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：

TP39 [计算机的应用];

学科分类号：

081203 ; 0835 ;

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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