Akıllı Tarım Uygulamaları için Histogram ve Makine Öğrenimi Kullanan Bitki Sınıflandırma Yöntemi

Year 2023, Volume: 7 Issue: 1, 17 - 28, 02.01.2024

Orhan Yaman , Türker Tuncer

https://doi.org/10.26650/acin.1070261

Abstract

Nesnelerin interneti (IoT) insanlık için çok değerli bir teknolojidir, dolayısıyla IoT bilgi güvenliği, endüstri 4.0, akıllı tarım gibi çeşitli alanlarda kullanılmaya başlanmıştır. Akıllı tarım uygulamaları sensörler, insansız hava araçları (İHA), uydu teknolojileri, robotlar, görüntü işleme ve yapay zekâ teknolojileri kullanılarak geliştirilmektedir. Akıllı tarım uygulamaları ile sulama alanında tasarruf sağlanmakta ve üretim sırasında çevre kirliliği azaltılmaktadır. Aynı zamanda üretimi ve kaliteyi arttırır. Bu çalışmada, akıllı tarım uygulamaları için ultra hafif otomatik bitki türleri sınıflandırma yöntemi geliştirilmiştir. Bir İHA kullanılarak yeni bir görüntü veri seti elde edilmiştir. Elde edilen bitki türleri görüntüsünü sınıflandırmak için ultra hafif bir sınıflandırma yöntemi önerilmiştir. Önerilen ultra hafif bilgisayarlı görü modelimizde, histogram tabanlı basit bir özellik çıkarma işlevi sunulmuştur. Sunulan öznitelik çıkarıcı, histogram çıkarımı ve medyan filtresi birlikte kullanılmıştır. Oluşturulan öznitelikler, destek vektör makinesi (SVM) ve k en yakın komşu (KNN) olan iki sığ sınıflandırıcıya beslenir. Kullanılan SVM ve KNN sınıflandırıcıları arka arkaya %96,45 ve %94,11 doğruluk elde etmiştir. Sonuçlar, bu modelin bitki görüntü sınıflandırması için oldukça başarılı olduğunu ve fiziksel tarım ortamında kullanıma hazır olduğunu göstermektedir.

Keywords

Bitki sınıflandırması, Akıllı tarım, Histogram, Özellik çıkarma, Makine öğrenmesi

Plant Classification Method Using Histogram and Machine Learning for Smart Agriculture Applications

Abstract

Due to its high potential and value, the Internet of things (IoT) has been used in various areas such as information security, industry 4.0, and smart agriculture. IoT is used in agriculture through the use of sensors, unmanned aerial vehicles (UAV), satellite technologies, robots, image processing, and artificial intelligence technologies. These smart agricultural practices increase production and quality and lead to savings in irrigation, thereby reducing environmental pollution during production. This study proposes an ultra-lightweight automated plant species classification method for smart agriculture applications. A UAV is used to acquire a new image dataset. An ultra-lightweight classification method is then used to classify the acquired plant species images. Our proposed ultra-lightweight computer vision model presents a histogram-based simple feature extraction function. The presented feature extractor uses histogram extraction and median filter in conjunction. The generated features are fed to two shallow classifiers, which are the support vector machine (SVM), and k nearest neighbor (KNN). The utilized SVM and KNN classifiers have attained 96.45% and 94.11% accuracies consecutively. The results demonstrate that this model is very capable of plant image classification and is ready for use in a physical agriculture environment.

Keywords

Plant classification, Smart agriculture, Histogram, Feature extraction, Machine learning

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