Artificial intelligence-based agriculture automated monitoring systems using WSN

In this current era, the influence of AI (Artificial Intelligence) is becoming vital for many unsolved problems and to make intelligent solutions. This paper represents the potential of AI in the field of analyzing and implementing the intelligence in agriculture automation using the data collected from the WSN (Wireless Sensor Network) technology. This could help in making improved intelligent decisions. The application of WSN includes collecting, accounting, and analyzing data, which can be used for the process of monitoring the agriculture and its automation inhabitant activities. The method of agriculture automation includes sensors that can be able to measure the humidity, moisture, pressure in the atmosphere, PH level in the water or soil, and more. Enhancing the AI with the help of machine learning algorithm to enable intelligence in the automation will conserve many natural resources such as the consumption of the water, quality of soil/his intelligence will help the agriculturist in many ways. Here various machine-learning algorithms (Artificial Neural Networks-ANN) are tested for selecting a rightful systematic architecture for the process. In this work, it is found that the ANN named GRNN (Generalized Regression Neural Network) is best suited. Through these algorithmic formations, the system can able to produce 95% accuracy when compared to other systems. By using this automated system water is saved of up to 92% and produce a good yield compared with old irrigation systems.

Automated summary

In this paper, the potential of AI in agriculture automation is explored using data collected from Wireless Sensor Network technology. By enhancing AI with machine learning algorithms, intelligent decision-making can be achieved, leading to resource savings and increased yields for farmers. Through various machine learning algorithms, the best suited ANN named GRNN is identified, resulting in a system with 95% accuracy and up to 92% water savings compared to traditional irrigation systems.