Open Access   Article Go Back

A Comprehensive Review of Crop Disease Identification Through Modern Artificial Intelligence Technology

Gajendra Tandan1 , Asha Ambhaikar2

  1. Dept. of Computer Science, Kalinga University Naya, Raipur (CG), India.
  2. Dept. of Computer Science and IT, Kalinga University, Raipur (CG), India.

Section:Research Paper, Product Type: Journal Paper
Volume-12 , Issue-7 , Page no. 9-15, Jul-2024

CrossRef-DOI:   https://doi.org/10.26438/ijcse/v12i7.915

Online published on Jul 31, 2024

Copyright © Gajendra Tandan, Asha Ambhaikar . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

View this paper at   Google Scholar | DPI Digital Library

How to Cite this Paper

  • IEEE Citation
  • MLA Citation
  • APA Citation
  • BibTex Citation
  • RIS Citation

IEEE Style Citation: Gajendra Tandan, Asha Ambhaikar, “A Comprehensive Review of Crop Disease Identification Through Modern Artificial Intelligence Technology,” International Journal of Computer Sciences and Engineering, Vol.12, Issue.7, pp.9-15, 2024.

MLA Style Citation: Gajendra Tandan, Asha Ambhaikar "A Comprehensive Review of Crop Disease Identification Through Modern Artificial Intelligence Technology." International Journal of Computer Sciences and Engineering 12.7 (2024): 9-15.

APA Style Citation: Gajendra Tandan, Asha Ambhaikar, (2024). A Comprehensive Review of Crop Disease Identification Through Modern Artificial Intelligence Technology. International Journal of Computer Sciences and Engineering, 12(7), 9-15.

BibTex Style Citation:
@article{Tandan_2024,
author = {Gajendra Tandan, Asha Ambhaikar},
title = {A Comprehensive Review of Crop Disease Identification Through Modern Artificial Intelligence Technology},
journal = {International Journal of Computer Sciences and Engineering},
issue_date = {7 2024},
volume = {12},
Issue = {7},
month = {7},
year = {2024},
issn = {2347-2693},
pages = {9-15},
url = {https://www.ijcseonline.org/full_paper_view.php?paper_id=5705},
doi = {https://doi.org/10.26438/ijcse/v12i7.915}
publisher = {IJCSE, Indore, INDIA},
}

RIS Style Citation:
TY - JOUR
DO = {https://doi.org/10.26438/ijcse/v12i7.915}
UR - https://www.ijcseonline.org/full_paper_view.php?paper_id=5705
TI - A Comprehensive Review of Crop Disease Identification Through Modern Artificial Intelligence Technology
T2 - International Journal of Computer Sciences and Engineering
AU - Gajendra Tandan, Asha Ambhaikar
PY - 2024
DA - 2024/07/31
PB - IJCSE, Indore, INDIA
SP - 9-15
IS - 7
VL - 12
SN - 2347-2693
ER -

VIEWS PDF XML
190 172 downloads 92 downloads
  
  
           

Abstract

India is a developing country. The 65% of India`s people live in villages, whose main occupation is agriculture. India has certainly made progress in the field of information technology. The IT advancement and technology is direct impact on agriculture. After the advent of the 21st century, modern agricultural technology got a boost in India. In the present era, farmers are moving towards farming using modern and scientific methods. AI based technology is the foundation of modern technology. Equipped with modern equipment and applications for prevention of pests and diseases in crops. The AI technology quickly and speedily identify the diseases occurring in crops can very easily treated with accuracy high accuracy. In this review, we have studied a lot of AI and their sub-domain machine learning (ML) method application in agriculture, especially on crop leaf diseases. ML technology can be used to identify leaf disease in the captured images.

Key-Words / Index Term

Machine Learning, Tomato disease, CNN, Artificial Intelligence (AI), Agriculture, Disease, Food Crops

References

[1] N. Dawn et al., “Implementation of Artificial Intelligence, Machine Learning, and Internet of Things (IoT) in revolutionizing Agriculture: A review on recent trends and challenges,” International Journal of Experimental Research and Review, Vol.30, 2023, doi: 10.52756/ijerr. 2023.v30.018.
[2] M. Shoaib et al., “An advanced deep learning models-based plant disease detection: A review of recent research,” Frontiers in Plant Science. Frontiers Media SA, Vol.14, 2023. doi: 10.3389/fpls.2023.1158933.
[3] A. Siddiqua, M. A. Kabir, T. Ferdous, I. B. Ali, and L. A. Weston, “Evaluating Plant Disease Detection Mobile Applications: Quality and Limitations,” Agronomy, Aug., Vol.12, No.8, 2022. doi: 10.3390/agronomy12081869.
[4] S. Bhuvaneswari, R. Surendiran, R. Aarthi, M. Thangamani, and R. B. Lingisetti, “Disease Detection in Plant Leaf using LNet Based on Deep Learning,” International Journal of Engineering Trends and Technology, Sep., Vol.70, No.9, pp.64–75, 2022, doi: 10.14445/22315381/IJETT-V70I9P207.
[5] H. Anwar et al., “The NWRD Dataset: An Open-Source Annotated Segmentation Dataset of Diseased Wheat Crop,” Sensors, Aug., Vol.23, No.15, 2023. doi: 10.3390/s23156942.
[6] M. M. Hasan et al., “Enhancing Rice Crop Management: Disease Classification Using Convolutional Neural Networks and Mobile Application Integration,” Agriculture (Switzerland), Vol.13, No.8, Aug. 2023. doi: 10.3390/agriculture13081549.
[7] V. A. Gontijo da Cunha, J. Hariharan, Y. Ampatzidis, and P. D. Roberts, “Early detection of tomato bacterial spot disease in transplant tomato seedlings utilising remote sensing and artificial intelligence,” Biosyst Eng, Oct., Vol.234, pp.172–186, 2023. doi: 10.1016/j.biosystemseng.2023.09.002.
[8] J. A. Pandian, V. D. Kumar, O. Geman, M. Hnatiuc, M. Arif, and K. Kanchanadevi, “Plant Disease Detection Using Deep Convolutional Neural Network,” Applied Sciences (Switzerland), Jul., Vol.12, No.14, 2022. doi: 10.3390/app12146982.
[9] J. Andrew, J. Eunice, D. E. Popescu, M. K. Chowdary, and J. Hemanth, “Deep Learning-Based Leaf Disease Detection in Crops Using Images for Agricultural Applications,” Agronomy, vol. 12, no. 10, Oct. 2022, doi: 10.3390/agronomy12102395.
[10] S. S. Harakannanavar, J. M. Rudagi, V. I. Puranikmath, A. Siddiqua, and R. Pramodhini, “Plant leaf disease detection using computer vision and machine learning algorithms,” Global Transitions Proceedings, Vol.3, No.1, pp.305–310, Jun. 2022, doi: 10.1016/j.gltp.2022.03.016.
[11] A. M. Roy and J. Bhaduri, “A Deep Learning Enabled Multi-Class Plant Disease Detection Model Based on Computer Vision,” AI (Switzerland), Sep., Vol.2, No.3, pp.413–428, 2021. doi: 10.3390/ai2030026.
[12] M. Chohan*, A. Khan, R. Chohan, S. H. Katpar, and M. S. Mahar, “Plant Disease Detection using Deep Learning,” International Journal of Recent Technology and Engineering (IJRTE), May, Vol.9, No.1, pp.909–914, 2020. doi: 10.35940/ijrte. A2139.059120.
[13] S. R. Maniyath et al., “Plant disease detection using machine learning,” in Proceedings - 2018 International Conference on Design Innovations for 3Cs Compute Communicate Control, ICDI3C 2018, Institute of Electrical and Electronics Engineers Inc., Aug., pp.41–45, 2018. doi: 10.1109/ICDI3C.2018.00017.
[14] M. Shoaib et al., “An advanced deep learning models-based plant disease detection: A review of recent research,” Frontiers in Plant Science, Frontiers Media SA, Vol.14, 2023. doi: 10.3389/fpls.2023.1158933.
[15] S. Saadoon and A. Adel, “A Comparison Between SVM and K-NN for classification of Plant Diseases,” Diyala Journal for Pure Science, Vol.14, No.2, pp.94–105, 2018. doi: 10.24237/djps.1402.383b.
[16] S. Ponni, A. Sathya, S. Ramakrishnan, M. I. Shafreen, R. Harshini, and P. Malini, “Optimal Plant Leaf Disease Detection using SVM classifier with Fuzzy System.”, 2022.
[17] M. Fallah and E. G. Parmehr, “Detection of Chilo Suppressalis using Smartphone Images and Deep Learning,” Journal of Agricultural Machinery, Vol.13, No.2, pp.195–211, 2023. doi: 10.22067/jam.2022.72647.1064.
[18] F. BAL and F. KAYAALP, “Review of machine learning and deep learning models in agriculture,” International Advanced Researches and Engineering Journal, Vol.5, No.2, pp.309–323, 2021. doi: 10.35860/iarej.848458.
[19] P. Bharman, S. Ahmad Saad, S. Khan, I. Jahan, M. Ray, and M. Biswas, “Deep Learning in Agriculture: A Review,” Asian Journal of Research in Computer Science, pp.28–47, 2022. doi: 10.9734/ajrcos/2022/v13i230311.
[20] M. M. Hasan et al., “Enhancing Rice Crop Management: Disease Classification Using Convolutional Neural Networks and Mobile Application Integration,” Agriculture (Switzerland), Vol.13, No.8, Aug. 2023, doi: 10.3390/agriculture13081549.
[21] S. Bhuvaneswari, R. Surendiran, R. Aarthi, M. Thangamani, and R. B. Lingisetti, “Disease Detection in Plant Leaf using LNet Based on Deep Learning,” International Journal of Engineering Trends and Technology, vol.70, no.9, pp.64–75, Sep. 2022, doi: 10.14445/22315381/IJETT-V70I9P207.
[22] M. Chohan*, A. Khan, R. Chohan, S. H. Katpar, and M. S. Mahar, “Plant Disease Detection using Deep Learning,” International Journal of Recent Technology and Engineering (IJRTE), vol. 9, no. 1, pp. 909–914, May 2020, doi: 10.35940/ijrte. A2139.059120.
[23] J. Liu and X. Wang, “Plant diseases and pests detection based on deep learning: a review,” Plant Methods, Vol.17, No.1. BioMed Central Ltd, Dec. 01, 2021. doi: 10.1186/s13007-021-00722-9.
[24] S. Jana, A. Rijuvana Begum, S. Selvaganesan, and P. Suresh, “DEEP BELIEF NETWORK BASED DISEASE DETECTION IN PEPPER LEAF FOR FARMING SECTOR,” Turkish Journal of Physiotherapy and Rehabilitation, Vol.32, No.2, 2021.
[25] M. M. Hossain et al., “Analysis of the performance of feature optimization techniques for the diagnosis of machine learning-based chronic kidney disease,” Machine Learning with Applications, Vol.9, pp.100330, 2022, doi: 10.1016/j.mlwa.2022.100330.
[26] B. V. Gokulnath and G. Usha Devi, “A survey on plant disease prediction using machine learning and deep learning techniques,” Inteligencia Artificial, Vol.23, No.65, pp.136–154, 2020, doi: 10.4114/intartif. vol23iss65pp136-154.
[27] T. B. Shahi, C. Y. Xu, A. Neupane, and W. Guo, “Recent Advances in Crop Disease Detection Using UAV and Deep Learning Techniques,” Remote Sensing, MDPI, May 01, Vol.15, No.9, 2023. doi: 10.3390/rs15092450.
[28] M.-P. Song and G.-C. Gu, “RESEARCH ON PARTICLE SWARM OPTIMIZATION: A REVIEW,” 2004.
[29] P. Verma, V. K. Awasthi, S. K. Sahu, and A. K. Shrivas, “Coronary Artery Disease Classification Using Deep Neural Network and Ensemble Models Optimized by Particle Swarm Optimization,” International Journal of Applied Metaheuristic Computing, Vol.13, No.1, pp.1–25, 2021. doi: 10.4018/ijamc.292504.
[30] H. Anwar et al., “The NWRD Dataset: An Open-Source Annotated Segmentation Dataset of Diseased Wheat Crop,” Sensors, Vol.23, No.15, 2023, doi: 10.3390/s23156942.
[31] J. Andrew, J. Eunice, D. E. Popescu, M. K. Chowdary, and J. Hemanth, “Deep Learning-Based Leaf Disease Detection in Crops Using Images for Agricultural Applications,” Agronomy, Vol.12, No.10, 2022. doi: 10.3390/agronomy12102395.
[32] A. M. Roy and J. Bhaduri, “A Deep Learning Enabled Multi-Class Plant Disease Detection Model Based on Computer Vision,” AI (Switzerland), Vol.2, No.3, pp.413–428, 2021. doi: 10.3390/ai2030026.