8. Multi species weed detection with Retinanet one-step network in a maize field

In: Precision agriculture '21
Authors:
J.M. López Correa CSIC, Centre for Automation and Robotics, Spanish National Research Council, CSIC-UPM, Ctra. M300 Campo Real, Km 0.200, Arganda del Rey, 28500 Madrid, Spain.
DHARMa, Artificial Intelligence Laboratory, UTN, Coronel Rodríguez 273, Ciudad – Capital, M5500 Mendoza, Argentina.

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M. Todeschini CSIC, Centre for Automation and Robotics, Spanish National Research Council, CSIC-UPM, Ctra. M300 Campo Real, Km 0.200, Arganda del Rey, 28500 Madrid, Spain.

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D.S. Pérez DHARMa, Artificial Intelligence Laboratory, UTN, Coronel Rodríguez 273, Ciudad – Capital, M5500 Mendoza, Argentina.

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J. Karouta CSIC, Centre for Automation and Robotics, Spanish National Research Council, CSIC-UPM, Ctra. M300 Campo Real, Km 0.200, Arganda del Rey, 28500 Madrid, Spain.

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F. Bromberg CSIC, Centre for Automation and Robotics, Spanish National Research Council, CSIC-UPM, Ctra. M300 Campo Real, Km 0.200, Arganda del Rey, 28500 Madrid, Spain.

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A. Ribeiro CSIC, Centre for Automation and Robotics, Spanish National Research Council, CSIC-UPM, Ctra. M300 Campo Real, Km 0.200, Arganda del Rey, 28500 Madrid, Spain.

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D. Andújar CSIC, Centre for Automation and Robotics, Spanish National Research Council, CSIC-UPM, Ctra. M300 Campo Real, Km 0.200, Arganda del Rey, 28500 Madrid, Spain.

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Type:
Chapter
Pages:
79–86
DOI:
https://doi.org/10.3920/978-90-8686-916-9_8

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Weed density and composition are not uniform throughout the field, nevertheless, the conventional approach is to carry out a uniform application. Object Detection Networks have already arrived in agricultural applications that can be used for weed management. The current study developed a detection and classification of weeds system in a one-step procedure using RetinaNet Object Detection Network. The procedure was based on identifying Solanum nigrum L., Cyperus rotundus L. and Echinochloa crus-galli L. and two growth stages both for a broadleaf species (S. nigrum) as well as narrow-leaved species (C. rotundus) in a maize field. The predictions were evaluated by mAP metric. The result obtained was 0.88 with values between 0.98 and 0.75 depending on the class.
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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
1. Consideration of resilience for digital farming systems
2. Leaf area index estimation in maize breeding trials from RGB imagery and machine learning algorithms
3. A simple web-based tool for optimizing nitrogen variable rate application in durum wheat
4. Evaluation of different crop model-based approaches for variable rate nitrogen fertilization in winter wheat
5. A farm scale evaluation of variable rate application (VRA) for improving liquid digestate agronomic performances
6. Real-time fuzzy system for variable rate nitrogen application based on multiple parameters
7. Methodology for comparison between uniform and variable rate application in a drip-irrigated peach orchard
9. Influence on rooting intensity and nutrient utilization of maize at different plant spacings
10. Modelling of in-field dynamic response of a centrifugal spreader to variable rate application
11. Yield measurement of wilted forage and silage maize with forage harvesters
Section 2. – Precision horticulture
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Section 3. – Precision viticulture
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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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