Toward Big Data Manipulation for Grape Harvest Time Prediction by Intervals' Numbers Techniques

Abstract

The automation of agricultural production calls for accurate prediction of the harvest time. Our interest in particular here is in grape harvest time. Nevertheless, the latter prediction is not trivial also due to the scale of data involved. We propose a novel neural network architecture that processes whole histograms induced from digital images. A histogram is represented by an Intervals' Number (IN); hence, all-order data statistics are represented. In conclusion, the proposed IN Neural Network, or INNN for short, emerges with the capacity of predicting an IN from past INs. We demonstrate a proof-of-concept, preliminary application on a time series of digital images of grapes taken during their growth to maturity. Compared to a conventional Back Propagation Neural Network (BPNN), the results by INNN are superior not only in terms of prediction accuracy but also because the BPNN predicts only first-order data statistics, whereas the INNN predicts all-order data statistics.