Plant Leaf Disease Detection and Classification Based on CNN with LVQ Algorithm
Yazarlar (3)
Melike Sardoğan Yalova Üniversitesi, Türkiye
Doç. Dr. Adem TUNCER Yalova Üniversitesi, Türkiye
Dr. Öğr. Üyesi Yunus ÖZEN Yalova Üniversitesi, Türkiye
| Bildiri Türü | Tebliğ/Bildiri | Bildiri Dili | İngilizce |
| Bildiri Alt Türü | Tam Metin Olarak Yayınlanan Tebliğ (Uluslararası Kongre/Sempozyum) | ||
| Bildiri Niteliği | Alanında Hakemli Uluslararası Kongre/Sempozyum | ||
| DOI Numarası | 10.1109/UBMK.2018.8566635 | ||
| Kongre Adı | 3rd International Conference on Computer Science and Engineering UBMK’18 | ||
| Kongre Tarihi | 20-09-2018 / 23-09-2018 | ||
| Basıldığı Ülke | Bosna Hersek | Basıldığı Şehir | Saraybosna |
| Bildiri Linki | https://ieeexplore.ieee.org/document/8566635/ | ||
| Özet |
| The early detection of diseases is important in agriculture for an efficient crop yield. The bacterial spot, late blight, septoria leaf spot and yellow curved leaf diseases affect the crop quality of tomatoes. Automatic methods for classification of plant diseases also help taking action after detecting the symptoms of leaf diseases. This paper presents a Convolutional Neural Network (CNN) model and Learning Vector Quantization (LVQ) algorithm based method for tomato leaf disease detection and classification. The dataset contains 500 images of tomato leaves with four symptoms of diseases. We have modeled a CNN for automatic feature extraction and classification. Color information is actively used for plant leaf disease researches. In our model, the filters are applied to three channels based on RGB components. The LVQ has been fed with the output feature vector of convolution part for training the network. The … |
| Anahtar Kelimeler |
| Convolutional Neural Network (CNN) | Leaf Disease Classification | Leaf Disease Detection | Learning Vector Quantization(LVQ) |
BM Sürdürülebilir Kalkınma Amaçları
| Atıf Sayıları | |
| Google Scholar | 682 |
| Scopus | 466 |