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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

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.

Kaynakça

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  • 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.
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  • 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.
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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

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.

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.
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Ayrıntılar

Birincil Dil Türkçe
Konular Mühendislik
Bölüm Araştırma Makaleleri
Yazarlar

Sinan DEMİR> (Sorumlu Yazar)
ısparta uygulamalı bilimler üniversitesi
0000-0002-1119-1186
Türkiye


Levent BAŞAYİĞİT>
ısparta uygulamalı bilimler üniversitesi
0000-0003-2431-5763
Türkiye

Yayımlanma Tarihi 25 Haziran 2020
Yayınlandığı 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 . Retrieved from https://dergipark.org.tr/tr/pub/tjse/issue/55246/668300