FIELD: Fuzzy Inference for Efficient Learner Decision in Agronomic Task

Reliable prediction models for smart agricultural system needs methods that stay stable, easy to understand, and can be reproduced across different sites, seasons, and cultivars. FIELD is a compact procedure for selecting a regression model that highlights stability and practicality, not only raw accuracy. The method combines six different performance metrics for describing the effectiveness of each model. It measures the average absolute error on an external test set with truly unseen data. It checks how concentrated or spread out those test errors are to guarantee consistency across the modelling case. It records the total time needed to fit the model and make predictions, so efficient models are valued. It looks at cross validation and considers how much the losses change from split to split and what the average normalized error across folds is. It also records how spread out that cross validation error is. After cleaning and standardizing the data, FIELD trains five families of regressors, namely a linear model, a quadratic polynomial, a decision tree, a Gaussian process, and a small feed forward neural network. Each performance metric is mapped to a common normalized scale and is then combined with a Mamdani fuzzy inference system whose rule base is short and easy to audit, using simple if-then statements. Final scores are ranked to select the best one, and if two models tie the more interpretable one is preferred. The proposed procedure increases the stability of model selection without long and complex hyperparameter searches. It is practical for agronomic datasets of moderate size, with mixed signal to noise ratios and with seasonal shifts, because its robustness to changes in site, season, or cultivar is an explicit goal and increases the model transparency and explainability.