An Efficient Deep Learning Technique for Detecting and Classifying the Growth of Weeds on Fields (Proceedings of the Future Technologies Conference)
Yazarlar (4)
College Of Engineering, Computing, Technology, And Mathematics, Amerika Birleşik Devletleri
College Of Engineering, Computing, Technology, And Mathematics, Amerika Birleşik Devletleri
Dr. Öğr. Üyesi Mustafa GÜZEL
Tokat Gaziosmanpaşa Üniversitesi, Türkiye
Tokat Gaziosmanpaşa Üniversitesi, Türkiye
| Kitap Adı | Proceedings of the Future Technologies Conference |
| Bölüm(ler) | An Efficient Deep Learning Technique for Detecting and Classifying the Growth of Weeds on Fields |
| Bölüm Sayfaları | 818-835 |
| Kitap Türü | Kitap Bölümü |
| Kitap Alt Türü | Alanında uluslararası yayınlanan kitap bölümü |
| Kitap Niteliği | Scopus indeksinde taranan bilimsel kitap |
| Kitap Dili | İngilizce |
| Basım Tarihi | 01-2022 |
| DOI Numarası | 10.1007/978-3-031-18458-1_56 |
| ISBN | 978-3-031-18457-4 |
| Basıldığı Ülke | Kanada |
| Basıldığı Şehir |
| Özet |
| The agriculture cycle needs to be expanded in the next decades to meet the demand of the world population. Weeds are one of the main challenges that severely affect the agricultural production and its quality. An accurate, automatic, low cost, little environmental impacts and real-time weeds detection technique is required to control weeds effectively on fields. In addition, automating the classification process of weeds based on their growth stages is crucial for using appropriate weeds-controlling techniques. In this paper, we fly a drone to collect a dataset of four different weed (Consolida Regalis) growth stages. As well, we developed and trained deep learning object detector (YOLOv5) to detect weed (Consolida Regalis) and to classify its four growth stages in real-time with a sufficient accuracy. The results show that the generated YOLOv5 small model succeeds to detect and classify the weed’s growth stages in real-time with highest recall 0.794 at 156 FPS. However, YOLOv5 large model depicts efficient detection and classification precision of 0.827 at 70 FPS. |
| Anahtar Kelimeler |
| Deep learning | Weeds detection | Weeds growth stage detection | YOLOv5 |
BM Sürdürülebilir Kalkınma Amaçları
| Atıf Sayıları | |
| SCOPUS | 5 |