AI-Driven Identification of Grapevine Fungal Spores via Microscopic Imaging and Feature Optimization with Cuckoo Search Algorithm
Grapevine diseases caused by fungal pathogens pose a significant threat to viticulture, leading to considerable economic losses and reduced productivity. Early, intelligent detection of fungal spores is vital for effective disease management. This study presents a high-accuracy classification model...
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| Language: | English |
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Elsevier
2025-08-01
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| Series: | Smart Agricultural Technology |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S277237552500262X |
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| author | Xin Shi Seyed Mohamad Javidan Yiannis Ampatzidis Zhao Zhang |
| author_facet | Xin Shi Seyed Mohamad Javidan Yiannis Ampatzidis Zhao Zhang |
| author_sort | Xin Shi |
| collection | DOAJ |
| description | Grapevine diseases caused by fungal pathogens pose a significant threat to viticulture, leading to considerable economic losses and reduced productivity. Early, intelligent detection of fungal spores is vital for effective disease management. This study presents a high-accuracy classification model that uses microscopic images to differentiate among four closely related grapevine pathogens: Lasiodiplodia brasiliensis, L. crassispora, L. exigua, and L. gilanensis. Advanced image processing techniques were applied to segment spores and extract texture, color, and shape features. A support vector machine (SVM) classifier achieved 97.5% overall accuracy after preprocessing, a substantial improvement over the initial 81.25% without image preprocessing. Individual species classification accuracies were 95.24% for L. brasiliensis, 95.00% for L. crassispora, and 100% for both L. exigua and L. gilanensis. Feature selection using the cuckoo search algorithm identified ten key attributes for classification. Among these, texture features (energy, contrast, entropy, homogeneity, and standard deviation) proved most impactful, achieving classification accuracies of 75.50%, 70.06%, 62.14%, 55.67%, and 52.51%, respectively. Shape features (major and minor axis lengths, area) and color features (mean, standard deviation) followed with lower accuracy scores. The results emphasize the supportive role of texture in fungal spore classification and highlight the supporting roles of shape and color. This AI-driven framework offers a robust tool for early disease diagnosis in grapevines, with broader implications for automated plant pathology, precision agriculture, and smart farming technologies. |
| format | Article |
| id | doaj-art-e56cf9d83f154237b2a48e36ca69050f |
| institution | OA Journals |
| issn | 2772-3755 |
| language | English |
| publishDate | 2025-08-01 |
| publisher | Elsevier |
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| series | Smart Agricultural Technology |
| spelling | doaj-art-e56cf9d83f154237b2a48e36ca69050f2025-08-20T02:26:14ZengElsevierSmart Agricultural Technology2772-37552025-08-011110102910.1016/j.atech.2025.101029AI-Driven Identification of Grapevine Fungal Spores via Microscopic Imaging and Feature Optimization with Cuckoo Search AlgorithmXin Shi0Seyed Mohamad Javidan1Yiannis Ampatzidis2Zhao Zhang3Institute of Agricultural Mechanization, Xinjiang Academy of Agricultural Sciences, Urumqi 830091, ChinaDepartment of Biosystems Engineering, Tarbiat Modares University, Tehran, Iran; Corresponding authors.Agricultural and Biological Engineering Department, Southwest Florida Research and Education Center, University of Florida, 2685 FL‑29, Immokalee, FL 34142, USA; Corresponding authors.Key Laboratory of Smart Agriculture System Integration, Ministry of Education, China Agricultural University, Beijing 100083, ChinaGrapevine diseases caused by fungal pathogens pose a significant threat to viticulture, leading to considerable economic losses and reduced productivity. Early, intelligent detection of fungal spores is vital for effective disease management. This study presents a high-accuracy classification model that uses microscopic images to differentiate among four closely related grapevine pathogens: Lasiodiplodia brasiliensis, L. crassispora, L. exigua, and L. gilanensis. Advanced image processing techniques were applied to segment spores and extract texture, color, and shape features. A support vector machine (SVM) classifier achieved 97.5% overall accuracy after preprocessing, a substantial improvement over the initial 81.25% without image preprocessing. Individual species classification accuracies were 95.24% for L. brasiliensis, 95.00% for L. crassispora, and 100% for both L. exigua and L. gilanensis. Feature selection using the cuckoo search algorithm identified ten key attributes for classification. Among these, texture features (energy, contrast, entropy, homogeneity, and standard deviation) proved most impactful, achieving classification accuracies of 75.50%, 70.06%, 62.14%, 55.67%, and 52.51%, respectively. Shape features (major and minor axis lengths, area) and color features (mean, standard deviation) followed with lower accuracy scores. The results emphasize the supportive role of texture in fungal spore classification and highlight the supporting roles of shape and color. This AI-driven framework offers a robust tool for early disease diagnosis in grapevines, with broader implications for automated plant pathology, precision agriculture, and smart farming technologies.http://www.sciencedirect.com/science/article/pii/S277237552500262XDisease detectionFeature selectionFungal pathogensGrapevine diseasesMachine learningMicroscopic images |
| spellingShingle | Xin Shi Seyed Mohamad Javidan Yiannis Ampatzidis Zhao Zhang AI-Driven Identification of Grapevine Fungal Spores via Microscopic Imaging and Feature Optimization with Cuckoo Search Algorithm Smart Agricultural Technology Disease detection Feature selection Fungal pathogens Grapevine diseases Machine learning Microscopic images |
| title | AI-Driven Identification of Grapevine Fungal Spores via Microscopic Imaging and Feature Optimization with Cuckoo Search Algorithm |
| title_full | AI-Driven Identification of Grapevine Fungal Spores via Microscopic Imaging and Feature Optimization with Cuckoo Search Algorithm |
| title_fullStr | AI-Driven Identification of Grapevine Fungal Spores via Microscopic Imaging and Feature Optimization with Cuckoo Search Algorithm |
| title_full_unstemmed | AI-Driven Identification of Grapevine Fungal Spores via Microscopic Imaging and Feature Optimization with Cuckoo Search Algorithm |
| title_short | AI-Driven Identification of Grapevine Fungal Spores via Microscopic Imaging and Feature Optimization with Cuckoo Search Algorithm |
| title_sort | ai driven identification of grapevine fungal spores via microscopic imaging and feature optimization with cuckoo search algorithm |
| topic | Disease detection Feature selection Fungal pathogens Grapevine diseases Machine learning Microscopic images |
| url | http://www.sciencedirect.com/science/article/pii/S277237552500262X |
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