Database and Knowledge Base Integration Method to Support Decision-Making Related to Quality of Application and Use of Agricultural Sprayers

This paper presents a method for integrating collected data into a knowledge base, which is useful for assisting decision making processes in practical agricultural spraying practices. The knowledge base in this case is formed by the models of a function of distribution of droplet mean, ranges of the sprayer operating conditions for each nozzle and also by the drops classification standards. The previously obtained results from the knowledge base are thus organized in terms of the operating conditions in a controlled environment. The electrohydraulic operating conditions of the sprayer which includes speed and flow measurements are used to define experimental tests, perform calibration of the spray booms and select nozzle types. The evaluation of the quality of the application is based on the analysis of the normal distributions of volumetric mean diameters along with the information obtained from the knowledge base. Therefore, integrating measurements to a knowledge base can improve the decision-making in the adjustments required in agricultural spraying process, making possible the representation of the mean drop rates and the fitting of the drops mean diameters to a distribution function. The consistency of the analyzed data base and the independence between the data of each test were evaluated using the Grubbs test and the quartilequartile plot. Results show that the use of advanced models can help in the semantic interpretation of the quality of the agricultural sprayers and to improve decision-making processes.