Abstract
A significant part of cereal production is intended for agri-food processing, which implies a necessity to search for and implement modern storage systems for this product. Stored grain is exposed to many unfavorable factors, particularly caryopsis macro-damage caused mainly by grain weevil (Sitophilus granarius L.). This triggers a substantial decrease in the value of the stored material, thus resulting in serious economic losses. Due to this fact, it is necessary to take steps to effectively detect this pest’s presence when grain is delivered to storage facilities. The purpose of this work was to identify the representative physical characteristics of wheat caryopsis affected by grain weevil. An automated visual system was developed to ease the detection of damaged kernels and adult weevils. In order to obtain the empirical data, a decision was made to take advance of SKCS 4100 (the Perten Single Kernel Characterization System). The measurements obtained were used to build the training sets necessary in the process of ANN (artificial neural network) learning with digital neural classifiers. Next, a set of identifying neural models was created and verified, and then the optimal topology was selected. The utilitarian goal of the research was to support the decision-making process taking place during grain storage.
Highlights
Cereal grain storage is mainly conditioned by ambient temperature and humidity, which constitute crucial parameters affecting the quality of stored grain
Two different types of neural networks were tested for each kind of dataset. It was the parameters of the given neural network, such as correlation, coefficient of total determination, and quotient of standard deviations, that determined the selection of the best neural network
Following the selection of the given network, the process of learning the network was implemented. During this process, based on the selected algorithm, special attention was put on its ability of approximation and generalization, based on quality measurements with the lowest root mean square (RMS) error
Summary
Cereal grain storage is mainly conditioned by ambient temperature and humidity, which constitute crucial parameters affecting the quality of stored grain. The presence of living organisms that cause direct losses resulting from their infestation is important. There are notable indirect losses in the stored material resulting from contamination with excretions and secretions, as well as from moistening and heating. Stored grain may be infested by many different organisms, such as bacteria, fungi, mites, and insects. One of the most damaging grain pest species in Europe is the grain weevil (Sitophilus granarius L.) [1]. It can cause up to 5% of losses in stored crops. This pest is often a cleverly hidden grain destroyer. Its beetles can be detected during sieving, the identification of eggs, larvae, and pupae is difficult and requires appropriate laboratory tests
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