Surveillance and steering of irrigation system in cloud using Wireless Sensor Network and Wi-Fi module

Internet of Things (IoT) has become very popular in the field of communication. IoT will become a reality over the next few years, with rapid and widespread smart devices will be able to execute independently according to the change in their surroundings. The use IoT techniques to identify and channelize the irrigation methods are discussed in this work. In order to develop an automated technique to analyze the water required by the plants at a particular time, a database is created. The database contains training samples related to the soil type, moisture content, temperature, plant leaf condition and the humidity level and the amount of water flow required for subsequent set of features. Different type of soils and plants differ in water content required. The database is build using the features extracted from the images of the soil and the plants. The color of the plants and the soil images can be retrieved using Fisher's linear discriminant analysis (LDA), The Fisher's LDA approach is used to project the plant and soil RGB images into the database to retrieve the color indicating the moisture level. The RGB images are needed to be analyzed accurately to determine the water requirement. However, color processing has great advantages for its simplicity, robustness, power and efficiency. The graph cut based segmentation can be implemented to segment the regions accurately and using this technique the water content in the leaf and the soil covering the plants can be differentiated. The processing of the images should be performed by differentiating the plant and soil segments. A novel classification algorithm called transductive support vector machine (TSVM) is used for classification and quantification. Thus, the required irrigation level of the plants can be determined and the process could be carried out in an efficient manner.