Application of Neural Networks and Image Visualization for Early Forecast of Apple Yield

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.