Disentangling the sources of chlorophyll-content variability in banana fields

In: Precision agriculture '19
Authors:
J. Lamour Compagnie Fruitière, Marseille 13000, France.
UMR ITAP, Montpellier SupAgro, Irstea, Univ. Montpellier, France.

Search for other papers by J. Lamour in
Current site
Google Scholar
PubMed Close
,
C. Leroux UMR ITAP, Montpellier SupAgro, Irstea, Univ. Montpellier, France.
SMAG, Montpellier 34960, France.

Search for other papers by C. Leroux in
Current site
Google Scholar
PubMed Close
,
G. Le Moguédec INRA, UMR AMAP, Montpellier 34000, France.

Search for other papers by G. Le Moguédec in
Current site
Google Scholar
PubMed Close
,
O. Naud UMR ITAP, Montpellier SupAgro, Irstea, Univ. Montpellier, France.

Search for other papers by O. Naud in
Current site
Google Scholar
PubMed Close
,
M. Léchaudel CIRAD, UMR QualiSud, 97130 Capesterre-Belle-Eau, Guadeloupe, France.
QualiSud, Univ Montpellier, CIRAD, Montpellier SupAgro, Univ d’Avignon, Univ de La Réunion, Montpellier, France.

Search for other papers by M. Léchaudel in
Current site
Google Scholar
PubMed Close
, and
B. Tisseyre UMR ITAP, Montpellier SupAgro, Irstea, Univ. Montpellier, France.

Search for other papers by B. Tisseyre in
Current site
Google Scholar
PubMed Close
Type:
Chapter
Pages:
299–305
DOI:
https://doi.org/10.3920/978-90-8686-888-9_37

Purchase instant access (PDF download and unlimited online access):

$40.00
Add to Cart
Redeem Access Token

The objective of this study was to evaluate the effect of banana phenological stages on the chlorophyll content of its leaves in order to assess possible restrictions on the potential use of remote sensing for nutritional diagnoses when phenological stages cannot be discriminated on images. Chlorophyll measurements were made with a Dualex sensor at different positions on banana leaves with different ranks on the plant, on plants at different phenological stages and located in different fields of a commercial farm. The effect of these factors was studied using mixed models with nested factors and the results showed that they have a significant effect on the chlorophyll content. It is therefore suggested to take into account the diversity of phenological stages in banana fields before considering nutritional diagnoses services based on remote sensing.

  • Collapse
  • Expand
Cover Precision agriculture '19
E-Book ISBN:
9789086868889
Publisher:
Wageningen Academic
Print Publication Date:
08 Jul 2019
Front Matter
Section 1 – Precision agriculture
Show Items
Section 2 – Precision horticulture
Show Items
Section 3 – Precision viticulture
Show Items
Section 4 – Precision crop protection
Show Items
Section 5 – Proximal and remote sensing of soil and crop
Design of a portable sensor suite for real-time monitoring of crop water stress index in maize breeding plots
Characterisation of fungal diseases on winter wheat crop using proximal and remote multispectral imaging
Assessing infield temporal and spatial variability of leaf water potential using satellite imagery and meteorological data
Characterisation of cereal morphology through proximal stereo vision under contrasting nitrogen inputs
Early detection of corn and sunflower stress induced by chemical spraying
Hyperspectral imaging application under field conditions: assessment of the spatio-temporal variability of grape composition within a vineyard
Potential of optical sensors for nitrogen management in spring barley
Sentinel-2 vegetation indices and apparent electrical conductivity to predict barley (Hordeum vulgare L.) yield
Accessing the plant architecture in 3D for plant phenotyping - recent approaches and requirements
Vegetation indices from remote sensing imagery as proxies for yield and grain N in wheat
Using terrestrial photogrammetry for leaf area estimation in maize under different plant growth stages
Early detection of Fusarium infection in corn using spectral analysis
Proximal versus remote sensing to monitor pasture quality in a Mediterranean Montado ecosystem
Section 6 – Applications of unmanned aerial systems
Show Items
Section 7 – Satellite-based applications for precision agriculture
Show Items
Section 8 – Site-specific nutrient, lime and seed management
Show Items
Section 9 – Site-specific canopy and harvest quality
Show Items
Section 10 – Precision pasture management
Show Items
Section 11 – Drainage optimization and variable rate irrigation
Show Items
Section 12 – Geostatistics, mapping and spatial data analysis
Show Items
Section 13 – Robotics, guidance and automation
Show Items
Section 14 – Economics of precision agriculture
Show Items
Section 15 – Adoption of precision agriculture
Show Items
Section 16 – Small farms/small holders and precision agriculture
Show Items
Section 17 – On farm experimentation with site-specific technologies
Show Items
Section 18 – Software and mobile apps for precision agriculture
Show Items
Section 19 – Decision support for precision agriculture
Show Items
Section 20 – Data mining for precision agriculture
Show Items
Back Matter

Metrics

All Time Past 365 days Past 30 Days
Abstract Views 62 51 10
Full Text Views 2 2 0
PDF Views & Downloads 1 0 0