Preliminary study for weed biomass prediction combining visible images with a plant-growth model

In: Precision agriculture '19
Authors:
J. Merienne Agroécologie, AgroSup Dijon, INRA, Univ. Bourgogne, Univ. Bourgogne Franche-Comté, 21000 Dijon, France.

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A. Larmure Agroécologie, AgroSup Dijon, INRA, Univ. Bourgogne, Univ. Bourgogne Franche-Comté, 21000 Dijon, France.

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C. Gée Agroécologie, AgroSup Dijon, INRA, Univ. Bourgogne, Univ. Bourgogne Franche-Comté, 21000 Dijon, France.

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Type:
Chapter
Pages:
597–603
DOI:
https://doi.org/10.3920/978-90-8686-888-9_74

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This preliminary study was designed to test an image approach (RGB images) to estimate crop and weed aerial biomass (BM) and predict their growths. From an image processing, based on crop/weed discrimination, a calibration was done between photographed leaf area (PLA) and BM deduced from a standard (destructive) approach. Then, a plant growth model component (AZODYN) was used with PLA as input, considering wheat and weeds separately. For wheat, the results confirm that to initialize the model, the image approach may replace the standard one with no impact on the values of predicted BM whereas weed results are more mixed.

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Cover Precision agriculture '19
E-Book ISBN:
9789086868889
Publisher:
Wageningen Academic
Print Publication Date:
08 Jul 2019
Front Matter
Section 1 – Precision agriculture
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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 – Site-specific canopy and harvest quality
Section 10 – Precision pasture management
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Section 11 – Drainage optimization and variable rate irrigation
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Section 12 – Geostatistics, mapping and spatial data analysis
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Section 13 – Robotics, guidance and automation
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Section 14 – Economics of precision agriculture
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Section 15 – Adoption of precision agriculture
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Section 16 – Small farms/small holders and precision agriculture
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Section 17 – On farm experimentation with site-specific technologies
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Section 18 – Software and mobile apps for precision agriculture
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Section 19 – Decision support for precision agriculture
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Section 20 – Data mining for precision agriculture
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Back Matter

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