A Deep Learning-Driven Computer Vision and Web-Based Monitoring System for Automated Nutrient Management in Soilless Agriculture

Eda Nur Urtekin; Emre Dandıl

doi:10.15832/ankutbd.1817471

A Deep Learning-Driven Computer Vision and Web-Based Monitoring System for Automated Nutrient Management in Soilless Agriculture

Abstract

Sustainable food production increasingly relies on intelligent automation technologies that optimize resource usage while maintaining crop health. This study presents a deep learning-driven computer vision and web based monitoring system designed for automated nutrient management in soilless (hydroponic) agriculture. The proposed system integrates environmental sensors and a Raspberry Pi-based control unit to continuously monitor plant growth parameters and manage nutrient solutions through real-time feedback. A novel strawberry leaf image dataset was constructed to detect visual symptoms of nutrient deficiencies such as chlorosis and tip burn. Three convolutional neural network (CNN) architectures such as ResNet-18, MobileNetV2, and DenseNet-121 were trained and evaluated to classify these deficiencies. Among them, MobileNetV2 achieved the highest performance with an overall test accuracy of 0.9967, demonstrating superior generalization across classes. In addition, Grad-CAM visualizations confirmed that the model accurately localized key symptom regions on the leaves, enhancing interpretability and trustworthiness. A React-based web interface was developed to enable remote visualization of system data, sensor readings, and nutrient levels, allowing real-time monitoring and manual intervention when necessary. The proposed system effectively integrates artificial intelligence, computer vision, and IoT-based monitoring to provide an efficient, automated, and sustainable solution for modern hydroponic farming.

Keywords

Supporting Institution

This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

Ethical Statement

This study did not involve human or animal subjects; therefore, ethics committee approval was not required.

Thanks

We would like to thank the producers (…………………….) in …………….. who contributed to the creation of the dataset developed for the detection of strawberry diseases in this study.

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Details

Primary Language

English

Subjects

Artificial Intelligence (Other), Agricultural Automatization

Journal Section

Research Article

Authors

Eda Nur Urtekin
0009-0008-5490-4706
Türkiye

Emre Dandıl ^*
0000-0001-6559-1399
Türkiye

Publication Date

July 28, 2026

Submission Date

November 4, 2025

Acceptance Date

February 16, 2026

Published in Issue

Year 2026 Volume: 32 Number: 3

DOI

https://doi.org/10.15832/ankutbd.1817471

IZ

https://izlik.org/JA65LJ75TK

Cite

RIS / Bibtex

APA

Urtekin, E. N., & Dandıl, E. (2026). A Deep Learning-Driven Computer Vision and Web-Based Monitoring System for Automated Nutrient Management in Soilless Agriculture. Journal of Agricultural Sciences, 32(3), 645-668. https://doi.org/10.15832/ankutbd.1817471

AMA

1.Urtekin EN, Dandıl E. A Deep Learning-Driven Computer Vision and Web-Based Monitoring System for Automated Nutrient Management in Soilless Agriculture. J Agr Sci-Tarim Bili. 2026;32(3):645-668. doi:10.15832/ankutbd.1817471

Chicago

Urtekin, Eda Nur, and Emre Dandıl. 2026. “A Deep Learning-Driven Computer Vision and Web-Based Monitoring System for Automated Nutrient Management in Soilless Agriculture”. Journal of Agricultural Sciences 32 (3): 645-68. https://doi.org/10.15832/ankutbd.1817471.

EndNote

Urtekin EN, Dandıl E (July 1, 2026) A Deep Learning-Driven Computer Vision and Web-Based Monitoring System for Automated Nutrient Management in Soilless Agriculture. Journal of Agricultural Sciences 32 3 645–668.

IEEE

[1]E. N. Urtekin and E. Dandıl, “A Deep Learning-Driven Computer Vision and Web-Based Monitoring System for Automated Nutrient Management in Soilless Agriculture”, J Agr Sci-Tarim Bili, vol. 32, no. 3, pp. 645–668, July 2026, doi: 10.15832/ankutbd.1817471.

ISNAD

Urtekin, Eda Nur - Dandıl, Emre. “A Deep Learning-Driven Computer Vision and Web-Based Monitoring System for Automated Nutrient Management in Soilless Agriculture”. Journal of Agricultural Sciences 32/3 (July 1, 2026): 645-668. https://doi.org/10.15832/ankutbd.1817471.

JAMA

1.Urtekin EN, Dandıl E. A Deep Learning-Driven Computer Vision and Web-Based Monitoring System for Automated Nutrient Management in Soilless Agriculture. J Agr Sci-Tarim Bili. 2026;32:645–668.

MLA

Urtekin, Eda Nur, and Emre Dandıl. “A Deep Learning-Driven Computer Vision and Web-Based Monitoring System for Automated Nutrient Management in Soilless Agriculture”. Journal of Agricultural Sciences, vol. 32, no. 3, July 2026, pp. 645-68, doi:10.15832/ankutbd.1817471.

Vancouver

1.Eda Nur Urtekin, Emre Dandıl. A Deep Learning-Driven Computer Vision and Web-Based Monitoring System for Automated Nutrient Management in Soilless Agriculture. J Agr Sci-Tarim Bili. 2026 Jul. 1;32(3):645-68. doi:10.15832/ankutbd.1817471