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Article

Empowering Kiwifruit Cultivation with AI: Leaf Disease Recognition Using AgriVision-Kiwi Open Dataset

by
Theofanis Kalampokas
1,
Eleni Vrochidou
1,
Efthimia Mavridou
1,
Lazaros Iliadis
2,
Dionisis Voglitsis
3,
Maria Michalopoulou
4,
George Broufas
5 and
George A. Papakostas
1,*
1
MLV Research Group, Department of Informatics, Democritus University of Thrace, 65404 Kavala, Greece
2
Laboratory of Mathematics and Informatics (ISCE), Department of Civil Engineering, Democritus University of Thrace, 67100 Xanthi, Greece
3
Department of Electrical and Computer Engineering, Democritus University of Thrace, 67100 Xanthi, Greece
4
Department of Physical Education and Sports Science, Democritus University of Thrace, 69100 Komotini, Greece
5
Department of Agricultural Development, Democritus University of Thrace, 68200 Orestiada, Greece
*
Author to whom correspondence should be addressed.
Electronics 2025, 14(9), 1705; https://doi.org/10.3390/electronics14091705
Submission received: 15 March 2025 / Revised: 18 April 2025 / Accepted: 19 April 2025 / Published: 22 April 2025

Abstract

Kiwifruits are highly valued for their nutritional and health-related benefits as well as for their economic importance, since they significantly contribute to the economy of many countries that cultivate them. However, kiwifruits are very sensitive to diseases that may substantially impact their final quantity and quality. Computer vision (CV) has been extensively employed for disease recognition in the agricultural sector within the last decade; yet there are limited works dealing with kiwifruit disease recognition, and there is an obvious lack of open datasets to promote relevant research, especially when compared to research on other cultivations, e.g., grapes. To this end, this study introduces the first-reported open dataset for kiwifruit leaf disease recognition, including Alternaria, Nematodes and Phytophthora, while image datasets of Nematodes have not been previously reported. The proposed dataset, named AgriVision-Kiwi Dataset, has been used first for leaf detection with You Only Look Once version 11 (YOLOv11), reporting a bounding box loss of 0.053, and then to train various deep learning models for kiwifruit diseases recognition, reporting accuracies of 98.80% ± 0.5, e.g., 98.30% to 99.30%, after 10-fold cross-validation. The introduced dataset aims to encourage the development of CV applications towards the timely prevention of diseases’ spreading.
Keywords: computer vision; deep learning; disease recognition; kiwifruit cultivation; leaf diseases; precision agriculture; Alternaria; Nematodes; Phytophthora; digital transformation computer vision; deep learning; disease recognition; kiwifruit cultivation; leaf diseases; precision agriculture; Alternaria; Nematodes; Phytophthora; digital transformation

Share and Cite

MDPI and ACS Style

Kalampokas, T.; Vrochidou, E.; Mavridou, E.; Iliadis, L.; Voglitsis, D.; Michalopoulou, M.; Broufas, G.; Papakostas, G.A. Empowering Kiwifruit Cultivation with AI: Leaf Disease Recognition Using AgriVision-Kiwi Open Dataset. Electronics 2025, 14, 1705. https://doi.org/10.3390/electronics14091705

AMA Style

Kalampokas T, Vrochidou E, Mavridou E, Iliadis L, Voglitsis D, Michalopoulou M, Broufas G, Papakostas GA. Empowering Kiwifruit Cultivation with AI: Leaf Disease Recognition Using AgriVision-Kiwi Open Dataset. Electronics. 2025; 14(9):1705. https://doi.org/10.3390/electronics14091705

Chicago/Turabian Style

Kalampokas, Theofanis, Eleni Vrochidou, Efthimia Mavridou, Lazaros Iliadis, Dionisis Voglitsis, Maria Michalopoulou, George Broufas, and George A. Papakostas. 2025. "Empowering Kiwifruit Cultivation with AI: Leaf Disease Recognition Using AgriVision-Kiwi Open Dataset" Electronics 14, no. 9: 1705. https://doi.org/10.3390/electronics14091705

APA Style

Kalampokas, T., Vrochidou, E., Mavridou, E., Iliadis, L., Voglitsis, D., Michalopoulou, M., Broufas, G., & Papakostas, G. A. (2025). Empowering Kiwifruit Cultivation with AI: Leaf Disease Recognition Using AgriVision-Kiwi Open Dataset. Electronics, 14(9), 1705. https://doi.org/10.3390/electronics14091705

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