Optimizing The Fertility Rate of Sugarcane Crops at Precision Agriculture Using The Fuzzy Logic Method

Abstract

Soil fertility has a significant role in the sugarcane plantation industry to maintain plant fertility so that optimal yield productivity is obtained. The management system that has been used by farmers only based on practices and estimation, so that it can not determine the exact needs of water, lime and fertilizers in each area of the plant. Therefore, we need a system that are able to provide a reference for giving water volume, lime content and fertilization according to the level of nutritional needs of sugarcane plants. This study aims to design a system that is able to provide recommendations for sugarcane needs, based on soil nutrient content using the fuzzy logic method. The first step in this method is the fuzzification process carried out on four types of data used as input parameters, namely soil moisture, soil pH, plant phase, and nutrient content. The next step is choosing the relevant criteria from each assessment to get best alternative. The next stage, a membership function is created to estimate the next process and defuzzification process. According to the result of the study found the value of cost efficiency, optimization of growth in stem height and plant tillers. The resulting cost efficiency is 30.05% compared to the factory method. While the level of optimization of plant growth compared to the factory method, tillering growth increased 8% but the growth of primary stem height was higher by the factory method of 3%.