Computer image analysis in obtaining characteristics of images: greenhouse tomatoes in the process of generating learning sets of artificial neural networks

Zaborowicz, Maciej; Przybył, J.; Koszela, Krzysztof; Boniecki, P.; Mueller, W.; Raba, Barbara; Lewicki, Andrzej; Przybył, Krzysztof

doi:10.1117/12.2064066

Cited by 14 publications

(4 citation statements)

References 15 publications

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“…The data collected and classified in this way was converted to a form of training set, which was necessary to generate the ANN. The set of the neural models was created in the Statistica v. 10 program with a simulator implemented in the "Neural Networks" module [20,[24][25][26][27].…”

Section: Methodsmentioning

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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Section: Methodsmentioning

confidence: 99%

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

et al. 2020

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“…Most works in the literature only take consider a certain aspect in food production. The authors of Reference [10] only carried out the preparation and creation of a data set for the training of a neural network. The authors of Reference [11] focused on the complete implementation of the automated acquisition system.…”

Section: Introductionmentioning

confidence: 99%

Definition and Application of a Computational Parameter for the Quantitative Production of Hydroponic Tomatoes Based on Artificial Neural Networks and Digital Image Processing

et al. 2021

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This work presents an alternative method, referred to as Productivity Index or PI, to quantify the production of hydroponic tomatoes using computer vision and neural networks, in contrast to other well-known metrics, such as weight and count. This new method also allows the automation of processes, such as tracking of tomato growth and quality control. To compute the PI, a series of computational processes are conducted to calculate the total pixel area of the displayed tomatoes and obtain a quantitative indicator of hydroponic crop production. Using the PI, it was possible to identify objects belonging to hydroponic tomatoes with an error rate of 1.07%. After the neural networks were trained, the PI was applied to a full crop season of hydroponic tomatoes to show the potential of the PI to monitor the growth and maturation of tomatoes using different dosages of nutrients. With the help of the PI, it was observed that a nutrient dosage diluted with 50% water shows no difference in yield when compared with the use of the same nutrient with no dilution.

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“…The computer image processing includes image compression, filtering or elimination of swoosh. In contrast to image analysis which refers to the detection of shapes from images and the characteristics of an object extract from a digital photo [5][6][7][8][9][10]. However, Artificial Neural Networks (ANN) have the ability to generalize their knowledge, and their time operation is much faster and more efficient than the use of traditional methods used by experts [11][12][13].…”

Section: Introductionmentioning

confidence: 99%

The recognition of potato varieties using of neural image analysis method

et al. 2015

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The aim of this paper was to extract the representative features and generate an appropriate neural model for classification of varieties of edible potato. Potatoes of variety the Vineta and the Denar were the empirical object of this thesis. The main concept of the project was to develop and prepare an image database using the computer image analysis software. The choice of appropriate neural model the one which will have the greatest abilities to identify the selected variety. The aim of this project is ultimately to conduct assistance and accelerate work of the expert, who classifies and keeps different varieties of potatoes in heaps.

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Computer image analysis in obtaining characteristics of images: greenhouse tomatoes in the process of generating learning sets of artificial neural networks

Cited by 14 publications

References 15 publications

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

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

Definition and Application of a Computational Parameter for the Quantitative Production of Hydroponic Tomatoes Based on Artificial Neural Networks and Digital Image Processing

The recognition of potato varieties using of neural image analysis method

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