A smart irrigation monitoring service using wireless sensor networks
Downloads
Published
DOI:
https://doi.org/10.58414/SCIENTIFICTEMPER.2023.14.4.44Keywords:
Smart irrigation, Soil moisture, Crop yields, IoT, Zigbee protocol.Dimensions Badge
Issue
Section
License
Copyright (c) 2023 The Scientific Temper

This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
The present research uses wireless sensor networks (WSN) to create a smart watering system. The system’s ability to perform real-time monitoring and management of irrigation makes sure that crops get the right quantity of water depending on their unique needs. The suggested method boosts agricultural yields, decreases labor costs, and improves water usage efficiency. The system uses a field-deployed network of inexpensive wireless sensors to track the soil moisture levels in real time. The central controller utilizes the wirelessly sent sensor data to decide when and how much water should be applied to the crops. Utilizing wireless protocols like Zigbee, these nodes connect to a central gateway, where the data is processed and examined to establish the ideal watering needs for each crop. The technology is scalable and simple to install in larger agricultural fields. The study’s findings indicate that the system can boost crop yields by up to 30% while boosting water usage efficiency by up to 60%. Farmers may decrease their water use, save time and money, and enhance their profitability by adopting the smart irrigation monitoring service powered by WSN.Abstract
How to Cite
Downloads
Similar Articles
- S K Bairagi, Ram Chandra, R P Singh, Effect of Different Phosphorus and Potassium Levels on a Seed Crop of French Bean (Phaseolus vulgaris L.) , The Scientific Temper: Vol. 12 No. 1&2 (2021): The Scientific Temper
- Teklu Hailu, Regasa Begna , Pre-extension demonstration of inter-cropping of improved forages with food and cash crops at Semen Bench Woreda, Southwest Ethiopia , The Scientific Temper: Vol. 15 No. 02 (2024): The Scientific Temper
- Shantanu Kanade, Anuradha Kanade, Secure degree attestation and traceability verification based on zero trust using QP-DSA and RD-ECC , The Scientific Temper: Vol. 15 No. spl-2 (2024): The Scientific Temper
- Shahala Sheikh, Lalsingh Khalsa, Nitin Chandel, Vinod Varghese, Hygrothermoelastic large deflection behaviour in a thin circular plate with non-Fourier and non-Fick law , The Scientific Temper: Vol. 15 No. 02 (2024): The Scientific Temper
- S. Munawara Banu, M. Mohamed Surputheen, M. Rajakumar, Bio-Inspired and Machine Learning-Driven Multipath Routing Protocol for MANETs Using Predictive Link Analytics , The Scientific Temper: Vol. 16 No. 10 (2025): The Scientific Temper
- Aditi Sahariya, Chellapilla Bharadwaj, Iwuala Emmanuel, Afroz Alam, Phytochemical Profiling and GCMS Analysis of Two Different Varieties of Barley (Hordeum vulgare L.) Under Fluoride Stress , The Scientific Temper: Vol. 12 No. 1&2 (2021): The Scientific Temper
- Tewoderos Legesse, Bekelech Sharew, Evaluation of white seeded sesame (Sesamum indicium L.) genotypes on growth and yield performance in Menit Goldya Woreda of West Omo Zone, SWE , The Scientific Temper: Vol. 15 No. 02 (2024): The Scientific Temper
- Parul Yadav, Priyanka Suryavanshi, Storage study on compositional analysis of quinoa and ragi based snacks , The Scientific Temper: Vol. 15 No. 02 (2024): The Scientific Temper
- Ashish Nagila, Abhishek K Mishra, The effectiveness of machine learning and image processing in detecting plant leaf disease , The Scientific Temper: Vol. 14 No. 01 (2023): The Scientific Temper
- G. Tripathi, Impact of Nanomaterials on Earthwoms : A New Threat to Megadrili Resources , The Scientific Temper: Vol. 11 No. 1&2 (2020): The Scientific Temper
<< < 5 6 7 8 9 10 11 12 13 14 > >>
You may also start an advanced similarity search for this article.

