Attempting to Estimate the Unseen-Correction for Occluded Fruit in Tree Fruit Load Estimation by Machine Vision with Deep Learning

Machine vision from ground vehicles is being used for estimation of fruit load on trees, but a correction is required for occlusion by foliage or other fruits. This requires a manually estimated factor (the reference method). It was hypothesised that canopy images could hold information related to the number of occluded fruits. Several image features, such as the proportion of fruit that were partly occluded, were used in training Random forest and multi-layered perceptron (MLP) models for estimation of a correction factor per tree. In another approach, deep learning convolutional neural networks (CNNs) were directly trained against harvest count of fruit per tree. A R-2 of 0.98 (n = 98 trees) was achieved for the correlation of fruit count predicted by a Random forest model and the ground truth fruit count, compared to a R-2 of 0.68 for the reference method. Error on prediction of whole orchard (880 trees) fruit load compared to packhouse count was 1.6% for the MLP model and 13.6% for the reference method. However, the performance of these models on data of another season was at best equivalent and generally poorer than the reference method. This result indicates that training on one season of data was insufficient for the development of a robust model.

Automated summary

Machine vision from ground vehicles is used to estimate fruit load on trees, but a correction is needed for occlusion. Various methods, such as Random forest and multi-layered perceptron (MLP) models, were used for estimation. Results show good accuracy for predicting fruit count, but the models performed poorly on data from a different season compared to the reference method.