Soil Moisture Prediction using Fuzzy Time Series and Moisture sensor Technology on Shallot Farming

Abstract

This research conducted by predicting soil moisture using Fuzzy Time Series (FTS) and soil moisture sensor technology on shallot farming. Well-controlled soil moisture affects the shallots and crops growth. It discusses soil moisture prediction and monitoring systems developed through Android-based mobile programming languages. Input data consists of sensor results obtained from automatic, online, and real-time acquisition using soil moisture sensor technology, then, sent to the server and stored in an online database. Furthermore, data acquisition is predicted using the FTS algorithm that applies a discourse universe to define and determine fuzzy sets. Fuzzy set results are continued to the process of sharing the discourse universe so that it becomes the final step. Prediction results are displayed on the information system dashboard developed. Using 24 data from soil moisture data, the predicted score is 760 at the beginning of 6:00. The results of the prediction are done by validating error deviations using the Mean Square Error of 1.5%. This proves that FTS is good enough in predicting soil moisture and safety to control soil moisture in shallots. For deeper analysis, researchers used various request data and U discourse universe at FTS to obtain various results based on the test data used.