Maturity Classification of Dragon Fruits Using Deep Learning Methods (AGRI-INTELLIGENCE)
Yazarlar (3)
Bünyamin Gençtürk
Dr. Öğr. Üyesi Elham Tahsın Yasın YASIN Sinop Üniversitesi, Türkiye
Doç. Dr. Murat Köklü Selçuk Üniversitesi, Türkiye
| Bölüm Adı | Maturity Classification of Dragon Fruits Using Deep Learning Methods | ||
| Kitap Adı | AGRI-INTELLIGENCE | ||
| Bölüm Sayfaları | 182-202 | ||
| Kitap Türü | Kitap Bölümü | ||
| Kitap Alt Türü | Alanında ulusal yayınlanan kitap bölümü | ||
| Kitap Niteliği | Alanında tanınmış ulusal bir yayınevince basılan bilimsel kitap | ||
| Kitap Dili | İngilizce | Basım Tarihi | 01-2024 |
| DOI Numarası | – | ISBN | 978-625-396-413-9 |
| Basıldığı Ülke | Türkiye | Basıldığı Şehir | Konya |
| Kitap Linki | https://www.cizgikitabevi.com/kitap/1954-agri-intelligence-artificial-intelligence-reflections-in-agriculture | ||
| UAK Araştırma Alanları |
Bilgi Güvenliği ve Kriptoloji
Görüntü İşleme
Yapay Zeka
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| Özet |
| One of the most promising approaches for detecting fruit maturity is achieved via the use of deep learning models. These models have demonstrated outstanding effectiveness in image classification problems (Gencturk et al., 2023; Tutuncu et al., 2022), making them a suitable choice for analyzing the maturity of dragon fruit based on its appearance. Pitaya, or dragon fruit, is a widely consumed fruit that comes in different varieties and colors (Khatun et al., 2024), The tropical fruit, with its stunning appearance and delicious flavor, has gained popularity among consumers due to its unique appearance and nutritional benefits. To meet the increasing demand for dragon fruit, it is essential to develop efficient methods for detecting the maturity of the fruit (Kamilaris & Prenafeta-Boldú, 2018). By obtaining a dragon fruit image dataset and training deep learning models on this data, we can develop a reliable system for automatically assessing the maturity of dragon fruit. This could have significant implications for the agricultural industry, enabling farmers and distributors to better manage their inventory and ensure quality control (Kamilaris & Prenafeta-Boldú, 2018; Khatun et al., 2024). The intention of this research is to figure out how to obtain a dragon fruit image dataset from the Mendeley Data website and demonstrate deep learning models' efficiency in categorizing the fruit's maturity. Our objective is to encourage the growth of automated systems by revolutionizing how we assess and manage fruit maturity in agriculture. Pre-trained deep learning models provide a practical and efficient solution for real-time maturity |
| Anahtar Kelimeler |
| Classification | Fruits | Deep Learning Methods |
