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Dergiler / Fırat Üniversitesi Mühendislik Bilimleri Dergisi / 2026 / Cilt: 38 Sayı: 1

Derin Öğrenme ve Çeşitli Işık Spektrumları Kullanılarak Domates Bitkisi Verimliliğinin Artırılması

Enhancing Tomato Plant Productivity Using Deep Learning and Varied Light Spectra

Abdil Karakan(AFYON KOCATEPE UNIVERSITY)0000-0003-1651-7568
Yüksel Oğuz(AFYON KOCATEPE UNIVERSITY)0000-0002-5233-151X
Selami Kesler(PAMUKKALE UNIVERSITY)0000-0002-7027-1426
Sayfa
267–284
Yayın Tarihi
2026-03-29
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Yapay Zeka Hızlı Bakış & Çıkarımlar

AI Insight
⚡ TL;DR(1 Cümlede Hızlı Çıkarım)

Domates bitkisinin verimliliği, fide, çiçeklenme ve ürün aşamalarında farklı ışık dalga boylarına göre değişiklik göstermektedir.

📌 3 Maddede Bu Makale

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Amaç & Hipotez:Dünya nüfusu her geçen gün artmakta, buna karşın tarım alanları azalmaktadır.
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Temel Bulgular & Veri:Bu nedenle tarımsal ürünlerde verimliliğin artırılması büyük önem taşımaktadır.
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Sonuç & Katkı:Domates bitkisinin verimliliği, fide, çiçeklenme ve ürün aşamalarında farklı ışık dalga boylarına göre değişiklik göstermektedir.
🔬Tür:Araştırma Makalesi
📍Kapsam:Genişletilmiş Akademik Örneklem
⚙️Değişkenler:Derin öğrenme · VGGNet · verimlilik

Özet

Dünya nüfusu her geçen gün artmakta, buna karşın tarım alanları azalmaktadır. Bu nedenle tarımsal ürünlerde verimliliğin artırılması büyük önem taşımaktadır. Işık, bitki büyümesi ve gelişimini önemli ölçüde etkileyen kritik bir faktördür. Bitkiler, cins ve türlerine bağlı olarak farklı ışık yoğunluklarına uyum sağlamıştır. Işık yoğunluğu değiştiğinde bitkilerin büyüme, gelişme ve üreme fonksiyonları etkilenmektedir. Domates bitkisinin verimliliği, fide, çiçeklenme ve ürün aşamalarında farklı ışık dalga boylarına göre değişiklik göstermektedir. Bu çalışma, derin öğrenme ve farklı ışık dalga boylarını kullanarak domates bitkilerinin verimliliğini artırmayı amaçlamaktadır. Domates bitkisinin fide, çiçeklenme ve ürün aşamalarını belirlemek amacıyla bir sınıflandırma çalışması yapılmıştır. Sınıflandırma, derin öğrenmede kullanılan dört farklı mimari ile gerçekleştirilmiştir. En yüksek doğruluk oranı %99,85 ile VGGNet mimarisinde elde edilmiştir. Geliştirilen gerçek zamanlı sistem ile domates bitkisinin ışık dalga boyları, derin öğrenme sonuçlarına göre otomatik olarak ayarlanmıştır. Araştırma sonucunda, domates bitkilerinde %4,10 oranında daha erken ürün elde edilmiş ve verimde %9,85 artış sağlanmıştır. Elde edilen bulgulara göre, derin öğrenme ve farklı ışık yoğunluklarının kullanımı domates bitkilerinin verimliliğini etkili bir şekilde artırmıştır.

Abstract

The world population is increasing daily, while agricultural lands are decreasing. Therefore, increasing productivity in agricultural products is crucial. Light is a critical factor that significantly affects plant growth and development. Plants have adapted to different light intensities depending on their genus and species. When the light intensity changes, the growth, development, and reproductive functions of these plants are affected. The productivity of tomato plants varies with different light wavelengths during the seedling, flowering, and crop stages. This study aims to increase the productivity of tomato plants by using deep learning and different light wavelengths. A classification study was conducted to determine the seedling, flowering, and yield stages of the tomato plant. The classification was performed using four different deep learning architectures. The highest accuracy rate of 99.85% was achieved with the VGGNet architecture. With the real-time system developed, the light wavelengths for the tomato plant were automatically adjusted according to the deep learning results. As a result of the research, tomato plants yielded crops 4.10% earlier and achieved a 9.85% increase in yield. According to the findings, the use of deep learning and different light intensities effectively improved the productivity of tomato plants.

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Bu Makaleye Atıf Yap

Karakan, A., Oğuz, Y., Kesler, S. (2026). Derin Öğrenme ve Çeşitli Işık Spektrumları Kullanılarak Domates Bitkisi Verimliliğinin Artırılması. Fırat Üniversitesi Mühendislik Bilimleri Dergisi, Cilt: 38 Sayı: 1, 267–284. https://doi.org/10.35234/fumbd.1799130

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