The Technology Uses in the Determination of Sugar Beet Diseases (Sugar Beet Cultivation, Management and Processing)
Yazarlar (2)
Tokat Gaziosmanpaşa Üniversitesi, Türkiye
Prof. Dr. Yusuf YANAR
Tokat Gaziosmanpaşa Üniversitesi, Türkiye
| Kitap Adı | Sugar Beet Cultivation, Management and Processing |
| Bölüm(ler) | The Technology Uses in the Determination of Sugar Beet Diseases |
| Bölüm Sayfaları | 621-642 |
| 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 | 10.1007/978-981-19-2730-0_30 |
| ISBN | 978-981-19-2729-4 |
| Basıldığı Ülke | Almanya |
| Basıldığı Şehir | |
| Kitap Linki | https://link.springer.com/book/10.1007/978-981-19-2730-0 |
| Özet |
| Early detection of plant disease and pest attack that cause substantial yield and economic losses in agricultural production and taking the necessary precautions on time make a great contribution to the reduction of product loss. Therefore, it is necessary to determine the outbreak, severity, and progress of the disease and pest in a timely and accurate manner. There is a need for faster and practical innovative methods that reduce human errors in the identification of plant diseases, disease severity, and progress of the disease, especially in wide production areas. Agricultural applications of drones have increased significantly in recent years because of their greater availability and the miniaturization of hardware such as GPS, sensors, cameras, inertial measurement units, etc. Drones mounted with camera are a cost-effective option for capturing images covering areas with disease and pest. However, visual inspection of such images can be a challenging and biased task, specifically for diseases and pests detecting. Image processing and deep learning methods have been used extensively for automatic determination and recognition of plant leaf diseases. In the present study, drones, equipments, and multispectral, hyperspectral, thermal, and RGB cameras used for the diagnosis of sugar beet diseases and image processing and deep learning techniques, and possible future of technological developments are discussed. The technological, economic, and vital effects of using these methods on human life and the environment are discussed. |
| Anahtar Kelimeler |
| Beet Necrotic Yellow Vein Virus | Deep learning | Disease detection | Drones | Image processing | Leaf spot disease | Powdery mildew | Sugar beet |
BM Sürdürülebilir Kalkınma Amaçları
| Atıf Sayıları | |
| SCOPUS | 10 |