Using Soft Sensors as a Basis of an Innovative Architecture for Operation Planning and Quality Evaluation in Agricultural Sprayers

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 (k−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.

A digital twin–driven method for online quality control in process industry

“…The former is commonly based on the first principle model but hard to be extensively used on account of its complicated processes and unquantifiable parametric relationships. The most popular modeling techniques for data-driven soft sensors include principal component regression (PCR) [12,13], partial least squares (PLS) [14,15], artificial neural networks (ANN) [16,17] and support vector machines (SVM) [18,19]. Wang et al [20] integrated random forest with Bayesian optimization to predict and maintain product quality and validated model superiorities through semiconductor production line data.…”

“…] + 𝑃 𝑚𝑚𝑖𝑛 (13) where 𝑃 𝑚 , 𝑃 𝑚𝑚𝑎𝑥 , 𝑃 𝑚𝑚𝑖𝑛 are mutation rate, maximum mutation rate and minimum mutation rate, respectively; 𝑓 𝑚 is fitness of individual to mutate; 𝛼 2 is a height parameter of the curve. The adaptive mutation rate curve is shown in Fig.…”