Plant Leaf Disease Detection Using Transfer learning

被引：0

作者：

Damak, Taheni ^{[1
]}

Marzougui, Sahar ^{[2
]}

Ben Ayed, Mohamed Ali ^{[1
]}

Masmoudi, Nouri ^{[3
]}

机构：

[1] Sfax Univ, New Technol & Telecommun Syst NTSCom, Sfax, Tunisia

[2] Sfax Univ, Natl Sch Elect & Telecommun ENETcom, Sfax, Tunisia

[3] Sfax Univ, Lab Elect & Informat Technol, Sfax, Tunisia

来源：

2024 IEEE 7TH INTERNATIONAL CONFERENCE ON ADVANCED TECHNOLOGIES, SIGNAL AND IMAGE PROCESSING, ATSIP 2024 | 2024年

关键词：

detect plant leaf diseases; Deep Learning; EfficientNetB3; DenseNet; ResNet50; MobileNet; Xception;

D O I：

10.1109/ATSIP62566.2024.10638884

中图分类号：

学科分类号：

摘要：

This paper aims to address the challenges of modern agriculture, in particular the prevalence of plant diseases. With the world's population expected to reach up to 9.7 billion by 2050[1], the demand for food continues to grow, requiring the production of more food with fewer resources. This study presents a comparison among variety of Deep Learning models to automatically detect plant leaf diseases. The experimentation results show that EfficientNetB3 was the best fit for this purpose, achieving 99.92% as accuracy. These results highlight Deep Learning's potential to overcome modern agriculture's challenges, paving the way for more sustainable and efficient food production.

引用

页码：234 / 238

页数：5

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