A novel approach for field sampling optimization incorporating a generic operational cost constraint
Résumé
This paper aims to present a new approach based on the conditioned Latin Hypercube sampling (cLHS) algorithm for the field sampling of a key parameter in precision agriculture. Capturing the within-field variability is essential for crop management, but today few sampling methods take into consideration the operator cost. This paper introduces a generic operational cost constraint that is incorporated into a cLHS-based method. It was applied to yield sampling in viticulture where operator path is highly constrained by rows and trellising. Results showed that the proposed method provides comparable values to the literature method. The proposed approach therefore constitutes an interesting framework for operational field sampling in agriculture.