Abstract

Worldwide productivity of crops is seriously threatened by crop leaf diseases, which can result in large crop losses and negative economic effects. Effective disease management and crop protection depend on the early and precise detection and classification of these illnesses. Machine learning approaches have gained popularity recently due to their ability to automate procedures related to illness diagnosis and classification. An overview of the several machine learning–based methods used for crop leaf disease diagnosis and classification is provided in this review paper. We go over the basic ideas and methods of the machine learning algorithms that are applied here, as well as their advantages and disadvantages. Additionally, we examine and contrast the results of several machine learning approaches published in the literature, emphasizing the critical elements affecting their efficacy. Ultimately, we pinpoint the present obstacles and forthcoming research avenues to promote progress in this domain.

Keywords: Machine learning, convolutional neural network (CNN), transfer learning, support vector machine (SVM), random forest, deep learning

INTRODUCTION

New peaks and achievements have been established in the healthcare, transportation, business analytics, and agricultural fields as a result of the current surge in advancements in computer vision, neural networks, and machine learning [1]. India’s economy relies heavily on agriculture. To stay alive in this ever-changing climate and surroundings, automation and improvements to traditional techniques of disease detection are essential. To address the crop’s supply and demand problems, the agricultural sector requires radical improvements. Maximum agricultural output may be achieved by regular crop monitoring and rapid disease diagnosis if a crop is contaminated. In India, cotton farmers frequently face difficulties due to leaf diseases.

Keyworde: Machine learning, convolutional neural network (CNN), transfer learning, support vector machine (SVM), random forest, deep learning

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Refrences:

1. Holzinger A, Kieseberg P, Weippl E, Tjoa AM. Current advances, trends and challenges of machine
learning and knowledge extraction: from machine learning to explainable AI. In: Machine Learning
and Knowledge Extraction: Second IFIP TC 5, TC 8/WG 8.4, 8.9, TC 12/WG 12.9 International
Cross-Domain Conference, CD-MAKE 2018, Hamburg, Germany, August 27–30, 2018,
Proceedings 2. pp. 1–8.
2. Too EC, Yujian L, Njuki S, Yingchun L. A comparative study of fine-tuning deep learning models
for plant disease identification. Computers Electron Agric. 2019; 161: 272–279.
3. Amara J, Bouaziz B, Algergawy A. A deep learning-based approach for banana leaf diseases classification.
In: Mitschang B, et al., editors. Datenbanksysteme für Business, Technologie und Web (BTW 2017).
Lecture Notes in Informatics. Bonn, Germany: Gesellschaft für Informatik; 2017. pp. 79–88.
4. Patil SP, Zambre RS. Classification of cotton leaf spot disease using support vector machine. Int J
Eng Res. 2014; 3 (4): 1511–1514.
5. Batmavady S, Samundeeswari S. Detection of cotton leaf diseases using image processing. Int J
Recent Technol Eng. 2019; 8 (2S4): 169–173.
6. Shah N, Jain S. Detection of disease in cotton leaf using artificial neural network. In: 2019 Amity
International Conference on Artificial Intelligence (AICAI), Dubai, UAE, February 4–6, 2019. pp.
473–476.
7. Bhimte NR, Thool VR. Diseases detection of cotton leaf spot using image processing and SVM
classifier. In: 2018 Second International Conference on Intelligent Computing and Control Systems
(ICICCS), Madurai, India, June 14–15, 2018. pp. 340–344.
8. Lu Y, Yi S, Zeng N, Liu Y, Zhang Y. Identification of rice diseases using deep convolutional neural
networks. Neurocomputing. 2017; 267: 378–384.

9. Pagariya R, Bartere M. Identification of cotton crop diseases and provide their remedies using K-
means clustering. Int J Adv Res Computer Sci. 2014; 5 (8): 58–61.

10. Warne PP, Ganorkar SR. Detection of diseases on cotton leaves using K-mean clustering method.
Int Res J Eng Technol. 2015; 2 (4): 425–431.
11. Sarangdhar AA, Pawar VR. Machine learning regression technique for cotton leaf disease detection
and controlling using IoT. In: 2017 International Conference of Electronics, Communication and
Aerospace Technology (ICECA), Coimbatore, India, April 20–22, 2017. Volume 2, pp. 449–454.
12. Arivazhagan S, Ligi SV. Mango leaf diseases identification using convolutional neural network. Int
J Pure Appl Math. 2018; 120 (6): 11067–11079.
13. Guo T, Dong J, Li H, Gao Y. Simple convolutional neural network on image classification. In: 2017
IEEE 2nd International Conference on Big Data Analysis (ICBDA), Beijing, China, March 10–12,
2017. pp. 721–724.
14. Wallelign S, Polceanu M, Buche C. Soybean plant disease identification using convolutional neural
network. In: The Thirty-First International Florida Artificial Intelligence Society Conference
(FLAIRS), May 10, 2018. pp. 146–151.
15. Durmuş H, Güneş EO, Kırcı M. Disease detection on the leaves of the tomato plants by using deep
learning. In: 2017 6th International Conference on Agro-Geoinformatics, Fairfax, VA, USA,
August 7–10, 2017. pp. 1–5.
16. Hidayatuloh A, Nursalman M, Nugraha E. Identification of tomato plant diseases by leaf image
using Squeezenet model. In: 2018 International Conference on Information Technology Systems
and Innovation (ICITSI), Bandung, Indonesia, October 22–26, 2018. pp. 199–204.
17. Sardogan M, Tuncer A, Ozen Y. Plant leaf disease detection and classification based on CNN with
LVQ algorithm. In: 2018 3rd International Conference on Computer Science and Engineering
(UBMK), Sarajevo, Bosnia and Herzegovina, September 20–23, 2018. pp. 382–385.
18. Tm P, Pranathi A, SaiAshritha K, Chittaragi NB, Koolagudi SG. Tomato leaf disease detection
using convolutional neural networks. In: 2018 Eleventh International Conference on Contemporary
Computing (IC3), Noida, India, August 2–4, 2018. pp. 1–5.
19. Varma SJ, Patoliya J. A survey on diseases detection and classification of cotton leaf. Int J Creative
Res Thoughts. 2018; 6 (1): 1014–1019.
20. Sibiya M, Sumbwanyambe M. Deep learning models for the detection of plant leaf diseases: a
systematic review. Preprints. February 21, 2019. Available at
https://www.preprints.org/manuscript/201902.0204/v1
21. Nagaraju M, Chawla P. Systematic review of deep learning techniques in plant disease detection.
Int J Syst Assurance Eng Manage. 2020; 11 (3): 547–560.
22. Mohanty SP, Hughes DP, Salathé M. Using deep learning for image-based plant disease detection.
Front Plant Science. 2016; 7: 215232.
23. Arsenovic M, Karanovic M, Sladojevic S, Anderla A, Stefanovic D. Solving current limitations of
deep learning based approaches for plant disease detection. Symmetry. 2019; 11 (7): 939.