Neural identification of selected apple pests

Boniecki, P.; Koszela, Krzysztof; Piekarska-Boniecka, H.; Weres, J.; Zaborowicz, Maciej; Kujawa, Sebastian; Majewski, A.; Raba, Barbara

doi:10.1016/j.compag.2014.09.013

Cited by 58 publications

(18 citation statements)

References 36 publications

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“…The hidden layers are defined to be the layers between the input and output layers. Authors in [85] used ANN in the classification process to identify the apple orchard pests. The resulting neural model has been trained by the use of the BP method.…”

Section: Back Propagation (Bp) Classifiermentioning

confidence: 99%

Some New Types of Approximations via Minimal Structure

Sharkasy

Altwme

2018

Journal of the Egyptian Mathematical Society

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In this paper, we use the notions of a minimal structure approximation space (short MSAS) and the notion of near open sets to introduce a new approximation of uncertain sets as a mathematical tool to modify the approximations. Relationships between these types are established via proof and counter examples. Also, some basic concepts of near approximations set are investigated and studied the relations between these different types of sets in MSAS. This set is a specific importance to help with the modifications of an approximation space via adding new concepts and facts. Finally, we use this concept to introduce the definitions of near lower approximation, near upper approximation, near boundary region, near rough and near exact sets and study some of the properties of this notion.

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Section: Back Propagation (Bp) Classifiermentioning

confidence: 99%

Some New Types of Approximations via Minimal Structure

Sharkasy

Altwme

2018

Journal of the Egyptian Mathematical Society

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“…Further position was taken by Turkey (6 publications), Spain (5 publications), Brazil, China, Serbia (4 publications each) and Thailand (3 publications). There were only 2 publications of the Polish authors (Boniecki et al, 2015;Szczypiński et al 2015). Authors from England, India, Malesia, Taiwan and USA published two articles each.…”

Section: Figure 1 Number Of Publications Concerning Ann Applicationsmentioning

confidence: 99%

Present Trends in Research on Application of Artificial Neural Networks in Agricultural Engineering

Francik

Ślipek

Frączek

et al. 2016

Agricultural Engineering

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The objective of the paper was to carry out a bibliometric quantitative analysis of publications concerning the application of artificial neural networks in the research area - agriculture and a bibliometric quantitative analysis and subject analysis with regard to agricultural engineering. A number of scientific publications devoted to the ANN found in the data base of the Web of Science - in documents published to 2015 was a basis for the quantitative analysis. Research on the use of artificial neural networks in the research area - agriculture is extending systematically. Moreover, a rapidly growing number of citations prove a continuous increase in the scientists’ interest in possibilities of the ANN applications. The quantitative analysis of scientific publications in 5 selected scientific journals and thematically related to agricultural engineering (indexed in the Web of Science) allowed a statement that 236 scientific articles from 1996- 2015 were related to the ANN application. The biggest number of publications was reported in Computers and Electronics in Agriculture - 118 articles. In 2011-2015 there was a growing trend in dynamics of publishing of scientific papers devoted to the ANN application to agricultural engineering. Thus, we may assume that the research related to application of the artificial neural networks to agricultural engineering will be continued and their scope and number will be still growing. The thematic analysis of the most often quoted publications from 2011-2015 in the journal Computers and Electronics in Agriculture, proved that they concern both the issues related to the classification problem as well as to modelling processes and systems. We should suppose that the subjects related to modelling of drying processes and application of neural networks for image analysis will grow dynamically in the following years.

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“…An effective identification of the effects of grain weevil feeding on stored grain entailed the designing, manufacturing, and verification of the new, original classification model [7][8][9][10][11]. The commonly recognized separation properties, such as those represented by artificial neural networks, imply the legitimacy of using them in the process of identifying grain damage caused by grain weevil [12].…”

Section: Introductionmentioning

confidence: 99%

Neural Visual Detection of Grain Weevil (Sitophilus granarius L.)

et al. 2020

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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.

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Neural identification of selected apple pests

Cited by 58 publications

References 36 publications

Some New Types of Approximations via Minimal Structure

Some New Types of Approximations via Minimal Structure

Present Trends in Research on Application of Artificial Neural Networks in Agricultural Engineering

Neural Visual Detection of Grain Weevil (Sitophilus granarius L.)

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