Visualizing Plant Disease Distribution and Evaluating Model Performance for Deep Learning Classification with YOLOv8

Ghafar, Abdul; Chen, Caikou; Atif Ali Shah, Syed; Ur Rehman, Zia; Rahman, Gul

doi:10.3390/pathogens13121032

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Open AccessArticle

Visualizing Plant Disease Distribution and Evaluating Model Performance for Deep Learning Classification with YOLOv8

by

Abdul Ghafar

¹,

Caikou Chen

^1,*

,

Syed Atif Ali Shah

^2,3

,

Zia Ur Rehman

¹

and

Gul Rahman

¹

College of Information Engineering, Yangzhou University, Yangzhou 225009, China

²

School of Engineering and Applied Sciences, Bahria University Islamabad, Islamabad P.O. Box 44000, Pakistan

³

Detechpro LLC, Integrating Solutions, Newark, DE 19906, USA

^*

Author to whom correspondence should be addressed.

Pathogens 2024, 13(12), 1032; https://doi.org/10.3390/pathogens13121032

Submission received: 16 September 2024 / Revised: 15 November 2024 / Accepted: 21 November 2024 / Published: 22 November 2024

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Abstract

This paper presents a novel methodology for plant disease detection using YOLOv8 (You Only Look Once version 8), a state-of-the-art object detection model designed for real-time image classification and recognition tasks. The proposed approach involves training a custom YOLOv8 model to detect and classify various plant conditions accurately. The model was evaluated using a testing subset to measure its performance in detecting different plant diseases. To ensure the model’s robustness and generalizability beyond the training dataset, it was further tested on a set of unseen images sourced from Google Images. This additional testing aimed to assess the model’s effectiveness in real-world scenarios, where it might encounter new data. The evaluation results were auspicious, demonstrating the model’s capability to classify plant conditions, such as diseases, with high accuracy. Moreover, the use of YOLOv8 offers significant improvements in speed and precision, making it suitable for real-time plant disease monitoring applications. The findings highlight the potential of this methodology for broader agricultural applications, including early disease detection and prevention.

Keywords: plants diseases; convolutional neural networks; YOLO v8; DarkNet; ResNet

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MDPI and ACS Style

Ghafar, A.; Chen, C.; Atif Ali Shah, S.; Ur Rehman, Z.; Rahman, G. Visualizing Plant Disease Distribution and Evaluating Model Performance for Deep Learning Classification with YOLOv8. Pathogens 2024, 13, 1032. https://doi.org/10.3390/pathogens13121032

AMA Style

Ghafar A, Chen C, Atif Ali Shah S, Ur Rehman Z, Rahman G. Visualizing Plant Disease Distribution and Evaluating Model Performance for Deep Learning Classification with YOLOv8. Pathogens. 2024; 13(12):1032. https://doi.org/10.3390/pathogens13121032

Chicago/Turabian Style

Ghafar, Abdul, Caikou Chen, Syed Atif Ali Shah, Zia Ur Rehman, and Gul Rahman. 2024. "Visualizing Plant Disease Distribution and Evaluating Model Performance for Deep Learning Classification with YOLOv8" Pathogens 13, no. 12: 1032. https://doi.org/10.3390/pathogens13121032

APA Style

Ghafar, A., Chen, C., Atif Ali Shah, S., Ur Rehman, Z., & Rahman, G. (2024). Visualizing Plant Disease Distribution and Evaluating Model Performance for Deep Learning Classification with YOLOv8. Pathogens, 13(12), 1032. https://doi.org/10.3390/pathogens13121032

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

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Visualizing Plant Disease Distribution and Evaluating Model Performance for Deep Learning Classification with YOLOv8

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