short-paper

Paddy Doctor: A Visual Image Dataset for Automated Paddy Disease Classification and Benchmarking

Authors:

Briskline Kiruba,

Pandarasamy ArjunanAuthors Info & Claims

CODS-COMAD '23: Proceedings of the 6th Joint International Conference on Data Science & Management of Data (10th ACM IKDD CODS and 28th COMAD)

Pages 203 - 207

https://doi.org/10.1145/3570991.3570994

Published: 04 January 2023 Publication History

CODS-COMAD '23: Proceedings of the 6th Joint International Conference on Data Science & Management of Data (10th ACM IKDD CODS and 28th COMAD)

Paddy Doctor: A Visual Image Dataset for Automated Paddy Disease Classification and Benchmarking

Pages 203 - 207

Abstract
References

Abstract

One of the critical biotic stress factors paddy farmers face is diseases caused by bacteria, fungi, and other organisms. These diseases affect plants’ health severely and lead to significant crop loss. Most of these diseases can be identified by regularly observing the leaves and stems under expert supervision. In a country with vast agricultural regions and limited crop protection experts, manual identification of paddy diseases is challenging. Thus, to add a solution to this problem, it is necessary to automate the disease identification process and provide easily accessible decision support tools to enable effective crop protection measures. However, the lack of availability of public datasets with detailed disease information limits the practical implementation of accurate disease detection systems. This paper presents Paddy Doctor, a visual image dataset for identifying paddy diseases. Our dataset contains 16,225 annotated paddy leaf images across 13 classes (12 diseases and normal leaf). We benchmarked the Paddy Doctor dataset using a Convolutional Neural Network (CNN) and four transfer learning based models (VGG16, MobileNet, Xception, and ResNet34). The experimental results showed that ResNet34 achieved the highest F1-score of 97.50%. We release our dataset and reproducible code in the open source for community use.

References

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Cited By

A PMurugan D(2025)Automated Paddy Leaf Disease Identification using Visual Leaf Images based on Nine Pre-trained Models ApproachProcedia Computer Science10.1016/j.procs.2024.12.013252(118-126)Online publication date: 2025
https://doi.org/10.1016/j.procs.2024.12.013
Tiwari RVora N(2024)Enhancing Paddy Leaf Disease Classification using CNN and MobileNetV2Journal of Soft Computing Paradigm10.36548/jscp.2024.3.0086:3(324-340)Online publication date: Sep-2024
https://doi.org/10.36548/jscp.2024.3.008
Bera ABhattacharjee DKrejcar O(2024)An attention-based deep network for plant disease classificationMachine Graphics and Vision10.22630/MGV.2024.33.1.333:1(47-67)Online publication date: 12-Nov-2024
https://doi.org/10.22630/MGV.2024.33.1.3
Show More Cited By

Index Terms

Paddy Doctor: A Visual Image Dataset for Automated Paddy Disease Classification and Benchmarking
1. Applied computing
  1. Computers in other domains
    1. Agriculture
2. Computing methodologies
  1. Artificial intelligence
    1. Computer vision

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CODS-COMAD '23: Proceedings of the 6th Joint International Conference on Data Science & Management of Data (10th ACM IKDD CODS and 28th COMAD)

January 2023

357 pages

ISBN:9781450397971

DOI:10.1145/3570991

Copyright © 2023 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 04 January 2023

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CODS-COMAD 2023

CODS-COMAD 2023: 6th Joint International Conference on Data Science & Management of Data (10th ACM IKDD CODS and 28th COMAD)

January 4 - 7, 2023

Mumbai, India

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Cited By

A PMurugan D(2025)Automated Paddy Leaf Disease Identification using Visual Leaf Images based on Nine Pre-trained Models ApproachProcedia Computer Science10.1016/j.procs.2024.12.013252(118-126)Online publication date: 2025
https://doi.org/10.1016/j.procs.2024.12.013
Tiwari RVora N(2024)Enhancing Paddy Leaf Disease Classification using CNN and MobileNetV2Journal of Soft Computing Paradigm10.36548/jscp.2024.3.0086:3(324-340)Online publication date: Sep-2024
https://doi.org/10.36548/jscp.2024.3.008
Bera ABhattacharjee DKrejcar O(2024)An attention-based deep network for plant disease classificationMachine Graphics and Vision10.22630/MGV.2024.33.1.333:1(47-67)Online publication date: 12-Nov-2024
https://doi.org/10.22630/MGV.2024.33.1.3
Jain SKumar R(2024)An Efficient Deep Learning Model for Detection of Rice Leaf Diseases using Paddy Doctor Dataset2024 First International Conference on Software, Systems and Information Technology (SSITCON)10.1109/SSITCON62437.2024.10797004(1-7)Online publication date: 18-Oct-2024
https://doi.org/10.1109/SSITCON62437.2024.10797004
B JT CA V(2024)Agricultural Advancement: Leveraging EfficientNetB4 for Enhanced Paddy Disease Classification2024 IEEE Students Conference on Engineering and Systems (SCES)10.1109/SCES61914.2024.10652384(1-6)Online publication date: 21-Jun-2024
https://doi.org/10.1109/SCES61914.2024.10652384
Uddin MJoolee J(2024)Fusion of Deep Learned and Handcrafted Features for Paddy Disease Recognition2024 International Conference on Artificial Intelligence, Metaverse and Cybersecurity (ICAMAC)10.1109/ICAMAC62387.2024.10829316(1-6)Online publication date: 25-Oct-2024
https://doi.org/10.1109/ICAMAC62387.2024.10829316
Aggarwal MKhullar VGoyal NProla T(2024)Resource-efficient federated learning over IoAT for rice leaf disease classificationComputers and Electronics in Agriculture10.1016/j.compag.2024.109001221:COnline publication date: 18-Jul-2024
https://dl.acm.org/doi/10.1016/j.compag.2024.109001
Quan THoa N(2024)Rice Leaf Diseases Classification Based on RiceNetFrom Smart City to Smart Factory for Sustainable Future: Conceptual Framework, Scenarios, and Multidiscipline Perspectives10.1007/978-3-031-65656-9_36(359-368)Online publication date: 31-Aug-2024
https://doi.org/10.1007/978-3-031-65656-9_36
ÖZDEMİR C(2023)Derin Öğrenme Modelleri ve Veri Ön İşleme Yöntemleri ile Çeltik Yaprak Hastalıklarının Erken TeşhisiEarly Diagnosis of Paddy Leaf Diseases using Deep Learning Models and Data Preprocessing TechniquesÇukurova Üniversitesi Mühendislik Fakültesi Dergisi10.21605/cukurovaumfd.137776338:3(807-817)Online publication date: 18-Oct-2023
https://doi.org/10.21605/cukurovaumfd.1377763
Niroj TPuvanendran R(2023)A Cross-Domain Hybrid Model for Paddy Disease Classification2023 5th International Conference on Advancements in Computing (ICAC)10.1109/ICAC60630.2023.10417161(697-702)Online publication date: 7-Dec-2023
https://doi.org/10.1109/ICAC60630.2023.10417161
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