A novel multi-head CNN design to identify plant diseases using the fusion of RGB images

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Tarih

2023

Dergi Başlığı

Dergi ISSN

Cilt Başlığı

Yayıncı

Elsevier

Erişim Hakkı

info:eu-repo/semantics/closedAccess

Özet

Plant diseases and insect pests cause a significant threat to agricultural production. Early detection and diagnosis of these diseases are critical and can reduce economic losses. The recent development of deep learning (DL) benefits various fields, such as image processing, remote sensing, medical diagnosis, and agriculture. This work proposed a novel approach based on DL for plant disease detection by fusing RGB and segmented images. A multi-headed DenseNet-based architecture was developed, considering two images as input. We evaluated the model on a public dataset, PlantVillage, consisting of 54183 images with 38 classes. The fivefold cross-validation technique achieved an average accuracy, recall, precision, and f1-score of 98.17%, 98.17%, 98.16%, and 98.12%, respectively. The proposed approach can distinguish various plant diseases with different characteristics by image fusion. The high success rate with low standard deviation proves the robustness of the model, and the model can be integrated into plant disease detection and early warning system.

Açıklama

Anahtar Kelimeler

Plant disease detection, Fusion CNN, Deep learning, DenseNet

Kaynak

Ecological Informatics

WoS Q Değeri

Q1

Scopus Q Değeri

Q1

Cilt

75

Sayı

Künye