Sustainable agricultural production, under the current world population explosion, calls for agricultural robot operations that are personalized, i.e., locally adjusted, rather than en masse. This work proposes implementing such operations based on logic in order to ensure that a reasonable operation is applied locally. In particular, the interest here is in grape harvesting, where a binary decision has to be taken regarding the maturity of a grape in order to harvest it or not. A Boolean lattice ontology of inequalities is considered regarding three grape maturity indices. Then, the established fuzzy lattice reasoning (FLR) is applied by the FLRule method. Comparative experimental results on real-world data demonstrate a good maturity prediction. Other advantages of the proposed method include being parametrically tunable, as well as exhibiting explainable decision-making with either crisp or ambiguous input measurements. New mathematical results are also presented.
C. Lytridis, G. Siavalas, T. Pachidis, S. Theocharis, E. Moschou, V. G. Kaburlasos, “Grape maturity estimation for personalized agrobot harvest by fuzzy lattice reasoning (FLR) on an ontology of constraints”, Sustainability, vol. 15, no. 9, 7331, 2023. https://doi.org/10.3390/su15097331 (Special Issue Computational Intelligence for Sustainability. Guest Editors: Zhebin Xue, Xianyi Zeng) https://www.mdpi.com/journal/sustainability/special_issues/Computational_IntelligenceSustainability