Detection of green apples in hyperspectral images of apple-tree foliage using machine vision

It is important for orchard owners to be able to estimate the quantity of fruit on the trees at the various growth stages, because a tree that bears too many fruits will yield small fruits. Thus, if growers are interested in controlling the fruit size, knowing in advance that there are too many developing fruits will give them the opportunity to treat the tree. This study proposes a machine vision-based method of automating the yield estimation of apples on trees at different stages of their growth. Since one of the most difficult aspects of apple yield estimation is distinguishing between green varieties of apples or those that are green in the first stages of growth, and the green leaves that surround them, this investigation concentrates on estimating the yield of green varieties of apples. Hyperspectral imaging was used, because it is capable of giving a wealth of information both in the visible and the near-infrared (NIR) regions and thus offers the potential to provide useful results. A multistage algorithm was developed that uses several techniques, such as principle components analysis (PCA) and extraction and classification of homogenous objects (ECHO) for analyzing hyperspectral data, as well as machine vision techniques such as morphological operations, watershed, and blob analysis. The method developed was tested on images taken in a Golden Delicious apple orchard in the Golan Heights, Israel, in two sessions: one during the first stages of growth, and the second just before harvest. The overall correct detection rate was 88.1%, with an overall error rate of 14.1%.