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Early information on yield has a special importance in the intensive apple production. Since the majority of older forecast methods are labor, time, organization and cost intensive a hybrid model based on image analysis and neural network was developed. From the end of fruit thinning in June till harvesting digital images of 120 trees of yellow-skin ‘Golden Delicious’ (four times) and 120 trees of red-skin ‘Braeburn’ (five times) were captured from intensive orchards. Firstly, each image was processed by image analysis algorithm to receive the data on number of fruits and a yield forecast, for each sampling period separately, which served as the input information for modeling the yield with the artificial neural network (ANN). The forecast of the hybrid method showed a higher accuracy than the image analysis for both varieties, since the new procedure managed to increase the correlation between the forecasted and weighed yield from 0.73 to 0.83 for ‘Golden Delicious’ and from 0.51 to 0.78 for ‘Braeburn’. The standard deviation/image was decreased from 4.79 to 2.83 kg for ‘Golden Delicious’ and from 3.64 to 2.55 kg for ‘Braeburn’. To introduce the new method in practice, additional tests on various locations including all important apple varieties are recommended.

Sustainable agriculture encompasses economic feasibility, social acceptance, and conservation of the environment. The three agricultural systems (conventional, integrated and organic) are currently in the focus of debate of sustainable agriculture. This paper presents a qualitative multi-attribute model, based on DEX-I methodology, for the assessment of sustainability of agricultural systems at a field level. The data for the implementation was based on field trial. In an overall assessment of the sustainability assessment outcomes the model ranked agricultural systems in the order: organic > integrated > conventional agricultural system. The model gives the decision makers the possibility to assess their decision.