Extended Fuzzy Decision Support Model for Cropland Recommendation of Food Cropping in Indonesia

Abstract

Food crops are the preferred crops to be cultivated on agricultural land in Indonesia, which has a wide area available for use as agricultural land. Each region's agricultural lands in Indonesia have distinct features (e.g., water capacity, land porosity, land height, etc.). Rice, maize, red beans and green beans are significant food crops that are commonly cultivated in Indonesia. The goal of this research is to develop an extended Fuzzy logic-based Decision Support Model (FDSM). The model is able to propose the best food crops to plant on a certain plot of land or location. These suggestions are based on the worth of the distance between the plants as well as the unique worth of the land or location. These suggestions are based on the worth of the distance between the plants as well as the unique worth of the land or location. The primary methods employed in this study are fuzzy logic and Euclidean distance measuring. The fuzzy logic method is used on purpose to prevent confusing values and to consider parameter values based on human linguistics. The fitness value is then calculated using the Euclidean distance value as a basic value for choice suggestions. In this follow-up study, three parameters from previous research were included, for a total of eleven factors (water availability, temperature, humidity, land height, land slope, rainfall, land porosity, rocks on the surface, flood potential and water acidity level). biological food plants in search of the best value based on eight different types of food crops, the model generates simulation suggestions for 514 administrative areas in 34 districts or cities in Indonesia (rice, maize, peanuts, soybeans, green beans, sweet potatoes, cassava and wheat).

The Multistage Fuzzy Logic-Based Decision Support Model to Determine the Salary of New Employees

“…FL is frequently used when there is ambiguity, inaccuracy, or other difficulty (Chen et al, 2021). The FL method correctly combines computer and human language to bring out the meaning of a value (Chen et al, 2021). The degree value in the membership function contributes to bringing out this meaning.…”

Prediction of Soil Quality in Rwanda for Ideal Cultivation of Potato (Solanum tuberosum) Using Fuzzy Logic and Machine Learning

“…Similarly, various other work was conducted to assess various problems that directly relate to assessment of soil quality; the studies included averaging method for soil properties [21], forecasting carbon in soil using multiple sets of machine learning [22], yield prediction [23], assessing effectiveness of machine learning [24], growth prediction using deep learning [25], assessing CO2 fluxes [26], prediction of texture [27], reliability forecasting model [28], and carbon variability assessment [29]. A fuzzy-logic-based approach for assessing soil quality has also been investigated by Ogunleye et al [30], Nooriman et al [31], Hoseini [32], Chen et al [33], and Atijosan et al [34]. However, the accuracy of the model largely depends on the massive size of the training data, which is sometimes unavailable.…”