Self-adaptive incremental learning-based real-time smart drip irrigation management using IoT and mobile application

Water scarcity and inefficient irrigation practices pose significant challenges to modern agriculture. Traditional irrigation systems often result in water wastage and suboptimal crop yields due to the lack of real-time monitoring and adaptive control. This paper presents an innovative drip irrigation system that leverages LoRaWAN (Long Range Wide Area Network) technology, sensors, actuators, and machine learning algorithms to address these challenges. Data from different sensors are collected by an Arduino Mega, transmitted wirelessly via LoRaWAN to an ESP32 while maintaining a specific bandwidth for uploading to a central MySQL database. The data are accessed through a custom Flutter mobile application, ensuring a platform independent, user friendly system integrating efficient hardware, software design. Our system introduces an Internet-less sensing and actuating architecture that supports deployment in remote locations where internet is not avilable, along with a modular design that enables seamless addition and removal of nodes. It also offers both automatic and manual modes, allowing users to control irrigation remotely based on real-time environmental conditions. Moreover the developed system is designed to operate in rural and remote areas with limited access to electricity. In addition to real-time monitoring, it integrates a machine learning model KNN (KNeighborsClassifier) by choosing among AdaBoost (Adaptive Boosting), Logistic Regression, SVC (Support Vector Classifier), and GBC (Gradient Boosting Classifier) based on their performance metrics and trained on a Kaggle-derived dataset with an accuracy of 95%. This model helps farmers predict and optimize irrigation schedules, ensuring optimal water usage and reducing waste. The proposed solution is scalable, adaptable to various types of crops, and can be customized for fields of different sizes, making it a versatile tool for modern, sustainable agriculture.