Arduino An IoT based Circuit Diagram AI-based smart irrigation systems can reduce energy consumption by applying precise amounts of water, detecting leaks, and adjusting watering schedules based on water demand and weather prediction. This leads to lower operating costs and substantial energy savings. the existing concept of automatic drip irrigation system. Deepak Sinwar et al. 2019 [3] presented different techniques and applications of Artificial Intelligence for yield prediction and smart irrigation. Sunil Kumar et al. 2019 [4] talk about intelligent IOT based smart irrigation devices and the process of their development. Pravina B A Smart Irrigation System Using the IoT and Advanced Machine Learning Model- A Systematic Literature Review Ponugoti Kalpana 1* , L. Smitha 2 , Dasari Madhavi 3 , Shaik Abdul Nabi 4 , G. Kalpana 5
D.2 Prototype design for irrigation system. The comprehensive design of the hardware configuration for the solar-powered irrigation system is presented in Fig 10. It incorporates both Fuzzy logic and Average value methods to autonomously monitor and determine the water requirements for crops, and subsequently irrigate them.
From Sensors to Smart Decisions: Building an Intelligent Irrigation ... Circuit Diagram
AI-driven irrigation systems should be designed with economic considerations in mind including duration of design process, as this can influence final product cost. Training and Support To help farmers effectively use and maintain smart irrigation systems, training materials and real-time support should be made available. These sentiments apply
An intelligent irrigation system, powered by Machine Learning (ML) and Internet of Things (IoT), can significantly optimize water usage and enhance crop production. This research proposes such a system, using IoT devices to collect real-time soil and environmental data. ML algorithms analyze these data to provide real-time irrigation recommendations. Using GSM technology, the system sends a message to the farmer's mobile phone, providing information about the crop's readiness status. Based on market demand, crop readiness, and other quality Implementing intelligent irrigation and adjusting the irrigation system is essential in today's agricultural system to control the amount of water required for the plant. This study focuses on data obtained from sensors measuring (soil temperature and humidity, temperature and humidity of ambient and light) and image processing of plant leaves. To analyze the data, two models were
