Agricultural Yield Prediction by Difference Equations on Data-Induced Cumulative Possibility Distributions

Accurate prediction of agricultural yield is important also toward timely engaging the resources necessary for harvest. Even more informative, challenging though, than predicting a single number is predicting a distribution regarding an agricultural yield (random) variable such as fruit weight. Cumulative distribution functions are often elusive in practice, moreover they could be nonstationary. Nevertheless, estimates of cumulative distribution functions can be induced from data samples at a sampling time. This work interprets an aforementioned estimate as a cumulative possibility distribution, which is represented by an Intervals’ Number (IN) based on the resolution identity theorem of fuzzy set theory. The orientation of this work is toward real-world applications. Optimizable parametric difference equations, defined in the metric cone of lattice-ordered INs, are proposed toward predicting an IN from past INs. Computational experiments are carried out on data collected from vineyards in northern Greece. Preliminary application results demonstrate, comparatively, the capacity of the proposed method. Future work extensions are discussed.