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

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

[1]

Md. Sabbir Ahmed. 2020. UCI - Rice Leaf Diseases Data Set (augmented). https://www.kaggle.com/datasets/badhon7432/paddyleafdiseaseuci. [Online; accessed 2022-11-24].

[2]

Bifta Sama Bari, Md Nahidul Islam, Mamunur Rashid, Md Jahid Hasan, Mohd Azraai Mohd Razman, Rabiu Muazu Musa, Ahmad Fakhri Ab Nasir, and Anwar P.P. Abdul Majeed. 2021. A real-time approach of diagnosing rice leaf disease using deep learning-based faster R-CNN framework. PeerJ Computer Science 7 (April 2021), e432. https://doi.org/10.7717/peerj-cs.432

[3]

R Jeya Bharathi. 2020. Paddy Plant Disease Identification and Classification of Image Using AlexNet Model. Int. J. Anal. Exp. modal Anal. 12, 0886 (2020), 1094–1098.

[4]

Konstantinos P. Ferentinos. 2018. Deep learning models for plant disease detection and diagnosis. Computers and Electronics in Agriculture 145 (Feb. 2018), 311–318. https://doi.org/10.1016/j.compag.2018.01.009

Digital Library

[5]

Food and Agriculture Organization (FOA). 2022. India at a glance. https://www.fao.org/india/fao-in-india/india-at-a-glance/en/. [Online; accessed 2022-11-24].

[6]

Aldrin Kein G. Francisco. 2019. Rice-Disease-DataSet. https://github.com/aldrin233/RiceDiseases-DataSet. [Online; accessed 2022-11-24].

[7]

Amritha Haridasan, Jeena Thomas, and Ebin Deni Raj. 2023. Deep learning system for paddy plant disease detection and classification. Environmental Monitoring and Assessment 195, 1 (2023), 1–28.

[8]

B. Leelavathy and Ram Mohan Rao Kovvur. 2020. Prediction of Biotic Stress in Paddy Crop Using Deep Convolutional Neural Networks. In Proceedings of International Conference on Computational Intelligence and Data Engineering. Springer Singapore, 337–346. https://doi.org/10.1007/978-981-15-8767-2_29

[9]

Nizor Ogbezuode. 2022. Rice Leaf Images. https://www.kaggle.com/datasets/nizorogbezuode/rice-leaf-images. [Online; accessed 2022-11-24].

[10]

Rutuja R. Patil and Sumit Kumar. 2022. Rice-Fusion: A Multimodality Data Fusion Framework for Rice Disease Diagnosis. IEEE Access 10(2022), 5207–5222. https://doi.org/10.1109/access.2022.3140815

[11]

Harshadkumar B. Prajapati, Jitesh P. Shah, and Vipul K. Dabhi. 2017. Detection and classification of rice plant diseases. Intelligent Decision Technologies 11, 3 (Aug. 2017), 357–373. https://doi.org/10.3233/idt-170301

Digital Library

[12]

Rukhsar and Santosh Kumar Upadhyay. 2022. Rice Leaves Disease Detection and Classification Using Transfer Learning Technique. In 2022 2nd International Conference on Advance Computing and Innovative Technologies in Engineering (ICACITE). IEEE, 2151–2156. https://doi.org/10.1109/icacite53722.2022.9823596

[13]

G K Sagarika, SJ Krishna Prasad, and S Mohana Kumar. 2020. Paddy Plant Disease Classification and Prediction Using Convolutional Neural Network. In 2020 International Conference on Recent Trends on Electronics, Information, Communication & Technology (RTEICT). IEEE, 208–214. https://doi.org/10.1109/rteict49044.2020.9315634

[14]

Prabira Kumar Sethy, Nalini Kanta Barpanda, Amiya Kumar Rath, and Santi Kumari Behera. 2020. Deep feature based rice leaf disease identification using support vector machine. Computers and Electronics in Agriculture 175 (Aug. 2020), 105527. https://doi.org/10.1016/j.compag.2020.105527

[15]

Vimal K Shrivastava, Monoj K Pradhan, Sonajharia Minz, and Mahesh P Thakur. 2019. Rice plant disease classification using transfer learning of deep convolution neural network. International Archives of the Photogrammetry, Remote Sensing & Spatial Information Sciences XLII-3/W6 (July 2019), 631–635. https://doi.org/10.5194/isprs-archives-xlii-3-w6-631-2019

[16]

R Swathika, S Srinidhi., N Radha, and K Sowmya.2021. Disease Identification in paddy leaves using CNN based Deep Learning. In 2021 Third International Conference on Intelligent Communication Technologies and Virtual Mobile Networks (ICICV). IEEE, 1004–1008. https://doi.org/10.1109/icicv50876.2021.9388557

[17]

Ümit Atila, Murat Uçar, Kemal Akyol, and Emine Uçar. 2021. Plant leaf disease classification using EfficientNet deep learning model. Ecological Informatics 61 (March 2021), 101182. https://doi.org/10.1016/j.ecoinf.2020.101182

[18]

Ruchi Verma and Varun Singh. 2022. Leaf Disease Identification Using DenseNet. In Artificial Intelligence and Speech Technology. Springer International Publishing, 500–511. https://doi.org/10.1007/978-3-030-95711-7_42

[19]

Yibin Wang, Haifeng Wang, and Zhaohua Peng. 2021. Rice diseases detection and classification using attention based neural network and bayesian optimization. Expert Systems with Applications 178 (Sept. 2021), 114770. https://doi.org/10.1016/j.eswa.2021.114770

[20]

Salma Zakzouk, Mohamed Ehab, Silvana Atef, Retaj Yousri, Rania M Tawfik, and M. Saeed Darweesh. 2021. Rice Leaf Diseases Detector Based on AlexNet. In 9th International Japan-Africa Conference on Electronics, Communications, and Computations (JAC-ECC). IEEE, 170–174. https://doi.org/10.1109/jac-ecc54461.2021.9691435

Cited By

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
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
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

Recommendations

Deep learning based classification for paddy pests & diseases recognition
ICMAI '18: Proceedings of 2018 International Conference on Mathematics and Artificial Intelligence

Pests and diseases are a threat to paddy production, especially in Indonesia, but identification remains to be a challenge in massive scale and automatically. Increasing smartphone usage and deep learning advance create an opportunity to answer this ...
Mapping Paddy Rice Biomass Using ALOS/PALSAR Imagery
ETTANDGRS '08: Proceedings of the 2008 International Workshop on Education Technology and Training & 2008 International Workshop on Geoscience and Remote Sensing - Volume 02

Accurately estimating rice biomass and yield is important not only for rice cropping management, but also for quantitatively understanding carbon cycle in agricultural ecosystem. This study integrated a rice canopy scattering model with Genetic ...
Application of GOCI-derived vegetation index profiles to estimation of paddy rice yield using the GRAMI rice model

Feasibility study of the GOCI satellite for rice yield simulation with GRAMI model.Advantages of GOCI with high temporal resolution to simulate rice growth and development.Method to calculate rice phenology based on the BRDF-adjusted reflectance with ...

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

cover image ACM Other conferences

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]

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 04 January 2023

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Short-paper
Research
Refereed limited

Conference

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

Acceptance Rates

Overall Acceptance Rate 197 of 680 submissions, 29%

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

8
Total Citations
View Citations
192
Total Downloads

Downloads (Last 12 months)117
Downloads (Last 6 weeks)16

Reflects downloads up to 21 Nov 2024

Other Metrics

View Author Metrics

Citations

Cited By

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
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
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
Meng YXu MKim HYoon SJeong YPark D(2023)Known and unknown class recognition on plant species and diseasesComputers and Electronics in Agriculture10.1016/j.compag.2023.108408215(108408)Online publication date: Dec-2023
https://doi.org/10.1016/j.compag.2023.108408

View Options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

HTML Format

View this article in HTML Format.

Media

Figures

Other

Tables

View Table of Contents