22. Grape yield spatial variability assessment using YOLOv4 object detection algorithm

In: Precision agriculture '21
Authors:
M. Sozzi University of Padova, Dept. Land Environment Agriculture and Forestry (TeSAF), Viale dell’Università 16, 35020 Legnaro (PD), Italy.

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S. Cantalamessa University of Teramo, Faculty of Bioscience and Agro-Food and Environmental Technology, via R. Balzarini 1, 64100 Teramo (TE), Italy.

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A. Cogato University of Padova, Dept. Land Environment Agriculture and Forestry (TeSAF), Viale dell’Università 16, 35020 Legnaro (PD), Italy.

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A. Kayad University of Padova, Dept. Land Environment Agriculture and Forestry (TeSAF), Viale dell’Università 16, 35020 Legnaro (PD), Italy.

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F. Marinello University of Padova, Dept. Land Environment Agriculture and Forestry (TeSAF), Viale dell’Università 16, 35020 Legnaro (PD), Italy.

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Type:
Chapter
Pages:
193–198
DOI:
https://doi.org/10.3920/978-90-8686-916-9_22

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Over the last few years, several versions of the machine learning algorithm, YOLO, have been developed, improving its performance. In this study, the last official version of YOLO (v4) was evaluated, to assess grape yield spatial variability. YOLO models were used to classify 24 georeferenced RGB images on an 8 ha vineyard. The models were used to detect the number of bunches, based on different resolution images (320-1,280 pixels) and different confidence thresholds (0.25-0.35). The detected number of bunches was then compared with the actual ones and with the relative final weight harvested from the vines used as a target for the collected images by correlation. According to the results, the best linear regression model for vines yield was obtained with 416 pixels images, which showed an R2 of 0.59, indicating YOLO as a suitable tool for detecting yield spatial variability.

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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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