Developing a Hybrid Irrigation System for Smart Agriculture Using IoT Sensors and Machine Learning in Sri Ganganagar, Rajasthan

The agriculture sector is one of the largest consumers of fresh water. Different types of irrigation systems are available, including center pivot, drip and sprinkler systems, and linear motion systems. However, the complex structure of existing irrigation systems and their high maintenance costs encourage Indian farmers to continue using these methods. Due to its ease of use and low energy consumption, surface irrigation is one of the most popular irrigation techniques. Although the main reasons for poor irrigation application efficiency are uneven irrigation water distribution and deep absorption, using a variety of technologies, countries are trying to increase the sustainability of agriculture. Automated irrigation systems contribute significantly to water conservation. The combination of automation and Internet of Things (IoT) improves agricultural practices. These technologies help farmers understand their crops, minimize their impact on the environment, and preserve resources. They also enable efficient monitoring of the weather, water resources, and soil. This research proposes an intelligent, low-cost field irrigation system. The proposed prototype can measure soil moisture, rain status, wind speed, water level, temperature, and humidity using a hardware sensor and unit. To decide whether to turn on or off the motor, a variety of sensors are used to get a range of readings and conclusions. They enable automatic watering when soil moisture levels are below a certain threshold, and if soil moisture is equal to the required moisture, then the irrigation process stops. Every few minutes, the sensors measure the environmental factors. Data are collected and stored on a ThingSpeak cloud server for analysis. To evaluate the data we collected, we used a variety of models, such as K-nearest neighbors (KNN), Naive Bayes, random forest, and logistic regression. Compared to other Naive Bayes and random forest models, the accuracy rate was 98.8%, the mean square error was 0.16, and the results of logistic regression, KNN, and SVM were in order: (98.3%/1.66), (99.3%/0.66), and (99.5%/0.5), respectively. In the end, an automated irrigation system run on IoT applications gives farmers access to remote monitoring and control, as well as information about the specifics of the irrigation field.