Developing a Smart integrated Model for an Autonomous Irrigation System Design Based on K-Nearest Neighbour (KNN) by Leveraging the Tools and Techniques of Machine Learning (ML) and Internet of Things (IoT)
Ishant Sangwan
Class X student, Venkateshwar Global School
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Efficient irrigation is pivotal for water conservation and agricultural sustainability. This paper describes the design, implementation, and evaluation of a low-cost, real-time smart irrigation system combining Internet of Things (IoT) hardware with a K-Nearest Neighbors (KNN) machine learning model. Soil moisture and environmental data are collected via capacitive sensors, DHT22 modules, and ESP8266 nodes. Collected data are processed locally to decide irrigation actions every 10 minutes. Field trials conducted over four weeks on a 100 m² vegetable plot compared KNN’s performance with Random Forest (RF) and Support Vector Machine (SVM). KNN achieved 78 % accuracy, moderate precision (0.75) and recall (0.80), outperforming some literature reports, while RF scored 82 % and SVM 76 %. Comparative analysis highlights KNN’s low computational overhead, simplicity, and adequate performance for small-scale applications. Cost and energy analysis suggest the KNN-ESP8266 system is affordable (~USD 50) and energy-efficient, making it well-suited for resource-constrained environments. Limitations include sensitivity to noisy sensor data and absence of weather forecasting integration. Future work will focus on adaptive K selection, cloud-edge orchestration, and integration of dynamic weather inputs to improve precision and scalability.
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