Computer image analysis in caryopses quality evaluation as exemplified by malting barley

Koszela, Krzysztof; Raba, Barbara; Zaborowicz, Maciej; Przybył, Krzysztof; Wojcieszak, Dawid; Czekała, Wojciech; Ludwiczak, Agnieszka; Przybylak, Andrzej; Boniecki, P.; Przybył, J.

doi:10.1117/12.2197032

Cited by 22 publications

(9 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…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%

“…The output variable was encoded in the form of a dichotomous binary nominal variable. The training set consisted of 1800 randomly selected data, and it was divided proportionally 2:1:1 into In order to create the classification neural models, a neural network simulator implemented in Statistica v. 10 package was used [1,[18][19][20]. The most important stage of the ANN generation was the preparation of the training files containing encoded selected representative properties, constituting the empirical basis for the classification [20][21][22][23].…”

Section: Methodsmentioning

confidence: 99%

“…The training set consisted of 1800 randomly selected data, and it was divided proportionally 2:1:1 into In order to create the classification neural models, a neural network simulator implemented in Statistica v. 10 package was used [1,[18][19][20]. The most important stage of the ANN generation was the preparation of the training files containing encoded selected representative properties, constituting the empirical basis for the classification [20][21][22][23]. For this purpose, four numerical input variables and one nominal output variable were determined, which resulted from the nature of the formulated scientific problem and represented the characteristic parameters of the process examined.…”

Section: Methodsmentioning

confidence: 99%

See 2 more Smart Citations

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

et al. 2020

View full text Add to dashboard Cite

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.

show abstract

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

See 1 more Smart Citation

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

et al. 2020

View full text Add to dashboard Cite

show abstract

“…Using them in intelligent information systems to perform analysis and recognition with sound or images simplifies production processes, the systematic monitoring of processes and the control of production process as well as the quality evaluation of finished goods. The research work that was done is one the examples of using ANN [2,34,35] which aims, among other things, at automating the process of controlling the quality condition of dried fruits with acoustic signals.…”

Section: Preparation Of Artificial Neural Networkmentioning

confidence: 99%

Classification of Dried Strawberry by the Analysis of the Acoustic Sound with Artificial Neural Networks

Przybył

Duda

Koszela

et al. 2020

Sensors

View full text Add to dashboard Cite

In this paper, the authors used an acoustic wave acting as a disturbance (acoustic vibration), which travelled in all directions on the whole surface of a dried strawberry fruit in its specified area. The area of space in which the acoustic wave occurs is defined as the acoustic field. When the vibrating surface—for example, the surface of the belt—becomes the source, then one can observe the travelling of surface waves. For any shape of the surface of the dried strawberry fruit, the signal of travelling waves takes the form that is imposed by this irregular surface. The aim of this work was to research the effectiveness of recognizing the two trials in the process of convection drying on the basis of the acoustic signal backed up by neural networks. The input variables determined descriptors such as frequency (Hz) and the level of luminosity (dB). During the research, the degree of crispiness relative to the degree of maturity was compared. The results showed that the optimal neural model in respect of the lowest value of the root mean square turned out to be the Multi-Layer Perceptron network with the technique of dropping single fruits into water (data included in the learning data set Z2). The results confirm that the choice of method can have an influence on the effectives of recognizing dried strawberry fruits, and also this can be a basis for creating an effective and fast analysis tool which is capable of analyzing the degree of ripeness of fruits including their crispness in the industrial process of drying fruits.

show abstract

“…Kinnow fruits are preferred over oranges as they are juicier, inexpensive and locally grown in certain parts of south Asia. Image analysis was previously used to identify and assess the quality of fruits, grains, and vegetables (Al-Sammarraie et al 2022; Boniecki et al 2015; Zaborowicz et al 2017; Koszela et al 2015). In the second case, we used fruits from neem ( Azadirachta indica ) tree.…”

Section: Introductionmentioning

confidence: 99%

Single and multi-analyte deep learning-based analysis framework for class prediction in biological images

Krishnan

Kumar

et al. 2022

Preprint

View full text Add to dashboard Cite

Measurement of biological analytes, characterizing flavor in fruits, is a cumbersome, expensive and time-consuming process. Fruits with higher concentration of analytes have greater commercial or nutritional values. Here, we tested a deep learning-based framework with fruit images to predict the class (sweet or sour and high or low) of analytes using images from two types of trees in a single and multi-analyte mode. We used fruit images from kinnow (n = 3451), an edible hybrid mandarin and neem (n = 1045), a tree with agrochemical and pharmaceutical properties. We measured sweetness in kinnows and five secondary metabolites in neem fruits (azadirachtin or A, deacetyl-salannin or D, salannin or S, nimbin or N and nimbolide or E) using a refractometer and high-performance liquid chromatography, respectively. We trained the models for 300 epochs, before and after evolution of hyper-parameters, using 300 generations with 50 epochs/generation, estimated the best models and evaluated their performance on 10 percent of independent images. The validation F1score and test accuracies were 0.79 and 0.77 and 82.55 percent and 60.8 percent respectively for kinnow and neem A analyte. A multi-analyte model enhanced the prediction of the neem model to high class when the D N S combined class predictions were high low high and to low class when D N combined class predictions were low:high respectively. The test accuracy increased further to nearly 70 percent with a 10-fold cross-validation error of 0.257 across ten randomly split train validation test sets proving the potential of a multi-analyte model to enhance the prediction accuracy, especially when the numbers of images are limiting.

show abstract

Computer image analysis in caryopses quality evaluation as exemplified by malting barley

Cited by 22 publications

References 10 publications

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

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

Classification of Dried Strawberry by the Analysis of the Acoustic Sound with Artificial Neural Networks

Single and multi-analyte deep learning-based analysis framework for class prediction in biological images

Contact Info

Product

Resources

About