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The unwanted soil collected during the harvesting of sugarbeets (soil tare) represents one of main by-products of the sugar industry in Europe. This solid waste causes problems in terms of production costs (soil tare could represent 15-20% of the transported product to the processing plant) and environmental impact (around 2 Mt/y of soil waste must be disposed in landfills by 20 processing plants in Italy).

In the last few years, various studies have stressed that the factors affecting the soil tare are multiple and dependant on the nature of the soil, the machines utilised to handle the product and the meteorological course.

In this study, we utilize different Neural Network predictors to power an online optimization scheme for the harvesting season. The aim of the research is to better understand the relationship between the soil tare in the sugarbeet industry and the meteorological course during the harvest and post harvest period by means of conventional statistical and neural networks analysis.

The research was carried out on the basis of hundreds of data relevant to loads of beets (30 t/each in average) processed in a sugarbeet industry operating in Italy (Ancona province) during the last few years. The model obtained could be useful to better program all the harvesting and post harvesting operations before the industrial processing. Simulated results show significant economical and ecological advantages.