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Agronomy
Volume 16
Issue 11
10.3390/agronomy16111094
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This is an early access version, the complete PDF, HTML, and XML versions will be available soon.
Article

Harnessing AI for Precision Agriculture: An Integrated System for Vineyard Pathogen and Pest Detection

by
Ioana-Diana Petre
1,*,
Ionuț Șandric
2,
Diana Elena Vizitiu
3,
Ionela-Daniela Sărdărescu
3,
Cristian Ioniță
1,
Marian Dardală
1 and
Simona Bacău
2,4
1
Faculty of Economic Cybernetics, Statistics and Informatics, Bucharest University of Economic Studies, 010374 Bucharest, Romania
2
Faculty of Geography, University of Bucharest, 010041 Bucharest, Romania
3
National Research and Development Institute for Biotechnology in Horticulture Ștefănești, 117715 Stefanesti, Romania
4
ESRI Romania, 011793 Bucharest, Romania
*
Author to whom correspondence should be addressed.
Agronomy 2026, 16(11), 1094; https://doi.org/10.3390/agronomy16111094
Submission received: 2 March 2026 / Revised: 3 April 2026 / Accepted: 8 April 2026 / Published: 31 May 2026
(This article belongs to the Section Pest and Disease Management)
Versions Notes

Abstract

Vineyards are affected by pathogens globally. Some of the most damaging pathogens are Uncinula necator, Plasmopara viticola, and thrips, which affect the plant entirely and threaten the health and productivity of vineyards. To control the emergence and spread of pathogens, early detection is essential. Studies to date focus on visual or molecular detection of pathogens but are limited in terms of scalability, labor intensity, need for equipment and expertise. To tackle these limitations, we propose the early detection of grapevine virus infections using Convolutional Neural Networks on both RGB and thermal infrared imagery captured via a smartphone and an FLIR sensor. To do so, we employ a four-step workflow where we first acquire nearly 500 images detecting symptoms of pathogens, which we then crop in smaller tiles. Then, we use the ArcGIS Train Deep Learning Model tool trained with Single Shot Detector and RetinaNet frameworks to detect image areas showing pathogen presence. Finally, we calculate the IoU score to compare precisions between different tile sizes and frameworks. The results demonstrate that pathogen detection using these models is highly effective, with most images having a IoU score above 0.7. Moreover, 30% of images score a precision of 1.0. The consequences of these findings highlight the importance of early detection of pathogens to better understand their spread and effects on vineyards, which finally contribute to proposing effective management measures.
Keywords: convolutional neural networks; deep learning; grapevine diseases; image processing; precision agriculture convolutional neural networks; deep learning; grapevine diseases; image processing; precision agriculture

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MDPI and ACS Style

Petre, I.-D.; Șandric, I.; Vizitiu, D.E.; Sărdărescu, I.-D.; Ioniță, C.; Dardală, M.; Bacău, S. Harnessing AI for Precision Agriculture: An Integrated System for Vineyard Pathogen and Pest Detection. Agronomy 2026, 16, 1094. https://doi.org/10.3390/agronomy16111094

AMA Style

Petre I-D, Șandric I, Vizitiu DE, Sărdărescu I-D, Ioniță C, Dardală M, Bacău S. Harnessing AI for Precision Agriculture: An Integrated System for Vineyard Pathogen and Pest Detection. Agronomy. 2026; 16(11):1094. https://doi.org/10.3390/agronomy16111094

Chicago/Turabian Style

Petre, Ioana-Diana, Ionuț Șandric, Diana Elena Vizitiu, Ionela-Daniela Sărdărescu, Cristian Ioniță, Marian Dardală, and Simona Bacău. 2026. "Harnessing AI for Precision Agriculture: An Integrated System for Vineyard Pathogen and Pest Detection" Agronomy 16, no. 11: 1094. https://doi.org/10.3390/agronomy16111094

APA Style

Petre, I.-D., Șandric, I., Vizitiu, D. E., Sărdărescu, I.-D., Ioniță, C., Dardală, M., & Bacău, S. (2026). Harnessing AI for Precision Agriculture: An Integrated System for Vineyard Pathogen and Pest Detection. Agronomy, 16(11), 1094. https://doi.org/10.3390/agronomy16111094

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