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 t...
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-02-01
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| Series: | Agronomy |
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| Online Access: | https://www.mdpi.com/2073-4395/11/2/347 |
| _version_ | 1827589233939841024 |
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| author | Anand Koirala Kerry B. Walsh Zhenglin Wang |
| author_facet | Anand Koirala Kerry B. Walsh Zhenglin Wang |
| author_sort | Anand Koirala |
| collection | DOAJ |
| description | 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 (<i>n</i> = 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<sup>2</sup> 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. |
| first_indexed | 2024-03-09T00:51:44Z |
| format | Article |
| id | doaj.art-a1bd7fbf973c4b6c94598801869a8481 |
| institution | Directory Open Access Journal |
| issn | 2073-4395 |
| language | English |
| last_indexed | 2024-03-09T00:51:44Z |
| publishDate | 2021-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Agronomy |
| spelling | doaj.art-a1bd7fbf973c4b6c94598801869a84812023-12-11T17:08:22ZengMDPI AGAgronomy2073-43952021-02-0111234710.3390/agronomy11020347Attempting to Estimate the Unseen—Correction for Occluded Fruit in Tree Fruit Load Estimation by Machine Vision with Deep LearningAnand Koirala0Kerry B. Walsh1Zhenglin Wang2Institute for Future Farming Systems, Central Queensland University, Building 361, Bruce Highway, Rockhampton, QLD 4701, AustraliaInstitute for Future Farming Systems, Central Queensland University, Building 361, Bruce Highway, Rockhampton, QLD 4701, AustraliaInstitute for Future Farming Systems, Central Queensland University, Building 361, Bruce Highway, Rockhampton, QLD 4701, AustraliaMachine 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 (<i>n</i> = 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<sup>2</sup> 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.https://www.mdpi.com/2073-4395/11/2/347fruit occlusiondeep learningmachine visionyield estimationfruit countneural network |
| spellingShingle | Anand Koirala Kerry B. Walsh Zhenglin Wang Attempting to Estimate the Unseen—Correction for Occluded Fruit in Tree Fruit Load Estimation by Machine Vision with Deep Learning Agronomy fruit occlusion deep learning machine vision yield estimation fruit count neural network |
| title | Attempting to Estimate the Unseen—Correction for Occluded Fruit in Tree Fruit Load Estimation by Machine Vision with Deep Learning |
| title_full | Attempting to Estimate the Unseen—Correction for Occluded Fruit in Tree Fruit Load Estimation by Machine Vision with Deep Learning |
| title_fullStr | Attempting to Estimate the Unseen—Correction for Occluded Fruit in Tree Fruit Load Estimation by Machine Vision with Deep Learning |
| title_full_unstemmed | Attempting to Estimate the Unseen—Correction for Occluded Fruit in Tree Fruit Load Estimation by Machine Vision with Deep Learning |
| title_short | Attempting to Estimate the Unseen—Correction for Occluded Fruit in Tree Fruit Load Estimation by Machine Vision with Deep Learning |
| title_sort | attempting to estimate the unseen correction for occluded fruit in tree fruit load estimation by machine vision with deep learning |
| topic | fruit occlusion deep learning machine vision yield estimation fruit count neural network |
| url | https://www.mdpi.com/2073-4395/11/2/347 |
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