Grape Maturity Estimation for Personalized Agrobot Harvest by Fuzzy Lattice Reasoning (FLR) on an Ontology of Constraints
-
Published:2023-04-28
Issue:9
Volume:15
Page:7331
-
ISSN:2071-1050
-
Container-title:Sustainability
-
language:en
-
Short-container-title:Sustainability
Author:
Lytridis Chris1ORCID, Siavalas George1, Pachidis Theodore1ORCID, Theocharis Serafeim2ORCID, Moschou Eirini1, Kaburlasos Vassilis G.1ORCID
Affiliation:
1. HUMAIN-Lab, International Hellenic University (IHU), 65404 Kavala, Greece 2. Viticulture Laboratory, Department of Horticulture, Viticulture School of Agriculture, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece
Abstract
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.
Funder
“Technology for Skillful Viniculture (SVtech)” Operational Program “Competitiveness, Entrepreneurship and Innovation” Greece and the European Union
Subject
Management, Monitoring, Policy and Law,Renewable Energy, Sustainability and the Environment,Geography, Planning and Development,Building and Construction
Reference21 articles.
1. (2022, December 12). United Nations World Population Projected to Reach 9.8 Billion in 2050. Available online: https://www.un.org/development/desa/en/news/population/world-population-prospects-2017.html. 2. De Clercq, M., Vatz, A., and Biel, A. (2018, January 11–13). Agriculture 4.0: The Future of Farming Technology. Proceedings of the World Government Summit, Dubai, United Arab Emirates. 3. Setting the Record Straight on Precision Agriculture Adoption;Erickson;Agron. J.,2019 4. Bechar, A. (2021). Innovation in Agricultural Robotics for Precision Agriculture, Springer International Publishing. 5. Fountas, S., Mylonas, N., Malounas, I., Rodias, E., Hellmann Santos, C., and Pekkeriet, E. (2020). Agricultural Robotics for Field Operations. Sensors, 20.
Cited by
1 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献
|
|