Abstract
One of the major problems facing humanity in the coming decades is the production of food on a large scale. The production of large quantities of food must be conducted in a sustainable and responsible manner for nature and humans. In this sense, the appropriate application of agricultural pesticides plays a fundamental role since pesticide application in a qualified manner reduces human and environmental risks as well as the costs of food production. Evaluation of the quality of application using sprayers is an important issue, and several quality descriptors related to the average diameter and distribution of droplets are used. This paper describes the construction of a data-driven soft sensor using the parametric principal component regression (PCR) method based on principal component analysis (PCA), which works in two configurations: with the input being the operating conditions of the agricultural boom sprayers and its outputs being the prediction of the quality descriptors of spraying, and vice versa. The soft sensor provides, in one configuration, estimates of the quality of pesticide application at a certain time and, in the other, estimates of the appropriate sprayer-operating conditions, which can be used for control and optimization of the processes in pesticide application. Full cone nozzles are used to illustrate a practical application as well as to validate the usefulness of the soft sensor designed with the PCR method. The selection of historical data, exploration, and filtering of data, and the structure and validation of the soft sensor are presented. For comparison purposes, the results with the well-known nonparametric k-Nearest Neighbor (NN) regression method are presented. The results of this research reveal the usefulness of soft sensors in the application of agricultural pesticides and as a knowledge base to assist in agricultural decision-making.
Highlights
With the rise in the data-processing capacity and the speed of calculation in the new generation of processors embedded in small devices, it is easier to create virtual instruments based on information and models obtained from the production process.mathematical models can be used to represent the variables that cannot be measured in a process based on the variables that are available and can be measured with instruments
To apply techniques that work with maximization of variance, such as principal component analysis (PCA) or reduction of errors, it is important that the data of the random observations fit a normal curve
For quality descriptors, the value of the root mean square error (RMSE) is quite high for the regression k Nearest Neighbor (k-neural networks (NN)) when compared to principal components (PCs) regression
Summary
With the rise in the data-processing capacity and the speed of calculation in the new generation of processors embedded in small devices, it is easier to create virtual instruments based on information and models obtained from the production process.mathematical models can be used to represent the variables that cannot be measured in a process based on the variables that are available and can be measured with instruments. With the rise in the data-processing capacity and the speed of calculation in the new generation of processors embedded in small devices, it is easier to create virtual instruments based on information and models obtained from the production process. Soft sensors are computer programs established on models that are used for estimating unmeasurable variables from production processes; they are based on estimation and prediction techniques that use a priori information collected using sensors and mathematical models that describe physical processes. In 1995, an inference estimator based on fuzzy logic to measure and control the purity of propylene from a high-purity distillation column was designed [3]. Estimation was made by adopting the distillation process model and by using it as the knowledge base for training the input and output data of the plant for specific situations
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