Neural Modelling for the Analysis of Changes in Selected Features of Plant Products
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The work investigates possibilities of plant products quality assessment by means of neural networks. A quick method of plant products assessment was proposed based on the correlations occurring between selected features of plant products and neural modelling. This approach facilitates sustainable agricultural production, which often requires making decisions based on approximate but quick assessment of the quality of produced or processed products. The method of quality assessment is presented using changes in the features of pumpkin being dried as an example. Changes in selected features of chemical composition and colour were analysed, including correlations between them. Initial analysis involved cluster analysis, which allowed for grouping data into cases characterized by similar quality. Based on the analysis, a neural model was developed, which, based on easily obtainable features, allowed for classification of products according to their quality features. This approach was positively verified based on the results of chemical composition and quality assessment performed using statistical analysis of data.
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