Water Management for IoT-Based Smart Agriculture Using Machine Learning Algorithms

Agriculture relies substantially on water. The IoT-based farm water management system explains how IoT technology can control irrigation intelligently. This system may be used to collect data by connecting several sensors such as soil moisture, temperature, and PIR motion sensors, among others. The collected data will be transferred to the user's mobile phone and sent to the users, ensuring crops receive the appropriate quantity of water and optimize irrigation schedules. By monitoring water levels, farmers may detect possible water loss locations and take action to prevent them. The primary goal of our article is to increase productivity, efficiency, and water use through live monitoring, water management, crop growth development, and the potential use of virtual water. Furthermore, breakthroughs in machine learning, such as crop prediction and recommendation, fertilizer detection, and leaf disease detection, will allow for more complex analysis of agricultural data, resulting in even great gains in water management and crop yield. However, it is important to ensure that these technologies are accessible to all farmers, regardless of the location or resources. If giving detailed information on soil properties and crop genetics, machine learning algorithms can forecast agricultural yields and find the best crops for a giving region. Now, farmers can maximize their crops by making data-driven decisions. This one system’s capability to exactly predict crops yields and resources requirements be one of its key advantages. To promote more sustainable and productive farming, farmers should also control the quantity of water and other resources used by them. By assuring a sufficient supply and reducing the likelihood of crops failure, crops forecast, leaf disease diagnosis, and guidance systems can assist address concern related to global food securities in addition to enhancing productivity.