The use of vision techniques for the evaluation of selected quality parameters of maize grain during storage

Abstract

The paper presents an innovative method based on vision techniques for rapid assessment of contamination in the mass of stored maize grain. The research was carried out in a selected grain warehouse in the Opolskie Province. Maize grain was used in the studies, which was subjected to tests based on computer image analysis. To assess the state of maize grain contamination, a proprietary computer application based on the RGB model was used., 0

Optimization of Management Processes in Assessing the Quality of Stored Grain Using Vision Techniques and Artificial Neural Networks

“…In developing the method another solution should be adopted, in particular one in which the sample to be acquired contains at most one layer of grain placed on a contrasting background (preferably blue R0G0B255). Irrespective of these difficulties, a relatively small scatter of the measured values could be observed for each of the grain types, i.e., maize [14,15].K.Swedziak,Ż.Grzywacz et al (2020) basis of the collected data, a model artificial neural network (ANN) MLP 52-6-3 was created, which, with the use of four independent features, allows us to determine changes in the content of water, protein and gluten in stored wheat. The chosen network returned good error values: learning, below 0.001; testing, 0.015; and validation, 0.008.…”

“…In order to promote the prospect of process industry with efficient, ecological and intelligent production, modern information technologies should be used in the process of production optimization, management and marketing [13]. More and more often in the cereal industry vision techniques based on computer image analysis and analysis by means of artificial neural networks are used [14,15]. On the basis of the analysis of the different characteristics of the processing industry, as well as the different objectives of smart production, optimal manufacturing for a highly efficienct and green-oriented processing industry is proposed.…”

A Model for Predicting and Grading the Quality of Grain Storage Processes Affected by Microorganisms under Different Environments

“…Therefore, toxins in grain seriously affect grain quality and endanger human health [15], which must be paid sufficient attention. Toxin production in grain is a complex process, and the rapid reproduction and growth of microorganisms are responsible for its toxicity [6,16], while the growth of microorganisms is closely related to the environment and is mainly influenced by temperature and moisture [17,18]. Toxin-producing fungi in microorganisms originate mainly from various fungi of the genera Aspergillus, Penicillium and Camara; these fungi are capable of producing various toxic secondary metabolites such as aflatoxin B1, zearalenone ZON and deoxynivalenol DON, which lead to an accelerated respiration rate of grain quality, increasing the explanation of carbohydrates, proteins and oils, thus seriously affecting the quality of grain [4].…”

“…Lutz et al [5] used a wireless sensor network, an IoT platform, to monitor the equilibrium moisture content in real time and used ANN to predict the quality of maize grains stored under different conditions. Szwedziak et al [6] used a proprietary computer application based on the RGB model to assess the contamination status of maize grains. Xie et al [7] predicted public risk perceptions more accurately by building bp neural networks.…”

Neural Modeling of the Distribution of Protein, Water and Gluten in Wheat Grains during Storage

“…Therefore, it seems justified to develop an innovative method allowing for the determination of the most important features of grain in an easy and fast way. Therefore, a modern method based on neural networks included in machine learning was used [31].…”