Modelling apple fruit yield using image analysis for fruit colour, shape and texture

Abstract

Summary This work describes a new, computerised vision-based model to estimate the diameter and number of apple fruit on a tree and hence its yield autonomously under natural weather conditions in a fruit orchard. A charge-coupled device (CCD) camera acquired images of cvs 'Golden Delicious' and 'Jonagold' apple ( Malus domestica L. Borkh.) trees seven times in the vegetation period, i.e. every two weeks from June to September 2007 for modelling the tree volume, fruit diameter and yield at harvest time. Images were processed off-line using image analysis for fruit colour, shape and texture. The fruit detection algorithm was successfully tested on trees bearing from 15 to 42 apple fruits and missed or mis-classified 1 to 3 apple fruits per tree. Fruit detection was sufficiently accurate with an 89% rate and an overall error rate of 2.2%. Fruit diameter was underestimated at the beginning of fruit growth in June, but the data corresponded closely (R 2 =0.96) with orchard measurements from July onwards, which enabled accurate modelling of the expected yield per tree. The proposed autonomous model hence has a large potential for forecasting the yield in June/July of harvest in September/October initially of apple in Europe and in the Northern hemisphere.