[Defended thesis] Baptiste Oger: Adaptative Constrained Optimization for Spatial Sampling in Precision Agriculture

Baptiste Oger is one of the #DigitAg co-funded PhDs

He defended his thesis on Friday 27th November at 9am.

 

 

Adaptative Constrained Optimization for Spatial Sampling in Precision Agriculture

  • Start Date: October 2017
  • University: MUSE Montpellier University of Excellence / Montpellier SupAgro
  • PhD School:  GAIA, Montpellier (France)
  • Field(s): Computer Science, Precision Agriculture
  • Doctoral Thesis Advisor:  Bruno Tisseyre (Montpellier SupAgro, ITAP)
  • Co-supervisors :  Philippe Vismara (Montpellier SupAgro, MISTEA), Bruno Tisseyre (Montpellier SupAgro, ITAP)
  • Funding: #DigitAg – Montpellier SupAgro
  • #DigitAg: Axis 3 (Sensors and data acquisition/processing)

Keywords: Precision agriculture, yield optimization, spatial sampling

Abstract:

The wine industry needs to know the yield of each vine field precisely to optimize quality management and limit the costs of harvest operations. Yield estimation is usually based on random vine sampling. The resulting estimations are often not precise enough because of the high variability within vineyard fields. Recent works showed the interest of using vegetative index (i.e. NDVI, GLCV, etc.) derived from high spatial resolution airborne/satellite images to optimize sampling. These works showed it was possible to improve yield estimation by 15 % depending on the considered vine field and the strength of the correlation between vegetative index and yield components.

Other recent research has proposed a unique original approach, based on the consideration of spatial and operational constraints, to optimize the operation of within-field machine operation in viticulture based on high spatial resolution information derived from airborne images and experts zoning (application to optimize selective harvest).

The originality of the PhD project is to propose an interdisciplinary approach that takes into account both these results to optimize the spatial sampling carried out by an operator. The work will aim at developing a methodology which considers i) high resolution information describing the within-field spatial variability ii) operational as well as spatial constraints (time required to perform an observation, time required to walk from a site of measurement to another, spatial organization of the cultivation like rows etc.) and iii) specificity of the field under consideration (like spatial organization of the variability as well as the strength of the correlation between high resolution data and the agronomic information under study, etc.).

The research will provide with the wine industry an adaptative Constrained Optimization for Spatial Sampling in Precision Agriculture. In the short term, the methods may be embedded in a mobile platform like a smartphone with localization facilities. In the long term, these results will be quite usable to optimize the sampling carried out by mobile platforms such as robots.

 

Contact: baptiste.oger [AT] supagro.fr

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Papers at international conferences