Estimation of Fractal Dimensions and Classification of Plant Disease with Complex Backgrounds
Accurate classification of plant disease by farming robot cameras can increase crop yield and reduce unnecessary agricultural chemicals, which is a fundamental task in the field of sustainable and precision agriculture. However, until now, disease classification has mostly been performed by manual m...
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MDPI AG
2025-05-01
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| Series: | Fractal and Fractional |
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| Online Access: | https://www.mdpi.com/2504-3110/9/5/315 |
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| author | Muhammad Hamza Tariq Haseeb Sultan Rehan Akram Seung Gu Kim Jung Soo Kim Muhammad Usman Hafiz Ali Hamza Gondal Juwon Seo Yong Ho Lee Kang Ryoung Park |
| author_facet | Muhammad Hamza Tariq Haseeb Sultan Rehan Akram Seung Gu Kim Jung Soo Kim Muhammad Usman Hafiz Ali Hamza Gondal Juwon Seo Yong Ho Lee Kang Ryoung Park |
| author_sort | Muhammad Hamza Tariq |
| collection | DOAJ |
| description | Accurate classification of plant disease by farming robot cameras can increase crop yield and reduce unnecessary agricultural chemicals, which is a fundamental task in the field of sustainable and precision agriculture. However, until now, disease classification has mostly been performed by manual methods, such as visual inspection, which are labor-intensive and often lead to misclassification of disease types. Therefore, previous studies have proposed disease classification methods based on machine learning or deep learning techniques; however, most did not consider real-world plant images with complex backgrounds and incurred high computational costs. To address these issues, this study proposes a computationally effective residual convolutional attention network (RCA-Net) for the disease classification of plants in field images with complex backgrounds. RCA-Net leverages attention mechanisms and multiscale feature extraction strategies to enhance salient features while reducing background noises. In addition, we introduce fractal dimension estimation to analyze the complexity and irregularity of class activation maps for both healthy plants and their diseases, confirming that our model can extract important features for the correct classification of plant disease. The experiments utilized two publicly available datasets: the sugarcane leaf disease and potato leaf disease datasets. Furthermore, to improve the capability of our proposed system, we performed fractal dimension estimation to evaluate the structural complexity of healthy and diseased leaf patterns. The experimental results show that RCA-Net outperforms state-of-the-art methods with an accuracy of 93.81% on the first dataset and 78.14% on the second dataset. Furthermore, we confirm that our method can be operated on an embedded system for farming robots or mobile devices at fast processing speed (78.7 frames per second). |
| format | Article |
| id | doaj-art-53708024b6a548e99affb4e52d785476 |
| institution | OA Journals |
| issn | 2504-3110 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Fractal and Fractional |
| spelling | doaj-art-53708024b6a548e99affb4e52d7854762025-08-20T01:56:24ZengMDPI AGFractal and Fractional2504-31102025-05-019531510.3390/fractalfract9050315Estimation of Fractal Dimensions and Classification of Plant Disease with Complex BackgroundsMuhammad Hamza Tariq0Haseeb Sultan1Rehan Akram2Seung Gu Kim3Jung Soo Kim4Muhammad Usman5Hafiz Ali Hamza Gondal6Juwon Seo7Yong Ho Lee8Kang Ryoung Park9Division of Electronics and Electrical Engineering, Dongguk University, 30 Pildong-ro 1-gil, Jung-gu, Seoul 04620, Republic of KoreaDivision of Electronics and Electrical Engineering, Dongguk University, 30 Pildong-ro 1-gil, Jung-gu, Seoul 04620, Republic of KoreaDivision of Electronics and Electrical Engineering, Dongguk University, 30 Pildong-ro 1-gil, Jung-gu, Seoul 04620, Republic of KoreaDivision of Electronics and Electrical Engineering, Dongguk University, 30 Pildong-ro 1-gil, Jung-gu, Seoul 04620, Republic of KoreaDivision of Electronics and Electrical Engineering, Dongguk University, 30 Pildong-ro 1-gil, Jung-gu, Seoul 04620, Republic of KoreaDivision of Electronics and Electrical Engineering, Dongguk University, 30 Pildong-ro 1-gil, Jung-gu, Seoul 04620, Republic of KoreaDivision of Electronics and Electrical Engineering, Dongguk University, 30 Pildong-ro 1-gil, Jung-gu, Seoul 04620, Republic of KoreaDivision of Electronics and Electrical Engineering, Dongguk University, 30 Pildong-ro 1-gil, Jung-gu, Seoul 04620, Republic of KoreaDivision of Electronics and Electrical Engineering, Dongguk University, 30 Pildong-ro 1-gil, Jung-gu, Seoul 04620, Republic of KoreaDivision of Electronics and Electrical Engineering, Dongguk University, 30 Pildong-ro 1-gil, Jung-gu, Seoul 04620, Republic of KoreaAccurate classification of plant disease by farming robot cameras can increase crop yield and reduce unnecessary agricultural chemicals, which is a fundamental task in the field of sustainable and precision agriculture. However, until now, disease classification has mostly been performed by manual methods, such as visual inspection, which are labor-intensive and often lead to misclassification of disease types. Therefore, previous studies have proposed disease classification methods based on machine learning or deep learning techniques; however, most did not consider real-world plant images with complex backgrounds and incurred high computational costs. To address these issues, this study proposes a computationally effective residual convolutional attention network (RCA-Net) for the disease classification of plants in field images with complex backgrounds. RCA-Net leverages attention mechanisms and multiscale feature extraction strategies to enhance salient features while reducing background noises. In addition, we introduce fractal dimension estimation to analyze the complexity and irregularity of class activation maps for both healthy plants and their diseases, confirming that our model can extract important features for the correct classification of plant disease. The experiments utilized two publicly available datasets: the sugarcane leaf disease and potato leaf disease datasets. Furthermore, to improve the capability of our proposed system, we performed fractal dimension estimation to evaluate the structural complexity of healthy and diseased leaf patterns. The experimental results show that RCA-Net outperforms state-of-the-art methods with an accuracy of 93.81% on the first dataset and 78.14% on the second dataset. Furthermore, we confirm that our method can be operated on an embedded system for farming robots or mobile devices at fast processing speed (78.7 frames per second).https://www.mdpi.com/2504-3110/9/5/315artificial intelligenceplant disease classificationresidual convolution attention networkfractal dimension estimation |
| spellingShingle | Muhammad Hamza Tariq Haseeb Sultan Rehan Akram Seung Gu Kim Jung Soo Kim Muhammad Usman Hafiz Ali Hamza Gondal Juwon Seo Yong Ho Lee Kang Ryoung Park Estimation of Fractal Dimensions and Classification of Plant Disease with Complex Backgrounds Fractal and Fractional artificial intelligence plant disease classification residual convolution attention network fractal dimension estimation |
| title | Estimation of Fractal Dimensions and Classification of Plant Disease with Complex Backgrounds |
| title_full | Estimation of Fractal Dimensions and Classification of Plant Disease with Complex Backgrounds |
| title_fullStr | Estimation of Fractal Dimensions and Classification of Plant Disease with Complex Backgrounds |
| title_full_unstemmed | Estimation of Fractal Dimensions and Classification of Plant Disease with Complex Backgrounds |
| title_short | Estimation of Fractal Dimensions and Classification of Plant Disease with Complex Backgrounds |
| title_sort | estimation of fractal dimensions and classification of plant disease with complex backgrounds |
| topic | artificial intelligence plant disease classification residual convolution attention network fractal dimension estimation |
| url | https://www.mdpi.com/2504-3110/9/5/315 |
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