Image analysis techniques in the study of slug behaviour

Kozłowski, R. J.; Kozłowski, Jan; Przybył, Krzysztof; Niedbała, Gniewko; Mueller, W.; Okoń, P.; Wojcieszak, Dawid; Koszela, Krzysztof; Kujawa, Sebastian

doi:10.1117/12.2244533

Cited by 11 publications

(5 citation statements)

References 11 publications

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“…These errors may result in irregularly low data values [150]. The accuracy of measurements is important for ensuring reliable results [151]. Accuracy is in line with the term uncertainty, which covers a wider range of doubts or inconsistencies in the obtained data [152].…”

Section: Restrictions On Selected Input Variablesmentioning

confidence: 94%

Selection of Independent Variables for Crop Yield Prediction Using Artificial Neural Network Models with Remote Sensing Data

Hara

Piekutowska

Niedbała

2021

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Knowing the expected crop yield in the current growing season provides valuable information for farmers, policy makers, and food processing plants. One of the main benefits of using reliable forecasting tools is generating more income from grown crops. Information on the amount of crop yielding before harvesting helps to guide the adoption of an appropriate strategy for managing agricultural products. The difficulty in creating forecasting models is related to the appropriate selection of independent variables. Their proper selection requires a perfect knowledge of the research object. The following article presents and discusses the most commonly used independent variables in agricultural crop yield prediction modeling based on artificial neural networks (ANNs). Particular attention is paid to environmental variables, such as climatic data, air temperature, total precipitation, insolation, and soil parameters. The possibility of using plant productivity indices and vegetation indices, which are valuable predictors obtained due to the application of remote sensing techniques, are analyzed in detail. The paper emphasizes that the increasingly common use of remote sensing and photogrammetric tools enables the development of precision agriculture. In addition, some limitations in the application of certain input variables are specified, as well as further possibilities for the development of non-linear modeling, using artificial neural networks as a tool supporting the practical use of and improvement in precision farming techniques.

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Section: Restrictions On Selected Input Variablesmentioning

confidence: 94%

Selection of Independent Variables for Crop Yield Prediction Using Artificial Neural Network Models with Remote Sensing Data

Hara

Piekutowska

Niedbała

2021

Land

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“…Computer image analysis technique supported by artificial intelligence is commonly used as a tool to solve decision-making issues in the field of agricultural engineering (Kumar and Mittal 2008). They are used in research concerning determination of representative characteristics of compost and other agricultural waste, meat and fruit and vegetables: barley, dried carrots, potatoes defects, tomatoes defects (Kozłowski et al 2016), apple and strawberry sorting, defect sorting in detecting defects in citrus and pedicel/peduncle, and assessment of size of grapevine berries, a system for apple color assessment (Xiaobo et al 2007), plant leaf detection and recognition of banana fruit maturity (Surya Prabha and Satheesh Kumar 2015), as well as assessment of structural changes in strawberry powders (Przybył et al 2018). The authors of this paper never came across any cases of use of neural image analysis to identify fruit and vegetable powders in the process of spray drying.…”

Section: Introductionmentioning

confidence: 99%

Application of artificial neural network for the quality-based classification of spray-dried rhubarb juice powders

2020

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The aim of the study was to develop a neural model enabling classification of fruit spray dried powders, on the basis of graphic data acquired from a bitmap received in the process of spray drying. The neural model was developed with multi-layer perceptron topology. Input variables were expressed in 46 image descriptors based on RGB, YCbCr, HSV (B) and HSL models. Sensitivity analysis of input variables and principal component analysis determined the significance level of each attribute. The optimal model with the lowest error value root mean square, at the level of 0.04 contained 46 neurons in the input layer, 11 neurons in the hidden layer, 10 neurons in the output layer. The results allowed to show that dyeing force (color features) had influence on effective differentiation of the research material consisting of spray-dried powders of rhubarb juice with various dried juice content levels: 30, 40 and 50% as well as high (“H”) and low (“L”) level of saccharification a chosen carrier (potato maltodextrin).

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“…As a result of the possibility of learning and generalizing data, the use of ANN gives better results than statistical methods. Neural modeling methods are used in classification, identification, and prediction, therefore, their potential is significant for practical application in broadly understood agriculture [25][26][27][28][29][30][31][32][33][34][35][36][37][38][39].…”

Section: Introductionmentioning

confidence: 99%

Application of Artificial Neural Networks to Analyze the Concentration of Ferulic Acid, Deoxynivalenol, and Nivalenol in Winter Wheat Grain

2020

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Biotic stress, which includes infection by pathogenic fungi, causes losses of wheat yield in terms of quantity and quality. Ear Fusarium is caused by strains of F. graminearum and F. culmorum, which can produce mycotoxins—deoxynivalenol (DON) and nivalenol (NIV). One of the wheat’s defense mechanisms against stressors is the activation of biosynthesis pathways of antioxidant compounds, including ferulic acid. The aim of the study was to conduct pilot studies on the basis of which neural models were created that would examine the impact of the variety and weather conditions on the concentration of ferulic acid, and link its content with the concentration of deoxynivalenol and nivalenol. The plant material was 23 winter wheat genotypes with different Fusarium resistance. The field experiment was conducted in 2011–2013 in Poland in three experimental combinations, namely: with full chemical protection; without chemical protection, but infested with natural disease (control); and in the absence of fungicidal protection, with artificial inoculation by genus Fusarium fungi. As a result of the pilot studies, three neural models—FERUANN analytical models (ferulic acid content), DONANN (deoxynivalenol content) and NIVANN (nivalenol content)—were produced. Each model was based on 14 independent features, 12 of which were in the form of quantitative data, and the other two were presented as qualitative data. The structure of the created models was based on an artificial neural network (ANN) of the multilayer perceptron (MLP) with two hidden layers. The sensitivity analysis of the neural network showed the two most important features determining the concentration of ferulic acid, deoxynivalenol, and nivalenol in winter wheat seeds. These are the experiment variant (VAR) and winter wheat variety (VOW).

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Image analysis techniques in the study of slug behaviour

Cited by 11 publications

References 11 publications

Selection of Independent Variables for Crop Yield Prediction Using Artificial Neural Network Models with Remote Sensing Data

Selection of Independent Variables for Crop Yield Prediction Using Artificial Neural Network Models with Remote Sensing Data

Application of artificial neural network for the quality-based classification of spray-dried rhubarb juice powders

Application of Artificial Neural Networks to Analyze the Concentration of Ferulic Acid, Deoxynivalenol, and Nivalenol in Winter Wheat Grain

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