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

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      Agronomy
      MDPI AG

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          Abstract

          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 R2 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 R2 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.

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                Author and article information

                Contributors
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                Journal
                ABSGGL
                Agronomy
                Agronomy
                MDPI AG
                2073-4395
                February 2021
                February 15 2021
                : 11
                : 2
                : 347
                Article
                10.3390/agronomy11020347
                a8b94e92-2363-401c-9f93-2316c9de3aa8
                © 2021

                https://creativecommons.org/licenses/by/4.0/

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                Self URI (article page): https://www.mdpi.com/2073-4395/11/2/347

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