAirborne Platform To Measure Canopy Temperature And NDVI Under Heat Stress in Maize. Journal of Crop Improvement, 29 (6): 669-690.
Öztürk A, Akkaya A (1996). Kışlık Buğdayda Verim, Verim Öğeleri Ve Fenolojik Dönemler Arasındaki İlişkiler. Atatürk Üniversitesi Ziraat Fakültesi Dergisi, 27 (3): 350-368.
Pena-Barragan JM (2012). Object-Based Approach For Crop Row Characterization in Uav Images For Site-Specific Weed Management. Proceedings of the 4th GEOBIA, May 7-9, 2012, Rio de Janerio-Brazil.
Perry EM, Brand J, Kant S, Fitzgerald GJ (2012). Field-Based Rapid Phenotyping With Unmanned Aerial Vehicles (UAV). Precision Agriculture, 1-5.
Rabatel G, Gorretta N, Labbe S (2014). Getting Simultaneous Red And Near-İnfrared Band Data From A Single Digital Camera For Plant Monitoring Applications: Theoretical And Practical Study. Biosystem Engineering, 117: 2-14.
Ranjitha G, Srinivasan MR, Rajesh A (2014). Detection And Estimation Of Damage Caused By Thrips Tabaci (Lind) Of Cotton Using Hyperspectral Radiometer. Agrotechnology, 3:1–5.
Rasmussen J, Ntakos G, Nielsen J, Svensgaard J, Poulsen RN, Christensen S (2016). Are Vegetation Indices Derived From Consumer-Grade Cameras Mounted On Uavs Sufficiently Reliable For Assessing Experimental Plots? European Journal of Agronomy, 74: 75-92.
Sankaran S, Khot LR, Zuniga Espinoza C, Jarolmasjed S, Sathuvalli VR, Vandemark GJ, Miklas PN, Carter AH, Pumphrey MO, Knowles NR, Pavek MJ (2015). Low-Altitude, High-Resolution Aerial Imaging Systems For Row And Field Crop Phenotyping: A Review. European Journal of Agronomy, 70: 112-123.
Shi Y, Thomasson JA, Murray SC, Pugh NA, Rooney WL, Shafian S (2016). Unmanned Aerial Vehicles For High-Throughput Phenotyping And Agronomic Research. PLoS ONE, 11(7): 1-26.
Swain KC, Uz Zaman Q (2012). Rice Crop Monitoring With Unmanned Helicopter Remote Sensing Images, ed. T. Fatoyinbo (Rijeka: InTech), 254–272.
Tucker CJ (1979). Red And Photographic Infrared Linear Combinations For Monitoring Vegetation. Remote Sensing of Environment, 8: 127-150.
Usul M (2010). Arazi Kalite Parametrelerinin Buğday Ürün Rekoltesi Üzerine Etkilerinin Uzaktan Algılama ve Coğrafi Bilgi Sistemi Kullanılarak Belirlenmesi Altınova Tarım İşletmesi Örneği. Ankara Üniversitesi, Fen Bilimleri Enstitüsü, Toprak Anabilim Dalı, Doktora Tezi (Basılmamış).
Watanabe K, Guo W, Arai K,Takanashi H, Kajiya-Kanegae H,Kobayashi M, Yano K, Tokunaga T,Fujiwara T, Tsutsumi N, Iwata H (2017). High-Throughput Phenotyping of Sorghum Plant Height Using An Unmanned Aerial Vehicle And Its Application To Genomic Prediction Modeling. Frontiers Plant Science, 8:4-21.
Wei X, Xu J, Guo H, Jiang L, Chen S, Yu C, Zhou Z, Hu P, Zhai H, Wan J (2010). DTH8 Suppresses Flowering in Rice, Influencing Plant Height And Yield Potential Simultaneously. Plant Physiology, 153: 1747–1758.
White JW, Andrade-Sanchez P, Gore MA, Bronson KF, Coffelt TA, Conley MM, Feldmann KA, French AN, Heun JT, Hunsaker DJ, Jenks MA, Kimball BA, Roth RL, Strand RJ, Thorp KR, Wall GW, Wang G (2012). Field-Based Phenomics For Plant Genetics Research. Field Crops Research, 133: 101–112.
Wojtowicz M, Wojtowicz A, Piekarczyk J (2016). Application of Remote Sensing Methods in Agriculture. Communications in Biometry and Crop Science,11: 31–50.
Yıldız H, Mermer A, Ünal E, Akbaş F (2012). Türkiye Bitki Örtüsünün NDVI Verileri İle Zamansal Ve Mekansal Analizi. Tarla Bitkileri Merkez Araştırma Enstitüsü Dergisi, 21 (2): 50-56.
Zaman‑Allah M, Vergara O, Araus JL, Tarekegne1 A, Magorokosho1 C, Zarco‑Tejada PJ, Hornero A, Hernandez Alba H, Das B, Craufurd P, Olsen M, Prasanna BM, Cairns J (2015). Unmanned Aerial Platform‑Based Multi‑Spectral Imaging For Field Phenotyping of Maize. Plant Methods, 1-10.
Zarco-Tejada PLJ, Rueda CA, Ustin SL (2003). Water Content Estimation in Vegetation With MODIS Reflectance Data And Model Inversion Methods. Remote Sensing of Environment, 85: 109–124.
Zhang C, Kovacs JM (2012). The Application Of Small Unmanned Aerial Systems For Precision Agriculture: A Review. Precision agriculture, 13(6): 693–712.
DOI: https://doi.org/10.15316/SJAFS.2018.144
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