https://doi.org/10.1051/epjconf/202636704011
Web Application of Convolutional Neural Networks with YOLOv8 for Early Detection of Diseases in Strawberry Crops
Faculty of Business Sciences, Professional Career of Business Systems Engineering,Scientific University of the South, Lima, Peru
* Corresponding author: This email address is being protected from spambots. You need JavaScript enabled to view it.
Published online: 29 April 2026
Abstract
In this research, we address a critical problem for strawberry growers in Lima: the management of phytosanitary diseases. Traditionally, these farmers relied on time-consuming, imprecise, and error-prone visual observation methods, resulting in annual production losses of 49.44%. We developed a comprehensive technological system based on convolutional neural networks (CNNs) using the YOLOv8 architecture, specifically designed to identify diseases such as powdery mildew, anthracnose, and gray mold, representing a significant shift toward precision agriculture methodologies. Our research was applied, with a quasi-experimental design and a quantitative approach. We worked with 474 high-resolution images of strawberry crops from 38 producers in Manchay Alto, Pachacamac district. Statistical analysis using SPSS version 27 with the Wilcoxon signed-rank test revealed statistically significant results (p = 0.000), achieving a very good technical accuracy of 96.74% (mAP@50) and remarkable system effectiveness, with 84.4% of cases reaching a high level. The system demonstrated superior performance compared to traditional inspection methods, facilitating timely disease detection and accurate diagnoses. Agronomic validation by local experts confirmed 91- 94% accuracy for four locally present diseases, while identifying systematic false positives for three diseases not present under Lima’s specific microclimatic conditions, revealing a critical gap between international training datasets and local disease prevalence that has significant implications for agricultural AI deployment in diverse agroclimatic regions.
Key words: convolutional neural networks / YOLOv8 / strawberry crops / disease detection / computer vision / precision agriculture / domain adaptation
© The Authors, published by EDP Sciences, 2026
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
