Methods for assessing quality of data coming from high spatial and temporal resolution observations : the case of decision making in viticulture
- Starting date: November 2017
- University: MUSE Montpellier Université d’Excellence / Institut Agro
- PhD school: GAIA, Montpellier
- Scientific field: Agronomy, ICT in agriculture
- Thesis management: Véronique Bellon-Maurel (Inrae, UMR Itap)
- Thesis supervisors: Bruno Tisseyre (Institut Agro, Itap), James Taylor (Newcastle University, UK / Inrae, Itap)
- Funding: Institut Agro
- #DigitAg : Labeled PhD – Axes 3, 4 et 6 – Challenges 1,et 6
Keywords: Data quality, High resolution data, Precision viticulture, Decision support, Crowdsourcing
Abstract: Development of new technologies (UAV, Sentinel satellite, smartphones, etc.) has fostered the rise of new sources of information in agriculture. These technologies were mostly designed for other business sectors but still represent potential opportunities for being a support to expertise and decision making in agriculture. However, the data coming from these new sources of information have varied characteristics in nature, quality, and spatial, temporal or spectral resolution and extent. These data characteristics are not necessarily suited to the use as a support for expertise and decision-making. In that respect, evaluating the interest of a new source of observation considering its specific characteristics is a strong operational issue raising methodological scientific questions. This issue should be reinforced in the coming years with the emergence of new technologies with their own characteristics (hyperspectral imagery, nano-satellites, Terahertz, etc.). In precision agriculture, many papers study the characteristics of measurement systems and optimal acquisition conditions for a dedicated and specific application (characterization of the variability of yield, vigor, water state, etc.). However, to our knowledge, there is no existing method to evaluate the interest of a new source of observation with given characteristics. The thesis will answer this challenge by proposing methods allowing i) to define the interest and the optimal mode of representation of a new source of spatial observation, ii) to evaluate the quality of a spatial data used as a decision support (iii) to improve the quality of spatial data in the particular case of a new source of observations: crowdsourcing.
Jury compound:
- Marianne CERF, Directrice de recherche, INRAE, France
- Romain JULLIARD, Directeur de recherche, CNRS-MNHN, France
- Cornelis VAN LEEUWEN, Professeur, Bordeaux Sciences Agro, France
- Véronique BELLON-MAUREL, IPEF, INRAE, France
- Bruno TISSEYRE, Professeur, Institut Agro – Montpellier SupAgro, France
Contact : leo.pichon@supagro.fr
Social networks: ResearchGate – LinkedIn
Communications & Papers:
Articles in peer-reviewed journals
Pichon L., Brunel G., Payan J.-C., Taylor J., Bellon-Maurel V., Tisseyre B. 2021. ApeX-Vigne: experiences in monitoring vine water status from within-field to regional scales using crowdsourcing data from a free mobile phone application. Precision Agriculture, 22(3), 608 – 626. https://doi.org/10.1007/s11119-021-09797-9
Brunel G., Moinard S., Ducanchez A., Crestey T., Pichon L., Tisseyre B. 2021. Empirical mapping for evaluating an LPWAN (LoRa) wireless network sensor prior to installation in a vineyard. OENO One, 55(2), 301 – 313. https://doi.org/10.20870/oeno-one.2021.55.2.3102
Pichon L., Taylor J., and Tisseyre B. 2020. Using smartphone leaf area index data acquired in a collaborative context within vineyards in southern France. OENO One, 54(1), 123–130. https://doi.org/10.20870/oeno-one.2020.54.1.2481
Leroux C., Jones H., Pichon L., Taylor J., and Tisseyre B. 2019. Automatic Harmonization of Heterogeneous Agronomic and Environmental Spatial Data. Precision Agriculture 20(6); https://doi.org/10.1007/s11119-019-09650-0
Pichon L., Leroux C., Macombe C., Taylor J., and Tisseyre B. 2019. What Relevant Information Can Be Identified by Experts on Unmanned Aerial Vehicles’ Visible Images for Precision Viticulture? Precision Agriculture 20, 278-294; https://doi.org/10.1007/s11119-019-09634-0
Leroux C., Jones H., Pichon L., Guillaume S., Lamour J., Taylor J., Naud O., Crestey T., Lablee J.L., Tisseyre B., 2018. GeoFIS: An open source, decision-support tool for precision agriculture data, Agriculture. 2018, 8 (6), 73; https://doi.org/10.3390/agriculture8060073.
Tisseyre B., Leroux C., Pichon L., Geraudie V., Sari T., 2018. How to define the optimal grid size to map high resolution spatial data? Precision agriculture. 19, 957–971 https://doi.org/10.1007/s11119-018-9566-5
Pichon L., Ducanchez A., Fonta H., Tisseyre B. 2016. Quality of Digital Elevation Models obtained from Unmanned Aerial Vehicles for Precision Viticulture. OENO One, 50(3). https://doi.org/10.20870/oeno-one.2016.50.3.1177
Minasny B., McBratney A., Pichon L., Sun W., Short M. 2009. Evaluating near infrared spectroscopy for field prediction of soil properties. Australian Journal of Soil Research, 47(7), 664-673 https://doi.org/10.1071/SR09005
Articles in non-refereed journals
Pichon L., Brunel G., Payan J.C., Tisseyre B., 2020, Apex-Vigne: A mobile application to facilitate the monitoring of growth and estimate the water status of the viticulture plots. IVES Technical Review; https://doi.org/10.20870/IVES-TR.2020.3558
Lachia N., Pichon L., Tisseyre B., 2018. L’Observatoire des Usages de l’Agriculture Numérique : Connaître les usages de l’agriculture numérique pour mieux accompagner la profession. Innovations Agronomiques, 67, 49-61.
Lachia N., Pichon L., Tisseyre B., 2018. Comment le numérique impacte le métier du conseil en viticulture. Agronomie, environnement & sociétés. 8, 41-50.
Pichon L., Leroux C., Tisseyre B., 2018. Une approche systématique pour identifier les informations pertinentes fournies par drone en viticulture de précision. Innovations Agronomiques, 67, 23-36
Besqueut G., Pichon L., Tisseyre B., 2015. Les drones en viticulture, quels enjeux, quels services ? Revue des œnologues et des techniques vitivinicoles en œnologiques. 157, 12-14.
Conference papers with proceedings
Pichon L., Bopp O., Tisseyre B., Characterising within-field variability of vine water status with simple visual observations of shoot growth. 2021. In: Precision agriculture’21., ED. John v. Stafford, Ampthill, UK, Wageningen Academic Publishers. 179-186; https://doi.org/10.3920/978-90-8686-916-9_20
Fornieles-Lopez E., Brunel G., Devaux N., Rançon F., Pichon L., Tisseyre B. Potential of temporal series of Sentinel-2 images to define zones of vine water restriction. 2021. In: Precision agriculture’21., ED. John v. Stafford, Ampthill, UK, Wageningen Academic Publishers. 543-550; https://doi.org/10.3920/978-90-8686-916-9_65
Brunel G., Moinard S., Pichon L., Tisseyre B. Potential of time series of VIS images from connected static camera for decision support in vineyard. 2021. In: Precision agriculture’21., ED. John v. Stafford, Ampthill, UK, Wageningen Academic Publishers. 821 – 827; https://doi.org/10.3920/978-90-8686-916-9
Lachia N., Pichon L., Marcq P., Taylor J., Tisseyre B. 2021. Why are yield sensors seldom used by farmers – a French case study. In: Precision agriculture’21., ED. John v. Stafford, Ampthill, UK, Wageningen Academic Publishers. 745 – 751; https://doi.org/10.3920/978-90-8686-916-9
Brunel G., Pichon L., Taylor J., and Tisseyre B. 2019. Easy Water Stress Detection System for Vineyard Irrigation Management. In: Precision agriculture’19., ED. John v. Stafford, Ampthill, UK, Wageningen Academic Publishers. 935-942; https://doi.org/10.3920/978-90-8686-888-9
Lachia N., Pichon L., and Tisseyre B. 2019. A Collective Framework to Assess the Adoption of Precision Agriculture in France: Description and Preliminary Results after Two Years. In: Precision agriculture’19., ED. John v. Stafford, Ampthill, UK, Wageningen Academic Publishers. 851-857.; https://doi.org/10.3920/978-90-8686-888-9
Pichon L., Leroux C., Geraudie V., Taylor J., and Tisseyre B. 2019. Investigating the Harmonization of Highly Noisy Heterogeneous Datasets Hand-Collected over the Same Study Domain. In: Precision agriculture’19., ED. John v. Stafford, Ampthill, UK, Wageningen Academic Publishers, 735-741.; https://doi.org/10.3920/978-90-8686-888-9
Lachia N., Pichon L., Tisseyre B., 2018. L’Observatoire des Usages de l’Agriculture Numérique : Connaître les usages de l’agriculture numérique pour mieux accompagner la profession. Carrefours de l’innovation Agronomique, Actes du colloque Numérique en productions végétales : prédire et agir, 26 Juin, Montpellier, 36-45.
Pichon L., Leroux C., Tisseyre B., 2018. Une approche systématique pour identifier les informations pertinentes fournies par drone en viticulture de précision. Carrefours de l’innovation Agronomique, Actes du colloque Numérique en productions végétales : prédire et agir, 26 Juin, Montpellier, 18-28.
Crestey T., Pichon L., Tisseyre B., 2017. Potential of freely available remote sensing visible images to support growers in delineating within field zones. Advances in Animal Biosciences: Precision Agriculture (ECPA) 2017, 8:2, 372–376 ; https://doi.org/10.1017/S2040470017000437
Pichon L., Besqueu G. and Tisseyre B., 2017. A systemic approach to identify relevant information provided by UAV in precision viticulture. Advances in Animal Biosciences: Precision Agriculture (ECPA) 2017, 8:2, 823–827; https://doi.org/10.1017/S2040470017001194
Declaration of invention
Brunel G., Pichon L. 2019. Déclaration d’invention de l’application mobile ApeX Vigne déposée le 2 juillet 2019.
Armand V., Brunel G., Pichon L. 2020. Déclaration d’invention de l’application web ApeX Territoire déposée le 31 janvier 2020
