{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,1]],"date-time":"2025-11-01T13:49:07Z","timestamp":1762004947174},"reference-count":10,"publisher":"World Scientific Pub Co Pte Lt","issue":"03","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Int. J. Semantic Computing"],"published-print":{"date-parts":[[2017,9]]},"abstract":"<jats:p> This paper presents a method to infer the quality of sprayers based on data collection of the drop spectra and their physical descriptors, which are used to generate a knowledge base to support decision-making in agriculture. The knowledge base is formed by collected experimental data, obtained in a controlled environment under specific operating conditions, and the semantics used in the spraying process to infer the quality in the application. The electro-hydraulic operating conditions of the sprayer system, which include speed and flow measurements, are used to define experimental tests, perform calibration of the spray booms and select the nozzle types. Using the Grubbs test and the quartile-quartile plot an exploratory analysis of the collected data was made in order to determine the data consistency, the deviation of atypical values, the independence between the data of each test, the repeatability and the normal representation of them. Therefore, integrating measurements to a knowledge base it was possible to improve the decision-making in relation to the quality of the spraying process defined in terms of a distribution function. Results shown that the use of advanced models and semantic interpretation improved the decision-making processes related to the quality of the agricultural sprayers. <\/jats:p>","DOI":"10.1142\/s1793351x17400104","type":"journal-article","created":{"date-parts":[[2017,10,2]],"date-time":"2017-10-02T01:49:02Z","timestamp":1506908942000},"page":"279-292","source":"Crossref","is-referenced-by-count":3,"title":["A Model Approach to Infer the Quality in Agricultural Sprayers Supported by Knowledge Bases and Experimental Measurements"],"prefix":"10.1142","volume":"11","author":[{"given":"Elmer A. G.","family":"Pe\u00f1aloza","sequence":"first","affiliation":[{"name":"Department of Electrical Engineering and Computing, University of S\u00e3o Paulo, S\u00e3o Carlos, S\u00e3o Paulo, Brazil"}]},{"given":"Paulo E.","family":"Cruvinel","sequence":"additional","affiliation":[{"name":"Embrapa Instrumentation, Brazilian Agricultural Research Corporation, S\u00e3o Carlo, S\u00e3o Paulo, Brazil"}]},{"given":"Vilma A.","family":"Oliveira","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering and Computing, University of S\u00e3o Paulo, S\u00e3o Carlos, S\u00e3o Paulo, Brazil"}]},{"given":"Augusto G. F.","family":"Costa","sequence":"additional","affiliation":[{"name":"Embrapa Cotton, Brazilian Agricultural Research Corporation, Campina Grande, Paraiba, Brazil"}]}],"member":"219","published-online":{"date-parts":[[2017,10]]},"reference":[{"key":"S1793351X17400104BIB002","volume-title":"Manejo de Plantas Daninhas na Cultura do Algodoneiro (Management of Weeds in the Cotton Crop)","author":"Costa A. G. F.","year":"2015","edition":"1"},{"issue":"5","key":"S1793351X17400104BIB003","first-page":"762","volume":"28","author":"Barroso A. L. L.","year":"2012","journal-title":"Bioscience Journal"},{"key":"S1793351X17400104BIB004","doi-asserted-by":"publisher","DOI":"10.1016\/j.scitotenv.2016.07.079"},{"key":"S1793351X17400104BIB005","doi-asserted-by":"publisher","DOI":"10.1016\/j.cropro.2016.01.008"},{"key":"S1793351X17400104BIB006","first-page":"29","volume":"257","author":"Stutter M. 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