Dataset Macro & Micro IOT Anfis

The dataset used in this study contains real-time nutrient measurements of sugarcane plants collected through an IoT sensor system. The data includes variables such as temperature, soil moisture, pH, electrical conductivity, as well as macronutrient (N, P, K) and micronutrient (Zn, Mn, Fe) concentrations. This dataset is provided in a CSV format, which is machine-readable and ready for analysis. The source code for the Adaptive Neuro-Fuzzy Inference System (ANFIS) model is hosted on GitHub and is used to estimate nutrient requirements for sugarcane plants. Written in MATLAB, this code involves regression based on ANFIS to model the nonlinear relationships between soil conditions, environmental factors, and nutrient demands. The code also includes preprocessing of raw data, training the ANFIS model, and evaluating the prediction results using performance metrics such as MAE, RMSE, and R². The uploaded files consist of the raw dataset (dataset.csv) and the MATLAB code for the ANFIS model (dataset.m), which is available via the GitHub URL. This dataset and code are intended for precision agriculture research aimed at optimizing fertilization strategies based on sensor data. The source code can be used to replicate the model and conduct further analysis on nutrient data for crops using an ANFIS-based AI approach.