Increased efficiency of Smart Water Systems for Vegetable Plants Using the Deep Learning Classification Approach

Wireless Sensor Network (WSN) can be considered as a rapidly developing technology that can be used to control and manage tasks with many nodes owned. Smart farmers are one of the most important WSN applications in helping farmers carry out many tasks including irrigation, farming, and harvesting. Many farmers still do manual irrigation and they do repetitive work, wasting energy and water costs. In agriculture, costs and energy used are important things to consider. This study will maximize smart watering systems by monitoring soil moisture using deep learning classifications to classify soil conditions for plants that are sensitive to available water. This means, the plants used as the object of this study, are plants that cannot receive a little water or a lot of water to grow such as cabbage, carrots and spinach. Based on this water system simulation, the results provide a classification of water volume and good soil moisture conditions using a droplet water system that allows plants to grow well.