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Determination of Demonstrating Problematic Growth of Plants with Use Unmanned Air Vehicle (UAVs)

Yıl 2020, Cilt: 2 Sayı: 1, 12 - 22, 25.06.2020

Sinan Demir Levent Başayiğit

Öz

Environment-oriented approaches that emerged as the need for agricultural production have increased the use of unmanned aerial vehicles (UAVs) for these purposes. Firstly, unmanned Aerial Vehicles were used as a good tool for providing the necessary data for agricultural management. Afterward, it was used for agricultural activities along with other technological products.
In this study, there was an example of the use of agricultural drones and multispectral sensors to provide data for agricultural production. For this purpose, an approach was set up to determine the health status of plants using images obtained from drones and sensors.
The research was carried out in the Education, Research and Application Farm of Agriculture Faculty, ISUBÜ. The farm included different land used/canopy cover types. In the process, the high spatial accuracy (RMSE <0.30 m) images were taken from the plants for the test plots. NDVI and TGI index were made in these images to distinguish.
As a result of the study, it was determined that healthy plants were distinguished with great accuracy. It was concluded that areas requiring urgent intervention could be identified at the beginning of the land.
It was found that the study has the potential to be developed as a method of providing data in production systems require for Good Agricultural Practices (GAP), Smart Agriculture and Agriculture 4.0.

Anahtar Kelimeler

UAVs Sustainable Agriculture Agriculture Drone Sensor VI NDVI TGI

Kaynakça

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  • Boon, M. A., Greenfield, R., & Tesfamichael, S. (2016). Wetland assessment using unmanned aerial vehicle (UAV) photogrammetry.
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  • Do, D., Pham, F., Raheja, A., & Bhandari, S. (2018, May). Machine learning techniques for the assessment of citrus plant health using UAV-based digital images. In Autonomous Air and Ground Sensing Systems for Agricultural Optimization and Phenotyping III (Vol. 10664, p. 106640O). International Society for Optics and Photonics.
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  • Gade, R., & Moeslund, T. B. (2014). Thermal cameras and applications: a survey. Machine vision and applications, 25(1), 245-262.
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  • Gitelson, A. A., Stark, R., Grits, U., Rundquist, D., Kaufman, Y., & Derry, D. (2002). Vegetation and soil lines in visible spectral space: a concept and technique for remote estimation of vegetation fraction. International Journal of Remote Sensing, 23(13), 2537-2562.
  • Gitelson, A., & Merzlyak, M. N. (1994). Quantitative estimation of chlorophyll-a using reflectance spectra: Experiments with autumn chestnut and maple leaves. Journal of Photochemistry and Photobiology B: Biology, 22(3), 247-252.
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Sorunlu Gelişim Gösteren Bitkilerin İnsansız Hava Araçları (İHA) ile Belirlenmesi

Yıl 2020, Cilt: 2 Sayı: 1, 12 - 22, 25.06.2020

Sinan Demir Levent Başayiğit

Öz

Günümüzde tarımsal üretimin ihtiyacı olarak ortaya çıkan çevre odaklı yaklaşımlar İnsansız Hava Araçlarının (İHA) bu amaçlara yönelik kullanımını hızla artırmıştır. İnsansız Hava Araçları öncelikle tarımsal üretim için gerekli verilerin sağlanmasında iyi bir araç olmuştur. Ardından diğer teknolojik ürünler ile birlikte bazı tarımsal üretim faaliyetlerinde doğrudan kullanım alanı bulmuştur.
Bu çalışmada, tarımsal üretime veri sağlamada tarım dronu ve multispektral algılama kameralarının kullanımına ait bir örnek yeralmaktadır. Bu amaçla dron ve kameralar ile elde edilen görüntülerden bitkilerin sağlık durumlarının belirlenmesine yönelik uygulama yapılmıştır.
Farklı bitki desenlerinin yer aldığı ISUBÜ Ziraat Fakültesi, Eğitim, Araştırma ve Uygulama Çiftliğinde yürütülen çalışmada seçilen test alanı için yüksek mekânsal doğrulukta (RMSE<0.30 m) görüntülerin üretimi mümkün olmuştur. Bu görüntülerde yapılan NDVI ve TGI ayrımları ile sağlıklı bitkilerin büyük doğrulukla ayırt edildiği ve acil müdahale gerektiren alanların arazi başında belirlenebildiği sonucuna varılmıştır.
Çalışmanın, İyi Tarım Uygulamaları, Akıllı Tarım ve Tarım 4.0 uygulamalarında veri sağlama yöntemi olarak kullanılma ve geliştirilme potansiyeli olduğu sonucuna varılmıştır.

Anahtar Kelimeler

İHA Sürdürülebilir Tarım Tarımsal Dron Sensör VI NDVI TGI

Kaynakça

  • Arnold, T., De Biasio, M., Fritz, A., & Leitner, R. (2013, December). UAV-based measurement of vegetation indices for environmental monitoring. In 2013 Seventh International Conference on Sensing Technology (ICST) (pp. 704-707). IEEE.
  • Ayala-Silva, T., & Beyl, C. A. (2005). Changes in spectral reflectance of wheat leaves in response to specific macronutrient deficiency. Advances in Space Research, 35(2), 305-317.
  • Bacco, M., Barsocchi, P., Ferro, E., Gotta, A., & Ruggeri, M. (2019). The Digitisation of Agriculture: a Survey of Research Activities on Smart Farming. Array, 3, 100009.
  • Bachmann, F., Herbst, R., Gebbers, R., & Hafner, V. V. (2013). Micro UAV based georeferenced orthophoto generation in VIS+ NIR for precision agriculture.
  • Banerjee, K., Krishnan, P., & Mridha, N. (2018). Application of thermal imaging of wheat crop canopy to estimate leaf area index under different moisture stress conditions. Biosystems Engineering, 166, 13-27.
  • Barbedo, J. G. A. (2019). A review on the use of unmanned aerial vehicles and imaging sensors for monitoring and assessing plant stresses. Drones, 3(2), 40.
  • Basayigit, L., Bozkurt, Y., & Kaya, I. (2009). Determination of Grasslands Using Landsat (TM) Data and Monitoring of The Change By Years Using GIS With Special Reference to Kars Province in Turkey. Fresenius Environmental Bulletin, 18(1), 62-97.
  • Başayiğit, L., Dedeoğlu, M., & Akgül, H. (2015). The prediction of iron contents in orchards using VNIR spectroscopy. Turkish Journal of Agriculture and Forestry, 39(1), 123-134.
  • Berni, J. A., Zarco-Tejada, P. J., Suárez, L., & Fereres, E. (2009). Thermal and narrowband multispectral remote sensing for vegetation monitoring from an unmanned aerial vehicle. IEEE Transactions on geoscience and Remote Sensing, 47(3), 722-738.
  • Boon, M. A., Greenfield, R., & Tesfamichael, S. (2016). Wetland assessment using unmanned aerial vehicle (UAV) photogrammetry.
  • Borlaug, N. E. (2019). Applying Agricultural Science and Technology to World Hunger Problems. Beef Cattle Science Handbook, 20.
  • Cai, G., Chen, B. M., & Lee, T. H. (2010). An overview on development of miniature unmanned rotorcraft systems. Frontiers of Electrical and Electronic Engineering in China, 5(1), 1-14.
  • Clevers, J. G., & Kooistra, L. (2011, June). Using hyperspectral remote sensing data for retrieving total canopy chlorophyll and nitrogen content. In 2011 3rd Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS) (pp. 1-4). IEEE.
  • Datt, B., McVicar, T. R., Van Niel, T. G., Jupp, D. L., & Pearlman, J. S. (2003). Preprocessing EO-1 Hyperion hyperspectral data to support the application of agricultural indexes. IEEE Transactions on Geoscience and Remote Sensing, 41(6), 1246-1259.
  • Daughtry, C. S. T., Walthall, C. L., Kim, M. S., De Colstoun, E. B., & McMurtrey Iii, J. E. (2000). Estimating corn leaf chlorophyll concentration from leaf and canopy reflectance. Remote sensing of Environment, 74(2), 229-239.
  • Demir, S. (2017). Haşhaş (Papaver Somniferum) Tarım Alanlarının Yüksek Çözünürlüklü Uydu Verileri ile Belirlenebilirliği Süleyman Demirel Üniversitesi Den Bilimleri Enstitüsü, Yüksek Lisans Tezi, Isparta, 34 s.
  • Demir, S. ve Başayiğit, L. (2019). Yüksek Çözünürlüklü Uydu Görüntüleri Kullanarak Haşhaş (Papaver Somniferum) Parsellerinin Belirlenmesi. Hint Uzaktan Algılama Derneği Dergisi , 47 (6), 977-987. DJI, 2019. DJI drone üreticisi (Phantom Serisi), Hong Kong. https://www.dji.com/support/product/phantom-4-pro (Erişim tarihi: 20 Aralık 2019)
  • Di Gennaro, S. F., Rizza, F., Badeck, F. W., Berton, A., Delbono, S., Gioli, B., ... & Matese, A. (2018). UAV-based high-throughput phenotyping to discriminate barley vigour with visible and near-infrared vegetation indices. International journal of remote sensing, 39(15-16), 5330-5344.
  • Do, D., Pham, F., Raheja, A., & Bhandari, S. (2018, May). Machine learning techniques for the assessment of citrus plant health using UAV-based digital images. In Autonomous Air and Ground Sensing Systems for Agricultural Optimization and Phenotyping III (Vol. 10664, p. 106640O). International Society for Optics and Photonics.
  • ERDAS (1999). ERDAS IMAGINE 8.2. field guide. Erdas INC. Atlanta, Georgia.
  • Farooq, M. S., Riaz, S., Abid, A., Abid, K., & Naeem, M. A. (2019). A Survey on the Role of IoT in Agriculture for the Implementation of Smart Farming. IEEE Access, 7, 156237-156271.
  • Gade, R., & Moeslund, T. B. (2014). Thermal cameras and applications: a survey. Machine vision and applications, 25(1), 245-262.
  • Garcia-Ruiz, F., Sankaran, S., Maja, J. M., Lee, W. S., Rasmussen, J., & Ehsani, R. (2013). Comparison of two aerial imaging platforms for identification of Huanglongbing-infected citrus trees. Computers and Electronics in Agriculture, 91, 106-115.
  • Gitelson, A. A., Stark, R., Grits, U., Rundquist, D., Kaufman, Y., & Derry, D. (2002). Vegetation and soil lines in visible spectral space: a concept and technique for remote estimation of vegetation fraction. International Journal of Remote Sensing, 23(13), 2537-2562.
  • Gitelson, A., & Merzlyak, M. N. (1994). Quantitative estimation of chlorophyll-a using reflectance spectra: Experiments with autumn chestnut and maple leaves. Journal of Photochemistry and Photobiology B: Biology, 22(3), 247-252.
  • Gómez-Candón, D., De Castro, A. I., & López-Granados, F. (2014). Assessing the accuracy of mosaics from unmanned aerial vehicle (UAV) imagery for precision agriculture purposes in wheat. Precision Agriculture, 15(1), 44-56.
  • Grenzdörffer, G. J., Engel, A., & Teichert, B. (2008). The photogrammetric potential of low-cost UAVs in forestry and agriculture. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 31(B3), 1207-1214.
  • Harwin, S., & Lucieer, A. (2012). Assessing the accuracy of georeferenced point clouds produced via multi-view stereopsis from unmanned aerial vehicle (UAV) imagery. Remote Sensing, 4(6), 1573-1599.
  • Huang, Y., Reddy, K. N., Fletcher, R. S., & Pennington, D. (2018). UAV low-altitude remote sensing for precision weed management. Weed technology, 32(1), 2-6.
  • Huete, A., Justice, C., & Van Leeuwen, W. (1999). MODIS vegetation index (MOD13). Algorithm theoretical basis document, 3, 213.
  • Hunt, E. R., Daughtry, C. S. T., Eitel, J. U., & Long, D. S. (2011). Remote sensing leaf chlorophyll content using a visible band index. Agronomy Journal, 103(4), 1090-1099.
  • Hunt Jr, E. R., Doraiswamy, P. C., McMurtrey, J. E., Daughtry, C. S., Perry, E. M., & Akhmedov, B. (2013). A visible band index for remote sensing leaf chlorophyll content at the canopy scale. International Journal of Applied Earth Observation and Geoinformation, 21, 103-112.
  • 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 Sensing of Environment, 198, 105-114.
  • Kallapur, A. G., & Anavatti, S. G. (2006, November). UAV linear and nonlinear estimation using extended Kalman filter. In 2006 International Conference on Computational Inteligence for Modelling Control and Automation and International Conference on Intelligent Agents Web Technologies and International Commerce (CIMCA'06) (pp. 250-250). IEEE.
  • Kavvadias, A., Psomiadis, E., Chanioti, M., Gala, E., & Michas, S. (2015, September). Precision Agriculture-Comparison and Evaluation of Innovative Very High Resolution (UAV) and LandSat Data. In HAICTA (pp. 376-386).
  • Laliberte, A. S., Herrick, J. E., Rango, A., & Winters, C. (2010). Acquisition, orthorectification, and object-based classification of unmanned aerial vehicle (UAV) imagery for rangeland monitoring. Photogrammetric Engineering & Remote Sensing, 76(6), 661-672.
  • Lelong, C., Burger, P., Jubelin, G., Roux, B., Labbé, S., & Baret, F. (2008). Assessment of unmanned aerial vehicles imagery for quantitative monitoring of wheat crop in small plots. Sensors, 8(5), 3557-3585.
  • Li, L., Zhang, Q., & Huang, D. (2014). A review of imaging techniques for plant phenotyping. Sensors, 14(11), 20078-20111.
  • Mäkynen, J., Saari, H., Holmlund, C., Mannila, R., & Antila, T. (2012, May). Multi-and hyperspectral UAV imaging system for forest and agriculture applications. In Next-Generation Spectroscopic Technologies V (Vol. 8374, p. 837409). International Society for Optics and Photonics.
  • Malenovský, Z., Lucieer, A., King, D. H., Turnbull, J. D., & Robinson, S. A. (2017). Unmanned aircraft system advances health mapping of fragile polar vegetation. Methods in Ecology and Evolution, 8(12), 1842-1857.
  • Mckinnon, T., & Hoff, P. (2017). Comparing RGB-based vegetation indices with NDVI for drone based agricultural sensing. Agribotix. Com, 1-8.
  • MGM, 2019. Türkiye İklim İstatistikleri. Meteoroloji Genel Müdürlüğü, Ankara. https://www.mgm.gov.tr/veridegerlendirme/il-ve-ilceler-istatistik.aspx?m=ISPARTA (Erişim tarihi:15.12.2019)
  • Neitzel, F., & Klonowski, J. (2011). Mobile 3D mapping with a low-cost UAV system. Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci, 38, 1-6.
  • Norasma, C. Y. N., Fadzilah, M. A., Roslin, N. A., Zanariah, Z. W. N., Tarmidi, Z., & Candra, F. S. (2019, April). Unmanned Aerial Vehicle Applications In Agriculture. In IOP Conference Series: Materials Science and Engineering (Vol. 506, No. 1, p. 012063). IOP Publishing.
  • Ojha, T., Misra, S., & Raghuwanshi, N. S. (2015). Wireless sensor networks for agriculture: The state-of-the-art in practice and future challenges. Computers and Electronics in Agriculture, 118, 66-84.
  • Pádua, L., Marques, P., Adão, T., Guimarães, N., Sousa, A., Peres, E., & Sousa, J. J. (2019). Vineyard Variability Analysis through UAV-Based Vigour Maps to Assess Climate Change Impacts. Agronomy, 9(10), 581.
  • Panagiotidis, D., Abdollahnejad, A., Surový, P., & Kuželka, K. (2019). Detection of fallen logs from high-resolution UAV Images. New Zealand Journal of Forestry Science, 49.
  • Peppa, M. V., Hall, J., Goodyear, J., & Mills, J. P. (2019). Photogrammetric assessment and comparison of DJI Phantom 4 pro and phantom 4 RTK small unmanned aircraft systems. ISPRS Geospatial Week 2019.
  • Pimstein, A., Karnieli, A., Bansal, S. K., & Bonfil, D. J. (2011). Exploring remotely sensed technologies for monitoring wheat potassium and phosphorus using field spectroscopy. Field Crops Research, 121(1), 125-135.
  • Raeva, P. L., Šedina, J., & Dlesk, A. (2019). Monitoring of crop fields using multispectral and thermal imagery from UAV. European Journal of Remote Sensing, 52(sup1), 192-201.
  • Rango, A., Laliberte, A., Herrick, J. E., Winters, C., Havstad, K., Steele, C., & Browning, D. (2009). Unmanned aerial vehicle-based remote sensing for rangeland assessment, monitoring, and management. Journal of Applied Remote Sensing, 3(1), 033542.
  • Ray, P. P. (2017). Internet of things for smart agriculture: Technologies, practices and future direction. Journal of Ambient Intelligence and Smart Environments, 9(4), 395-420.
  • Rock, G., Ries, J. B., & Udelhoven, T. (2011, January). Sensitivity analysis of UAV-photogrammetry for creating digital elevation models (DEM). In Proceedings of Conference on Unmanned Aerial Vehicle in Geomatics. Switzerland: Zurich.
  • Rokhmana, C. A. (2015). The potential of UAV-based remote sensing for supporting precision agriculture in Indonesia. Procedia Environmental Sciences, 24, 245-253.
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  • Senthilnath, J., Kandukuri, M., Dokania, A., & Ramesh, K. N. (2017). Application of UAV imaging platform for vegetation analysis based on spectral-spatial methods. Computers and Electronics in Agriculture, 140, 8-24..
  • Shamshiri, R. R., Hameed, I. A., Balasundram, S. K., Ahmad, D., Weltzien, C., & Yamin, M. (2018). Fundamental research on unmanned aerial vehicles to support precision agriculture in oil palm plantations. Agricultural Robots-Fundamentals and Application.
  • Simic Milas, A., Romanko, M., Reil, P., Abeysinghe, T., & Marambe, A. (2018). The importance of leaf area index in mapping chlorophyll content of corn under different agricultural treatments using UAV images. International journal of remote sensing, 39(15-16), 5415-5431.
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  • Stroppiana, D., Villa, P., Sona, G., Ronchetti, G., Candiani, G., Pepe, M., ... & Boschetti, M. (2018). Early season weed mapping in rice crops using multi-spectral UAV data. International journal of remote sensing, 39(15-16), 5432-5452.
  • Thenkabail, P. S., Lyon, J. G., & Huete, A. (2018). Advanced Applications in Remote Sensing of Agricultural Crops and Natural Vegetation. CRC Press.
  • Tripicchio, P., Satler, M., Dabisias, G., Ruffaldi, E., & Avizzano, C. A. (2015, July). Towards smart farming and sustainable agriculture with drones. In 2015 International Conference on Intelligent Environments (pp. 140-143). IEEE.
  • Trout, T. J., & DeJonge, K. C. (2017). Water productivity of maize in the US high plains. Irrigation Science, 35(3), 251-266.
  • Tucker, C. J. (1979). Red and photographic infrared linear combinations for monitoring vegetation. Remote sensing of Environment, 8(2), 127-150.
  • Turner, D., Lucieer, A., Malenovský, Z., King, D., & Robinson, S. (2014). Spatial co-registration of ultra-high resolution visible, multispectral and thermal images acquired with a micro-UAV over Antarctic moss beds. Remote Sensing, 6(5), 4003-4024.
  • Vanegas, F., Bratanov, D., Powell, K., Weiss, J., & Gonzalez, F. (2018). A novel methodology for improving plant pest surveillance in vineyards and crops using UAV-based hyperspectral and spatial data. Sensors, 18(1), 260.
  • Wahab, I., Hall, O., & Jirström, M. (2018). Remote Sensing of Yields: Application of UAV Imagery-Derived NDVI for Estimating Maize Vigor and Yields in Complex Farming Systems in Sub-Saharan Africa. Drones, 2(3), 28.
  • Wolfert, S., Ge, L., Verdouw, C., & Bogaardt, M. J. (2017). Big data in smart farming–a review. Agricultural Systems, 153, 69-80.
  • Wójtowicz, M., Wójtowicz, A., & Piekarczyk, J. (2016). Application of remote sensing methods in agriculture. Communications in Biometry and Crop Science, 11(1), 31-50.
  • Wu, J., Oueslati, W., & Li, M. (2019). Policy Options for Efficient Agricultural Land Management. Global Challenges For Future Food And Agricultural Policies, 1, 153.
  • Xiongkui, H., Bonds, J., Herbst, A., & Langenakens, J. (2017). Recent development of unmanned aerial vehicle for plant protection in East Asia. International Journal of Agricultural and Biological Engineering, 10(3), 18-30.
  • Zhang, C., & Kovacs, J. M. (2012). The application of small unmanned aerial systems for precision agriculture: a review. Precision agriculture, 13(6), 693-712.
Toplam 75 adet kaynakça vardır.

Ayrıntılar

Birincil Dil Türkçe
Konular Mühendislik
Bölüm Araştırma Makaleleri
Yazarlar

Sinan Demir 0000-0002-1119-1186

Levent Başayiğit 0000-0003-2431-5763

Yayımlanma Tarihi 25 Haziran 2020
Yayımlandığı Sayı Yıl 2020 Cilt: 2 Sayı: 1

Kaynak Göster

APA Demir, S., & Başayiğit, L. (2020). Sorunlu Gelişim Gösteren Bitkilerin İnsansız Hava Araçları (İHA) ile Belirlenmesi. Türk Bilim Ve Mühendislik Dergisi, 2(1), 12-22.
 Kapak Resmi İndir