420159

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

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.

Anahtar Kelimeler:

Drone, Tarım arazisi, Programlama, Video işleme

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

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,

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