Deep-Plant-Disease Dataset Is All You Need for Plant Disease Identification

Deep learning models have emerged as a promising alternative to conventional approaches for plant disease identification, a critical challenge in agricultural production. However, the existing plant disease datasets are insufficient to address the complexities of real-world agricultural scenarios, such as multi crop disease, unseen, few-shot, and domain shift adaptation. Additionally, the lack of standardized evaluation protocols and benchmark datasets hinders the fair evaluation of models against these challenges. To bridge this gap, we introduce Deep-Plant-Disease, the largest and most diverse dataset with novel text data designed to enhance model generalization in multi crop disease identification. We revisit and reformulate the task by establishing a standardized evaluation framework that supports consistent benchmarking and guides future research. Through experiments, we further validate the robustness and adaptability of models trained on our dataset, highlighting their effective transferability to real-world agricultural challenges.

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Published
2025-10-25
DOI
https://doi.org/10.1145/3746027.3758192
Primary Topic
Plant Disease Management Techniques
Type
article
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article

Deep-Plant-Disease Dataset Is All You Need for Plant Disease Identification

Alexis Joly, Jules Vandeputte, Sue Han Lee, Fei Siang Tay et al.
Plant Disease Management Techniques
article

Deep-Plant-Disease Dataset Is All You Need for Plant Disease Identification

Alexis Joly, Jules Vandeputte, Sue Han Lee, Fei Siang Tay, Abel Yu Hao Chai, Hervé Goëau, Pierre Bonnet, Kelly Li Zhen Jee
article en

Abstract

Deep learning models have emerged as a promising alternative to conventional approaches for plant disease identification, a critical challenge in agricultural production. However, the existing plant disease datasets are insufficient to address the complexities of real-world agricultural scenarios, such as multi crop disease, unseen, few-shot, and domain shift adaptation. Additionally, the lack of standardized evaluation protocols and benchmark datasets hinders the fair evaluation of models against these challenges. To bridge this gap, we introduce Deep-Plant-Disease, the largest and most diverse dataset with novel text data designed to enhance model generalization in multi crop disease identification. We revisit and reformulate the task by establishing a standardized evaluation framework that supports consistent benchmarking and guides future research. Through experiments, we further validate the robustness and adaptability of models trained on our dataset, highlighting their effective transferability to real-world agricultural challenges.

Centre National de la Recherche Scientifique (FR), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (FR), Institut national de recherche en sciences et technologies du numérique (FR), Université de Montpellier (FR), Institut Agro Montpellier (FR), Institut National de Recherche pour l'Agriculture, l'Alimentation et l'Environnement (FR), UMR Botanique et Modélisation de l’Architecture des Plantes et des végétations (FR), Centre de Biologie pour la Gestion des Populations (FR), Institut de Recherche pour le Développement (FR), Swinburne University of Technology Sarawak Campus (MY)
Openalex Percentile: Top 77%
Plant Disease Management Techniques
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