The Software Design Overview by Processing The Recording From Bird's-Eye View Images to Determine The Crop Detection and Functionality of The Various Land Types

Year 2023, Volume: 13 Issue: 3, 1696 - 1702, 01.09.2023

Eray Yildirim , İsrafil Şabikoğlu

https://doi.org/10.21597/jist.1134720

Abstract

To meet the demand for food, humanity has begun to use the latest technologies such as artificial intelligence, the internet of things, and drones, in addition to advanced tractors, seeds, and crop planting methods. Precision agriculture has been achieved by using the latest technology in this field, especially in recent years, the use of drones for agricultural land spraying has gained great interest. In this study, a Python programming language was used to process the video footage of a certain height taken from agricultural land with the help of a drone, using individual photo frames obtained from the footage. Each pixel was separated into different color contrast values, and certain colors were distinguished by counting. The proportional distribution of different types of surfaces on the land was determined. The software enabled the determination of various geophysical properties such as the productivity of crops, crop development status, and identification of areas where the crop does not grow.

Keywords

Drone, Farmland, Programming, Video processing

Çeşitli Arazi Türlerinin, Yetişmiş Ürün Tespiti ve İşlevselliğinin Belirlenmesi İçin, Kuşbakışı Görüntülerden Alınan Kaydın İşlenerek Yazılım Tasarımına Genel Bakış

Abstract

İnsanoğlunun gıda talebini karşılamak için, gelişmiş traktörler, tohumlar, ürün ekme yöntemleri kullanıldığı gibi, yapay zekâ, nesnelerin interneti, drone gibi son teknolojiler de kullanılmaya başlamıştır. Tarım arazilerinde Hassas Tarım uygulaması yapılarak en son teknolojinin bu alanda kullanılması sağlanmıştır. Özellikle son yıllarda drone ile tarım arazilerinin ilaçlanması büyük ilgi görmektedir. Bu çalışmada, bir drone yardımı ile belli yükseklikten alınan tarım arazisinin video görüntülerinden elde edilen fotoğraf karesi kullanılarak, Python yazılım dili ile fotoğrafın piksel piksel işlenmesi sağlanmıştır. Her piksel farklı renk contrast değerlerine ayrılarak belli renklerin ayrıştırılması gerçekleştirilmiştir. Ayrıştırılan bu renkler daha sonra sayılarak arazi üzerinde bulunan farklı tip yüzeyler oransal olarak belirlenmiştir. Yazılım ile arazi üzerindeki ekinlerin verimliliği, ürün gelişim durumu, ürün çıkmayan alanların tanımlanması gibi çeşitli jeofiziksel özelliklerin belirlenmesi sağlanmıştır.

Keywords

Drone, Tarım arazisi, Programlama, Video işleme

References

• Abdalla, W.K. (2020). Analyzing and Improving Image Processing Techniques via Uav and Satellite Images in Monitoring Precision Agriculture.(MoS), Gebze Teknik Üniversitesi, Fen Bilimleri Enstitüsü, Gebze, Türkiye.
• Abdullahi, H.S., Mahieddinie, F., & Sheriff, R.E. (2015). Technology impact on agricultural productivity: A review of precision agriculture using unmanned aerial vehicles. 7th Int. Conf. on Wireless and Satellite Systems, Bradford, UK.
• Ahmed, T., Garg, A., Murugan, D., & Singh, D. (2016). Fusion of Drone and Satellite Data for Precision Agriculture Monitoring. 11th International Conference on Industrial and Information Systems, Roorkee, India.
• Bolca, M., & Özen, F. (2012). A research of a suitable method on mapping olive tree fields with high resolution satellite images. Ege Üniv. Ziraat Fak. Derg. 49 (1):63-70.
• Capolupo, A. (2016). The Application of UAV and Photogrammetry for Supporting Precision Agriculture and Monitoring Environmental Problems. (PhD), Tuscia Universty, Viterbo, Italy.
• Dakkak-Arnoux, L., Pedersen, B., & Probst, L. (2018). Digital Transformation Monitor Drones in agriculture. Technical Report NO: EASME/COSME/2014/004. European Commission.
• Efford, N. (2000). Digital image processing: a practical introduction using Java, Addison-Wesley Longman Publishing Co., Inc. USA.
• Ghasab, M.A.J., Khamis, S., Mohammad, F., & Fariman, HJ. (2015). Feature decision-making ant colony optimization system for an automated recognition of plant species, Expert Systems with Applications, 42(5):2361–2370.
• Koyuncu, E., & Inalhan, G. (2008). A probabilistic B-spline motion planning algorithm for unmanned helicopters flying in dense 3D environments. In 2008 IEEE/RSJ International Conference on Intelligent Robots and Systems, 815-821.
• Marinello, F., Pezzuolo, A., Chiumenti, A., & Sartori, L. (2016). Technical Analysis of Unamnned Aerial Vehicles (Drones) for Agricultural Applications, 15th International Scientific Conference, Engineerinf for Rural Development, Jelgava, Latvia.
• McKinnon, T., & Hoff, P. (2017). Comparing RGB-Based Vegetation Indices with NDVI For Drone Based Agricultural Sensing. AGBX021-17.
• Noor, N.M., Abdullah, A., & Hashim, M. (2018). Remote sensing UAV/drones and its applications for urban areas: a review. In IOP Conference Series: Earth and Environmental Science, 169:1, 012003.
• Öztürk, M. (2018). Makine Öğrenmesi ve Görüntü İşleme Tekniklerini Kullanarak Drone ile Yaprak Sınıflandırma. (MoS), İstanbul Teknik Üniversitesi, Fen Bilimleri Enstitüsü, İstanbul, Türkiye.
• Prasad, S., Kumar, P., & Tripathi, R. (2011). Plant leaf species identification using curvelet transform, Computer and Communication Technology (ICCCT) 2nd International Conference on IEEE, 646–652.
• Sylvester, G. (2018). An eye in the sky for agriculture: the drone revolution, E-Agriculture in Action: Drones for Agriculture, International Telecommunication Union Publication, ICTs, Switzerland.
• Uysal, M., Toprak, A.S., & Polat, N. (2013). Photo realistic 3d modeling with UAV: Gedik Ahmet Pasha mosque in Afyonkarahisar. International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 5, W2.