Title: Hybrid irrigation system using YOLO V3 algorithm

Authors: Manisha Rajesh Mhetre

Addresses: Department of Instrumentation and Control Engineering, Vishwakarma Institute of Technology, Pune, India

Abstract: Today's farmers generally lack the necessary expertise of farming and agriculture, which makes it more irregular. The majority of farming and agricultural activities utilise prediction and forecasting. When it fails, the farmers must bear heavy losses, and some even commit themselves. We cannot ignore variables like temperature and moisture since we understand how important irrigation, good soil, air quality, and agricultural growth are. Farmers can benefit from sensor and monitor technologies to help them more accurately and continuously monitor crop quality. We developed a smart approach that can make 50% of a decision based on ontology, while the other 50% is dependent on the sensor data values. A machine learning method is then used to examine the resulting decision. Between the ESP8266 module and the primary internet of things server, an edge server is also deployed. By using this method, the IoT server's task will be reduced, and the system's performance will be enhanced. Users can quickly connect to and examine the data gathered by various sensors. The key benefit of this strategy is that it enables consumers to easily visualise the study's findings.

Keywords: CNN; field parameters; IoT; ontology; web application; Yolo V3.

DOI: 10.1504/IJAITG.2024.147899

International Journal of Agriculture Innovation, Technology and Globalisation, 2024 Vol.4 No.4, pp.384 - 399

Received: 11 Dec 2023
Accepted: 12 Feb 2024
Published online: 07 Aug 2025 *

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