[Defended thesis] Léo Pichon: Development of a crowdsourcing approach adapted to agriculture specificities: the case of monitoring vine water status with the shoot growth approach

 Léo Pichon is one of the #DigitAg labelled PhDs

Léo defended his thesis on 13th July 2021 at 10 AM at the Institut Agro, Montpellier (Amphithéâtre Philippe Lamour – Building 9, Coeur d’école, Floor 2).


Development of a crowdsourcing approach adapted to agriculture specificities: the case of monitoring vine water status with the shoot growth approach

  • Start Date: November 2017
  • University: MUSE Montpellier University of Excellence / Institut Agro – Montpellier SupAgro
  • PhD School: GAIA, Montpellier
  • Field(s): ICT for Agriculture, Agronomie
  • Doctoral Thesis Advisor: Véronique Bellon-Maurel (Inrae, UMR Itap)
  • Co-supervisors : Bruno Tisseyre (Institut Agro, Itap), James Taylor (Newcastle University, UK / Inrae, Itap)
  • Funding: Institut Agro – Montpellier SupAgro
  • #DigitAg: Labelled PhD – Axis 3, 4 & 6 – Challenge 1 & 6

Keywords: Precision Viticulture, Decision making, Data quality, Crowdsourcing, High resolution data

Abstract: Crowdsourcing is an approach consisting in answering a question defined by an organisation (research laboratory, company, etc.) by relying on the collective intelligence of a community of contributors. To date, crowdsourcing is not widely spread in agriculture, but it has great potential for collecting georeferenced observations to monitor phenomena at regional scale (e.g. diseases, pests or abiotic stresses monitoring). These crowdsourcing projects in agriculture have specificities in terms of participants (professional contributors, importance of the role of advisors), studied phenomena (with strong spatial and temporal covariances) and datasets collected (asynchronous and heterotopic) that have led some authors to coin the concept of farmsourcing to describe them. These specificities of farmsourcing projects influence the design of the projects and the involvement of the different stakeholders. They also influence the criteria and indicators for evaluating the success of such projects. Finally, they influence the methods for identifying outliers and surprising observations in corresponding datasets. To date, there is no existing approach taking into account the specificities of farmsourcing projects. The objective of this thesis is to propose tools and methods to develop a farmsourcing approach in both the design and the evaluation of the project (How to foster the contribution of participants? How to evaluate the success of a project?) and then in the characterisation of the quality of the resulting observations (How to identify outliers and surprising observations? How can these approaches be automated?) The thesis is based on a systemic approach with the implementation of a case study. This case study is the monitoring of the vine water status at regional scale using i) an indicator (iG-Apex) based on observations of vine shoot growth and ii) the development of a dedicated farmsourcing application (ApeX-Vigne).
Firstly, the work demonstrated the value of a simple but noisy approach, such as the one based iG-Apex, for characterising an agronomic variable of interest (in this case, the vine water status) at the field and intra-field levels in a decision support context. They demonstrated how an approach like this could be used to promote participation in farmsourcing projects. The work carried out explored the technological and methodological choices for designing and deploying on a large scale a mobile application promoting the gathering of georeferenced farmsourcing observations. It also proposed a simple approach based on the study of spatial structure to assess the capacity of these projects to provide relevant information at the regional scale. Finally, the work carried out explored an approach for automatically identifying outliers and surprising observations in farmsourcing datasets. This approach is based on density-based clustering methods taking into account spatial, temporal and attribute characteristics of observations.
In the coming years, this work should enable the development of farmsourcing tools and projects giving access to new sources of information for decision support at different spatial scales.


Contact:  leo.pichon [AT] supagro.fr

Networks: ResearchGate  LinkedIn


Articles dans revues à comité de lecture
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 dans revues sans comité de lecture
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.

Communications à congrès avec actes
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

Déclaration d’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