Читать книгу Agricultural Informatics - Группа авторов - Страница 16

Research experiments analyzed parameters for predicting crop yield. The study used aggressive neural network for prediction. Remote sensing was one among the used parameters for the work [7, 8]. The experiments implemented the flexible Neuro-fuzzy Inference system (ANFIS). The inputs to the ANFIS were moisture content available in soil, biomass information of the ground and repository organ. Yield was the output node for result. Limited data was used for designing the network to predict values for future. This is the challenge in prediction. Rearrangement of data was done by eliminating one year and using the remaining data. The estimation deviation was calculated and compared to the yield of the year that is left out. The procedure was applied recursively to all the years and averaged efficiency for prediction was obtained.

Experiments used Refs. [10–12] as stated in Figure 1.2. Hellenic sugar Industry used FINKNN for sugar production forecast based on the population of assessment. FINKNN is studied as K-nearest neighbor classifier that performs over the metric lattice of traditional convex fuzzy set. Results proved that FINKNN showed improved results for efficiency in forecasting.

Figure 1.2 Fuzzy cluster membership function representation in various field [9].