IoT Enabled WSN and Machine Learning Techniques to Surveillance the Smart Irrigation System
Keywords:
IoT, Machine Learning Techniques, Smart Irrigation, Smart FarmingAbstract
Farmers try to follow their experience in farming. They will not be aware of the changes that happens in the environment that directly affect the profit as well as the outcome from the farm field. The adoption of smart technologies in farming helps the farmers to utilize resources efficiently. The important resource of farming is water. The consumption of water should be frugal enough to regulate the water supply. The incompetent way of irrigation results in water wastage. Therefore, implementing IoT-enabled WSN techniques allows to moderate the irrigation system known as smart farming or smart agriculture. The smart irrigation system helps to efficiently use the water resource in farming. Sensors are deployed to sense environmental conditions such as temperature, humidity and soil moisture content. The continuous sensed data will be processed and sent to farmers. Based on the data received, farmers decide the water supply to the farm field. Injecting false or incorrect data results in overwatering or under-watering that affects the yield of the farm field directly. Detection of such false data is accomplished using two phases of classification. The two phases of classifications implemented in the proposed scheme are machine learning techniques and fault detection algorithm (FDA). The data that are classified as non-anomalous from the first phase of classification are subjected to the second phase that is FDA. This two-phase of classification concludes that the data received are non-anomalous. This entire classification process is done at the base station (BS). The data that are detected as anomalous either at the first phase or at the second phase are dropped directly without considering for further process. The data that are processed completely at BS will be forwarded to the farmers of the particular farm field.
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