The challenge in perennial fruit cultivation is estimating the number and diameter of fruit on a tree as early as possible to achieve yield estimates for farm operations, fruit trade, retailers and storage facilities. Apple recognition algorithms based on colour features are presented to estimate the number of apple fruits and develop early predicting models of apple yield. Fifty cv. ‘Gala’ apple digital images were captured twice on one, the preferred Western side of the tree row with a variability of between 70 and 170 fruit per tree, under natural daylight conditions at Bonn, Germany. Several image processing algorithms and fruit counting algorithms were used to analyse the apple images in both periods. Finally, an apple recognition algorithm with colour difference R-B and G-R was developed for apple images after June drop, and two different colour models were used to segment the ripening period’s apple images. The algorithm was tested on 50 images in each period. Close correlation coefficients R² of 0.80 and 0.85 were obtained for two developmental periods between apples detected by the fruit counting algorithm and those manually counted. Two sets of data in each period were used for modelling yield prediction of the apple fruits. In the calibration data set, the R² values between apples detected by the fruit counting algorithm and actual harvested yield are from 0.57 for young fruit after June drop to 0.70 in the fruit ripening period. In the validation data set, the R² value between the number of apples predicted by the model and actual yield at harvest from 0.58 to 0.71. The proposed model shows a great potential for early predicting yield for individual trees in an orchard. The present results on apple may be applicable to many other fruit crops like Citrus, pear, peach, apricot, kaki, nectarine and almond.