Artificial Intelligence-Based Plant Disease Classification in Low-Light Environments

Gondal, Hafiz Ali Hamza; Jeong, Seong In; Jang, Won Ho; Kim, Jun Seo; Akram, Rehan; Irfan, Muhammad; Tariq, Muhammad Hamza; Park, Kang Ryoung

doi:10.3390/fractalfract9110691

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Open AccessArticle

Artificial Intelligence-Based Plant Disease Classification in Low-Light Environments

by

Hafiz Ali Hamza Gondal

,

Seong In Jeong

,

Won Ho Jang

,

Jun Seo Kim

,

Rehan Akram

,

Muhammad Irfan

,

Muhammad Hamza Tariq

and

Kang Ryoung Park

^*

Division of Electronics and Electrical Engineering, Dongguk University, Pildong-ro 1-gil, Jung-gu, Seoul 04620, Republic of Korea

^*

Author to whom correspondence should be addressed.

Fractal Fract. 2025, 9(11), 691; https://doi.org/10.3390/fractalfract9110691 (registering DOI)

Submission received: 12 September 2025 / Revised: 22 October 2025 / Accepted: 25 October 2025 / Published: 27 October 2025

(This article belongs to the Special Issue Fractional Order Complex Systems: Advanced Control, Intelligent Estimation and Reinforcement Learning Image Processing Algorithms, Second Edition)

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Abstract

The accurate classification of plant diseases is vital for global food security, as diseases can cause major yield losses and threaten sustainable and precision agriculture. The classification of plant diseases in low-light noisy environments is crucial because crops can be continuously monitored even at night. Important visual cues of disease symptoms can be lost due to the degraded quality of images captured under low-illumination, resulting in poor performance of conventional plant disease classifiers. However, researchers have proposed various techniques for classifying plant diseases in daylight, and no studies have been conducted for low-light noisy environments. Therefore, we propose a novel model for classifying plant diseases from low-light noisy images called dilated pixel attention network (DPA-Net). DPA-Net uses a pixel aBention mechanism and multi-layer dilated convolution with a high receptive field, which obtains essential features while highlighting the most relevant information under this challenging condition, allowing more accurate classification results. Additionally, we performed fractal dimension estimation on diseased and healthy leaves to analyze the structural irregularities and complexities. For the performance evaluation, experiments were conducted on two public datasets: the PlantVillage and Potato Leaf Disease datasets. In both datasets, the image resolution is 256 × 256 pixels in joint photographic experts group (JPG) format. For the first dataset, DPA-Net achieved an average accuracy of 92.11% and harmonic mean of precision and recall (F1-score) of 89.11%. For the second dataset, it achieved an average accuracy of 88.92% and an F1-score of 88.60%. These results revealed that the proposed method outperforms state-of-the-art methods. On the first dataset, our method achieved an improvement of 2.27% in average accuracy and 2.86% in F1-score compared to the baseline. Similarly, on the second dataset, it aBained an improvement of 6.32% in average accuracy and 6.37% in F1-score over the baseline. In addition, we confirm that our method is effective with the real low-illumination dataset self-constructed by capturing images at 0 lux using a smartphone at night. This approach provides farmers with an affordable practical tool for early disease detection, which can support crop protection worldwide.

Keywords: artificial intelligence; dilated pixel attention network; fractal dimension estimation; Internet-of-Things; crop protection

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MDPI and ACS Style

Gondal, H.A.H.; Jeong, S.I.; Jang, W.H.; Kim, J.S.; Akram, R.; Irfan, M.; Tariq, M.H.; Park, K.R. Artificial Intelligence-Based Plant Disease Classification in Low-Light Environments. Fractal Fract. 2025, 9, 691. https://doi.org/10.3390/fractalfract9110691

AMA Style

Gondal HAH, Jeong SI, Jang WH, Kim JS, Akram R, Irfan M, Tariq MH, Park KR. Artificial Intelligence-Based Plant Disease Classification in Low-Light Environments. Fractal and Fractional. 2025; 9(11):691. https://doi.org/10.3390/fractalfract9110691

Chicago/Turabian Style

Gondal, Hafiz Ali Hamza, Seong In Jeong, Won Ho Jang, Jun Seo Kim, Rehan Akram, Muhammad Irfan, Muhammad Hamza Tariq, and Kang Ryoung Park. 2025. "Artificial Intelligence-Based Plant Disease Classification in Low-Light Environments" Fractal and Fractional 9, no. 11: 691. https://doi.org/10.3390/fractalfract9110691

APA Style

Gondal, H. A. H., Jeong, S. I., Jang, W. H., Kim, J. S., Akram, R., Irfan, M., Tariq, M. H., & Park, K. R. (2025). Artificial Intelligence-Based Plant Disease Classification in Low-Light Environments. Fractal and Fractional, 9(11), 691. https://doi.org/10.3390/fractalfract9110691

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Artificial Intelligence-Based Plant Disease Classification in Low-Light Environments

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