16. Fine-tuning and testing of a deep learning algorithm for pruning regions detection in spur-pruned grapevines

In: Precision agriculture '21
Authors:
P. Guadagna Università Cattolica del Sacro Cuore, Via E. Parmense 84, 29122 Piacenza, Italy.

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T. Frioni Università Cattolica del Sacro Cuore, Via E. Parmense 84, 29122 Piacenza, Italy.

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F. Chen Istituto Italiano di Tecnologia, Via Morego 30, 16163 Genova, Italy.

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A. Incerti Delmonte Istituto Italiano di Tecnologia, Via Morego 30, 16163 Genova, Italy.

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T. Teng Università Cattolica del Sacro Cuore, Via E. Parmense 84, 29122 Piacenza, Italy.
Istituto Italiano di Tecnologia, Via Morego 30, 16163 Genova, Italy.

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M. Fernandes Istituto Italiano di Tecnologia, Via Morego 30, 16163 Genova, Italy.

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A. Scaldaferri Istituto Italiano di Tecnologia, Via Morego 30, 16163 Genova, Italy.

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C. Semini Istituto Italiano di Tecnologia, Via Morego 30, 16163 Genova, Italy.

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S. Poni Università Cattolica del Sacro Cuore, Via E. Parmense 84, 29122 Piacenza, Italy.

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M. Gatti Università Cattolica del Sacro Cuore, Via E. Parmense 84, 29122 Piacenza, Italy.

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Type:
Chapter
Pages:
147–153
DOI:
https://doi.org/10.3920/978-90-8686-916-9_16

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Vineyard management cost is significantly affected by selective operations such as winter pruning. This study aimed to fine-tune and test a deep neural network (DNN) based algorithm for detecting pruning regions. Focusing on spur-pruned grapevines, in order to fine tune the DNN, around 1000 RGB images were acquired and pruning target regions ground-truthed. The DNN was tested on 5 vines, 232 frames were acquired and processed in real-time for identifying the regions of interest as potential pruning regions (PPRs). PPRs were then classified depending on wood type, orientation and visibility. True positives (TPs), false positives (FPs) and false negatives (FNs) were identified in each frame. Best detection performance was obtained for visible coplanar simple spurs (recall = 98%) while the recall index was generally lower than 60% when pruning regions (PRs) were not clearly visible. FPs were more frequently associated with old cuts located on permanent organs such as trunks and cordons.
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Cover Precision agriculture '21
E-Book ISBN:
9789086869169
Publisher:
Wageningen Academic
Print Publication Date:
19 Jul 2021
Front Matter
Section 1. – Precision agriculture
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Section 2. – Precision horticulture
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Section 3. – Precision viticulture
Section 4. – Precision crop protection
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Section 5. – Proximal and remote sensing of soil and crop
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Section 6. – Applications of unmanned aerial systems
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Section 7. – Satellite-based applications for precision agriculture
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Section 8. – Site-specific nutrient, lime and seed management
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Section 9. – Drainage optimization and variable rate irrigation
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Section 10. – Geostatistics, mapping and spatial data analysis
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Section 11. – Robotics, guidance and automation
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Section 12. – Adoption of precision agriculture
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Section 13. – Education in precision agriculture
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Section 14. – On-farm experimentation with site-specific technologies
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Section 15. – Software and mobile apps for precision agriculture
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Section 16. – Decision support for precision agriculture
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Section 17. – Miscellaneous
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Back Matter

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