Artificial Intelligence for Sustainable Agriculture: Early Disease Detection in Vineyards

DOI

10.2991/978-94-6239-666-1_12How to use a DOI?

Keywords

Artificial Neural Networks (ANN); Convolutional Neural Networks (CNN); Grape Diseases; Deep Learning; Image Processing

Abstract

This study addresses the role of artificial neural networks (ANNs) and their advanced version, Convolutional Neural Networks (CNNs), in the early diagnosis of diseases in agriculture and their application potential in vineyards. The time-consuming and costly nature of traditional methods has accelerated the adoption of ANN and especially CNN technologies in agricultural applications. Deep learning-based models have achieved accuracy rates exceeding 95% by detecting disease symptoms on grape leaves, offering early diagnosis capabilities.

The effective use of methods such as Convolutional Neural Networks (CNNs), Transfer Learning, and YOLO in real-time applications has provided fast and accurate detection capabilities in field conditions. These technologies facilitate the early detection of diseases, reducing the use of chemical pesticides and contributing to environmental sustainability. However, challenges such as data scarcity, the development of models resistant to field conditions, and hardware costs limit the widespread adoption of these technologies.

The effective use of convolutional neural networks in early disease detection in vineyards represents an important tool for increasing the efficiency of agricultural processes and reducing environmental impacts. It is expected that these technologies will find a wider application area in agriculture with larger data sets and optimized algorithms.

Copyright

© 2026 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Deniz Uğur Güzel
PY  - 2026
DA  - 2026/05/07
TI  - Artificial Intelligence for Sustainable Agriculture: Early Disease Detection in Vineyards
BT  - Proceedings of the 5th International Conference on Research of Agricultural and Food Technologies (I-CRAFT 2025)
PB  - Atlantis Press
SP  - 107
EP  - 124
SN  - 3005-155X
UR  - https://doi.org/10.2991/978-94-6239-666-1_12
DO  - 10.2991/978-94-6239-666-1_12
ID  - Güzel2026
ER  -