EnConv: enhanced CNN for leaf disease classification
Yükleniyor...
Dosyalar
Tarih
2025
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Springer science and business media deutschland GmbH
Erişim Hakkı
info:eu-repo/semantics/openAccess
Özet
Detecting leaf diseases in plants is essential to maintain crop yield and market value. Machine learning has shown promise in detecting these diseases as it can group data into predetermined categories after examining it from various angles. However, machine learning models require a thorough knowledge of plant diseases, and processing time can be lengthy. This study proposes an enhanced convolutional neural network that utilizes depthwise separable convolution and inverted residual blocks to detect leaf diseases in plants. The model considers the morphological properties and characteristics of the plant leaves, including color, intensity, and size, to categorize the data. The proposed model outperforms traditional machine learning approaches and deep learning models, achieving an accuracy of 99.87% for 39 classes of different plants such as tomato, corn, apple, potato, and more. To further improve the model, global average pooling was used in place of the flatten layer. Overall, this study presents a promising approach to detect leaf diseases in plants using an enhanced convolutional neural network with depthwise separable convolution and inverted residual blocks. The results show the potential benefits of using this model in agriculture to improve the early detection of plant diseases and maintain crop yield and market value.
Açıklama
Anahtar Kelimeler
Augmentation, Depthwise, Depthwise Separable Convolutional Neural Network (DS-CNN), Disease, ECNN, Leaf, Max-Pool, Soft-Max
Kaynak
Journal of plant diseases and protection
WoS Q Değeri
Q2
Scopus Q Değeri
Q1
Cilt
132
Sayı
1
Künye
Thanjaivadivel, M., Gobinath, C., Vellingiri, J., Kaliraj, S., & Femilda Josephin, J. S. (2025). EnConv: enhanced CNN for leaf disease classification. Journal of Plant Diseases and Protection, 132(1), 1-12.
