Microcontroller-Based Smart Irrigation System with AI-Powered Decision Logic

Abstract

Farming also needs good water management to increase food production to meet the global population needs with the minimal usage of freshwater. The traditional irrigation method usually results in the wastage of water and unpredictable crop results because it is performed manually and without being environmentally sensitive. The present paper provides a microcontroller-driven artificial intelligence (AI)-enabled smart irrigation system that can use the AI-enabled decision logic to deliver the water adaptively and precisely in real-time. The system takes advantage of a blend of cheap sensors to permit constant observation of ground moisture, temperature, humidity, and rainfall conditions. Field data is fed to an ESP32 microcontroller at the place of harvest where a low-end machine learning model based on the current environmental conditions, the specific crop, and the past irrigation standards are used to estimate the current watering needs of the crop. Thanks to the application of AI model deployed with the help of TensorFlow Lite for Microcontrollers, decision-making is conducted offline at the edge of the network, significantly contributing to uninterrupted operation with poor or even no connectivity in rural regions. The energy-efficient solenoid valves controlled by relays are used to realize actuation with precise application of well-regulated water also when necessary. A testbed was used to demonstrate field trials on lettuce and tomato crops, where the water usage was greatly reduced up to 38 percent and stability of soil moisture was enhanced in addition to 12 percent more crop yield compared to the traditional timer-driven irrigation. Real-time remote monitoring, analytics, and adaptive learning can also happen through the cloud-based logging system through Firebase. The proposed solution is scalable, robust, and sustainable; hence appropriate in the implementation in small and medium-sized farms. It can discard its reliance on manual operation and make intelligent and autonomous irrigation choices with the help of inbuilt AI and create the ability to make intelligent choices in precision agriculture. The study reveals the potential and advantages of coupling microcontroller-based control systems with edge AI in agricultural systems that will lead to inexpensive yet smart irrigation technologies that can be replicated in different climatic and soil circumstances to achieve food security and resource preservation.