Combining target sampling with within field route-optimization to optimise on field yield estimation in viticulture
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Combining target sampling with within field route‑optimization to optimise on field yield estimation in viticulture B. Oger1,2 · P. Vismara2,3 · B. Tisseyre1
© Springer Science+Business Media, LLC, part of Springer Nature 2020
Abstract This paper describes a new approach for yield sampling in viticulture. It combines approaches based on auxiliary information and path optimization to offer more consistent sampling strategies, integrating statistical approaches with computer methods. To achieve this, groups of potential sampling points, comparable according to their auxiliary data values are created. Then, an optimal path is constituted that passes through one point of each group of potential sampling points and minimizes the route distance. This part is performed using constraint programming, a programming paradigm offering tools to deal efficiently with combinatorial problems. The paper presents the formalization of the problem, as well as the tests performed on nine real fields were high resolution NDVI data and medium resolution yield data were available. In addition, tests on simulated data were performed to examine the sensitivity of the approach to field data characteristics such as the correlation between auxiliary data and yield, the spatial auto-correlation of the data among others. The approach does not alter much the results when compared to conventional approaches but greatly reduces sampling time. Results show that, for a given amount of time, combining model sampling and path optimization can give estimation error up to 30% lower for a given amount of time compared to previous methods. Keywords Yield estimation · Sampling · NDVI · Constraint programming · Simulation · Spatial data · Viticulture
Introduction In order to optimize harvest organization and quality management, the wine industry needs to know the yield of each vine field. Ideally, yield has to be estimated a few days before harvest with a relative error of less than 10% (Carrillo et al. 2016). Although models have * B. Oger [email protected] 1
ITAP, Univ. Montpellier, Montpellier SupAgro, INRAE, Montpellier, France
2
MISTEA, Univ. Montpellier, Montpellier SupAgro, INRAE, Montpellier, France
3
LIRMM, Univ. Montpellier, CNRS, Montpellier, France
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Vol.:(0123456789)
Precision Agriculture
been developed to forecast the yield at the regional level (Cristofolini and Gottardini 2000), their results were not precise enough to manage logistic issues in relation to harvest operations at the farm or at the winery level. Therefore, precise estimation of vine field yield always requires fruit sampling and counting (Clingeleffer et al. 2001). This estimation must be carried out quickly (few minutes per field) at a time when the workload at harvest or for the preparation of the harvest is critical. Practical constraints, like the time available to visit all the fields before harvest, limit the number of sampled sites per field. Therefore, yield estimation is based on a low number of sites sampled (4 to 5 sites per field) where
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