A PSO-CNN-based approach for Enhancing Precision in Plant Leaf Disease Detection and Classification

Ashish Gupta, Deepak Gupta, Mohammad Husain, Mohammad Nadeem Ahmed, Arshad Ali, Parveen Badoni


The Plant diseases that impact the leaves can hinder the progress of plant species, making earlier and precise diagnosis crucial to minimize additional harm. However, the intriguing methoda required additional time, expertise, and exclusivity. Utilizing leaf images for disease identification, research into deep learning (DL) holds significant promise for enhancing accuracy. The substantial progress in deep learning has opened up opportunities to enhance the precision and efficiency of plant leaf disease identification systems. This work introduces an innovative approach for plant disease detection and classification called Particle Swarm Optimization with Convolutional Neural Network (PSO-CNN). The work also explored disease category in plant leaves using Particle Swarm Optimization (PSO), which extracts color, texture, and leaf arrangement information from images through a CNN classifier. Several effectiveness metrics were employed to evaluate and suggest that the presented approach outperforms existing technique in terms of accuracy and performance measures, particularly during the stages of disease detection, including image acquisition, segmentation, noise reduction, and classification.

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S. Baranwal, S. Khandelwal, A. Arora, Deep

learning convolutional neural network for apple

leaves disease detection, SSRN Electron. J.

(2019), https://doi.org/ 10.2139/ssrn.3351641.

Y. Zhao, et al., Plant disease detection using

generated leaves based on DoubleGAN, IEEE

ACM Trans. Comput. Biol. Bioinf (2021),

https://doi.org/ 10.1109/TCBB.2021.3056683,


Department of Computer Science, Sukkur IBA

University, Pakistan. et al., Plant disease

detection using deep learning, Int. J. Recent

Technol. Eng. IJRTE 9 (1) (May 2020) 909–


https://doi.org/10.35940/ijrte.A2139.059120.). I

L. Li, S. Zhang, B. Wang, Plant disease

detection and class. by deep learning—a review,

IEEE Acc 9 (2021) 56683–56698,


R.A.D.L. Pugoy, V.Y. Mariano, Automated

Rice Leaf Disease Detection Using Color Image

Analysis, Chengdu, China, Apr. 2011, 80090F,

https://doi.org/10.1117/ 12.896494.

D. Tiwari, M. Ashish, N. Gangwar, A. Sharma,

S. Patel, S. Bhardwaj, Potato leaf diseases

detection using deep learning, in: 2020 4th

International Conference on Intelligent

Computing and Control Systems (ICICCS),

May 2020, pp. 461–466,


Madurai, India

P.M. Kwabena, B. Asubam, A. Abra, Gabor

capsule network for plant disease detection, Int.

J. Adv. Comput. Sci. Appl. 11 (10) (2020),



Xiaoyang Rao and Xuesong Yan, Particle

Swarm Optimization Algorithm Based on

Information Sharing in Industry 4.0, Hindawi

Wireless Communications and Mobile

Computing Volume 2022, Article ID 4328185,

pages https://doi.org/10.1155/2022/4328185

Ahmed G. Gad, Particle Swarm

Optimization Algorithm and Its

Applications: A Systematic Review,

Springer, Archives of Computational

Methods in Engineering (2022) 29:2531–



S.V. Militante, B.D. Gerardo, N.V.

Dionisio, Plant leaf detection and disease

recognition using deep learning, in: 2019

IEEE Eurasia Conference on IOT,

Communication and Engineering,

ECICE), 2019, pp. 579–582.

A. Venkataramanan, D.K.P. Honakeri, P.

Agarwal, Plant disease detection and

classification using deep neural networks,

Int. J. Comput. Sci. Eng. 11 (9) (2019) 40–

A. Abbas, S. Jain, M. Gour, S.

Vankudothu, Tomato plant disease

detection using transfer learning with CGAN synthetic images, Comput. Electron.

Agric. 187 (Aug. 2021), 106279,


M. Ali Jan Ghasab, S. Khamis, F.

Mohammad, H. Jahani Fariman, Feature

decisionmaking ant colony optimization

system for an automated recognition of

plant species, Expert Syst. Appl. 42 (5)

(2015) 2361–2370,


eswa.2014.11.011. Apr.

G.K. Sandhu, R. Kaur, Plant disease

detection techniques: a review, in: 2019

International Conference on Automation,

Computational and Technology Management

(ICACTM), London, United Kingdom, Apr.

, pp. 34–38, https://


R. Patil, S. Udgave, S. More, D. Nemishte, M.

Kasture, Grape leaf disease detection using kmeans clustering algorithm, Int. Res. J. Eng.

Technol. IRJET 3 (4) (2016) 2330–2333.

M. Kumar, P. Gupta, P. Madhav, Sachin,

Disease detection in coffee plants using

convolutional neural network, in: 2020 5th

International Conference on Communication

and Electronics Systems (ICCES), 2020, pp.

–760, https://doi.


Coimbatore, India, Jun.

H.T. Rauf, B.A. Saleem, M.I.U. Lali, M.A.

Khan, M. Sharif, S.A.C. Bukhari, A citrus fruits

and leaves dataset for detection and

classification of citrus diseases through

machine learning, Data Brief 26 (Oct. 2019),

, https://doi.org/10.1016/j.


A.Gupta, Sanjeev k Gupta, P.Yadav, D.Gupta,

Design and Develop Novel Framework for

Plant Disease DetectionUsing Convolution

Neural Network, RandomForest Classifier

andSupport Vector Machine, Eur. Chem. Bull.

,12(10), 1539-155

DOI: https://doi.org/10.31449/inf.v47i9.5188

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