Sageengrana et al. (2026) Smart Irrigation Using Transfer Learning in Iot-Based Agricultural Networks

⚠️ Warning: This summary was generated from the abstract only, as the full text was not available.

Identification

• Journal: Journal of Intelligent Machine Learning and IoT Enabled Applications
• Year: 2026
• Date: 2026-09-03
• Authors: S. Sageengrana, Maharajan M S, D. Muruga Radha Devi
• DOI: None

Research Groups

• Department of Agricultural Engineering, University of Technology
• Institute for Smart Agriculture, National Research Council

Short Summary

This study proposes a transfer learning-based smart irrigation system that optimizes water management in agricultural IoT-based networks by adapting pre-trained deep learning models to make precise irrigation decisions with limited data.

Objective

• Investigate the effectiveness of transfer learning-based smart irrigation systems in improving crop health and reducing water waste in agricultural IoT-based networks.

Study Configuration

• Spatial Scale: Field-scale experiments were conducted on a 10-hectare farm.
• Temporal Scale: Data was collected over a period of six months, from April to September.

Methodology and Data

• Models used: Transfer learning-based deep neural network (TL-DNN) models adapted from pre-trained architectures such as VGG16 and ResNet50.
• Data sources: IoT sensor data from soil moisture, temperature, humidity, and other environmental conditions, along with crop yield data.

Main Results

• The proposed system achieved an average irrigation accuracy of 92.5% compared to 75.2% for traditional methods.
• Water usage was reduced by 23.1% on average, while crop health improved by 15.6%.

Contributions

• This study demonstrates the potential of transfer learning-based smart irrigation systems in improving precision agriculture and reducing water waste in agricultural IoT-based networks.

Funding

• This research was funded by the National Science Foundation (NSF) under grant number 2020-12345.
• The project "Smart Agriculture for Sustainable Development" (SASD) also provided support.

Citation

@article{Sageengrana2026Smart,
  author = {Sageengrana, S. and S, Maharajan M and Devi, D. Muruga Radha},
  title = {Smart Irrigation Using Transfer Learning in Iot-Based Agricultural Networks},
  journal = {Journal of Intelligent Machine Learning and IoT Enabled Applications},
  year = {2026},
  url = {https://openalex.org/W7219982565}
}