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Ecoregional application of the LINTUL-POTATO model

Comparison between the choices five-level criteria and nutri-score

The current food environment in The Netherlands is considered obesogenic. Eighty percent of the products in supermarkets are unhealthy. The Wheel of Five is the well-established, science-based Dutch food-based dietary guideline (FBDG) developed to stimulate healthier choices. In addition, simple directions on food packaging, such as front-of-package (FOP) health logos, could also be helpful. However, these tools should be in line with each other, in order not to confuse the consumer. To study this, we evaluated two FOP nutrient profiling systems (NPSs) for their alignment with the Wheel of Five: Choices five-level criteria and Nutri-Score. For this, a small but representative sample of 124 products was selected from the Dutch food composition database (NEVO). For these products, the scores for Choices and Nutri-Score were calculated using the published criteria, whilecompliance with the Wheel of Five was established by using the criteria from Netherlands Nutrition Center (NNC). The Wheel of Five food groups were used to categorize the products. Differences between the Wheel of Five and Choices are smaller than with Nutri-Score, concluding that Choices ismore consistent with the Wheel of Five and might be an attractive alternation for a FOP health logo on the Dutch market.

Description of the LINTUL-POTATO crop growth model

Hyper-spectral frequency selection for the classification of vegetation diseases

Reducing the use of pesticides by early visual detection of diseases in precision agriculture is important. Because of the color similarity between potato-plant diseases, narrow band hyper-spectral imaging is required. Payload constraints on unmanned aerial vehicles require reduc- tion of spectral bands. Therefore, we present a methodology for per-patch classification combined with hyper-spectral band selection. In controlled experiments performed on a set of individual leaves, we measure the performance of five classifiers and three dimensionality-reduction methods with three patch sizes. With the best-performing classifier an error rate of 1.5% is achieved for distinguishing two important potato-plant diseases.

Agro-ecological description of LINTUL-POTATO