The research was carried out to detect plant protection products on plant leaves using excited luminescence. The tests were carried out for the leaves of two random plant species and four typical plant protection products. The study of excitation and emission spectra (EX-EM) was carried out on the Edinburgh Instruments FS900 luminescence spectrometer equipped with an attachment for measurements from the surface. The collected EX-EM characteristics of clean leaves were compared with the EX-EM characteristics of leaves coated with plant protection products and the EX-EM characteristics of the agents themselves. The obtained results allowed for the assessment of the suitability of the excited luminescence method for the plant protection spraying measuring, including the detection or identification of inappropriate use of plant protection products by a farmer.
Fruit juices and vegetable and fruit juices are the products, which provide our bodies with a lot of valuable and nutritional ingredients and play a major role in prevention of numerous illnesses. Raspberries are the valuable source of bioactive compounds. As part of preserving food, whose main aim is to extend stability of products obtained only in season, the researchers took advantage of spray drying technique. In the research part of the study, research samples were prepared in the form of raspberry powders obtained from the process of dehumidified spray drying. Because of the research, a neural model was made, which supported the evaluation of the quality of detecting powder samples based on their color. The devised neural network reached classification accuracy at 0.924.
In recent years one can observe a continuous growth of demand for dried vegetables. This tendency has an impact on the development of dehydrated food market segment in Poland, which enables to manage a surplus of vegetable production. More and more often dried vegetables are used in various sectors of food industry, both because of their high nutritional values and because of the changing nutritional habits among customers. Among dried vegetables, dried carrots seem to play a strategic role on account of the fact that this produce has a wide spectrum of applications and is famous for its high nutritional value. The research was conducted in order to evaluate the quality of dried carrot cubes using three different techniques of drying. It should be noted that during the research both correct and incorrect dried carrots were used. What is more, the process of deep learning of convolutional artificial neural networks was carried out with MobileNet architecture for classification, for a selected research sample. The classification included both the type of drying process and the quality of drying for binary division on account of the applied parameters. The obtained models were characterized by high capability to classify samples at the level of 85 - 100% with the exception of lyophilized dried carrots where the trained network reached the effectiveness of classification at the level of 71% for the validation set. The research proved that fast and noninvasive evaluation of the quality of dried carrot cubes in different conditions is possible and highly effective using artificial neural networks.
The aim of the research presented in this work was to develop a model of Artificial Neural Networks (ANN) with the use of computer image analysis for the qualitative classification of deep-frozen raw material - breaded pollock cutlets (Backfisch). Shape and color discriminants were selected, by using a computer program, it was possible to obtain numerical data and build a learning set from them. This work is an example of using one of the methods of artificial intelligence in the food industry. The designed network was characterized by a very high ability for classification, its training was done by the technique of the so-called " with a teacher". Such actions are motivated by the requirements of consumers who are becoming more and more attentive to the products they consume and expect calorically balanced and very high quality products.
Application of more and more advanced information technologies in agriculture comprises broader and broader range of production processes, planning processes, monitoring and marketing processes. The applied information technologies are used in production technology of animals and crop production. Within the recent decades one can observe a dynamic development of research on artificial intelligence, and at the same time the development of research within the range of advisory systems (expertise), as well the use of methods of computer image analysis in the process of quality assessment/evaluation of vegetables and fruits. One of the areas of using computer image analysis is supporting decision making processes within the range of quality evaluation of agri-food products. The aim of the project was to use image analysis and artificial neural networks to classify quality of convective dried carrot.
Recently the demand for fruit and vegetable juice powders has increased significantly as there are numerous benefits of using these products in various forms of food. Therefore, it is important to optimise spray drying techniques and find how processing factors influence the quality of powders. For this reason, researchers seek modern methods to aid the assessment of quality of food powders. In this study classes of raspberry powders were distinguished on the basis of selected physical parameters such as: colour expressed in the CIE L* a* b* system, moisture content, and water activity. The classification accuracy of the neural models developed in this study was over 96%.
Comparative research was carried out on three methods of acquiring visual data on the shape of obstacles in wastelands and forest areas for automatic control of the stability of self-propelled machines. The tests were carried out for the following sensors: ultrasonic, 3D IFM O3M251 sensor, and Intel RealSense D455 sensor. The sensors were mounted on a common beam and positioned inline above the obstacles. Four cylindrical objects of various structures and sizes, placed on a driven belt conveyor, were used as recognition obstacles. Tests were carried out at two operating speeds. As a result of the research, obstacle heights were identified concerning relation to their movement under the sensors on the conveyor belt. The obtained results allowed for the assessment of the suitability of each of the methods in the context of their use as a substrate monitoring system for the stabilization systems of agricultural and forest self-propelled machines.
Modern technologies with artificial intelligence are widely used in processing industry and serve, among other things, as boost to increase efficiency and automatization of production processes, which in turn allows to increase productivity of companies while at the same increasing their competitiveness. At present a real challenge for this branch of economy is manufacturing farm and food products, characterized by best parameters in terms of quality while maintaining optical production and distribution costs of biological material which is subject to processing. The given paper touches upon the subject related to evaluation of changes of quality of dried meat made from turkey meat in the process of microwave and vacuum drying as well as its influence on the quality of meat, microstructure and sensory features.
In this paper an attempt was made to build classification models, based on convolutional neural networks, for identification of co-substrate composted with sewage sludge. Due to the pilot character of the studies, they were limited to two co-substrates, i.e. maize straw and rapeseed straw. In total, 12 composting experiments were carried out, each half of them with the content of each of the adopted types of straw. As a result of experiments, 2304 images of composted material samples were obtained, and they bacame the input information for the neural networks. Classification models were developed using the Tensorflow environment, TFLearn library and Python programming language. In their structure, one convolutional layer with different number of convolutional filters and one pooling layer were used to extract image features, and also two fully-connected layers were adopted for classification purposes. The training of the network was carried out with the use of the Adam optimization algorithm. Finally, 4 convolutional neural networks were developed, and their classification error estimated for the test set ranged from 4.1 to 11.0%.
The paper concerns the implementation of research task no. 4 of the PBS3/B8/26/2015 project, the aim of which was to create models of artificial neural networks determining the meat of pork half-carcasses. As a result, thanks to threedimensional scans of the examined half-carcasses, the characteristic cross-section of half-carcasses was determined and the parameters necessary to determine the meatiness were defined. The neon model realizing this task was RBF 9:9-25- 1:1 network, which for the cross-section of 89 was characterized by test quality 0,9887 and RMSE error 0,1556. The work carried out allowed for the development of an algorithm approved by the European Commission on 11 February 2019 np. 2019/252 and the production of devices ESTIMEAT and MEAT3D
KEYWORDS: Visualization, Databases, Data modeling, Visual process modeling, Signal processing, Life sciences, Human-machine interfaces, Associative arrays, Chemical elements, Information technology
Various mechanisms of mapping relational structures in graph database Neo4j presented in previous publications describing both detailed and more universal solutions, did not take into account the whole complexity of relational databases. The main aim of the aforementioned actions was to utilize the possibilities of graph database of programming languages paying special attention to searching for connections between tables when both their number and relations is very high and documentation is incomplete or unavailable. The effects of queries realization were presented with graph form, which resulted from the potential of the client program of the platform called Neo4j used for the needs of this research. The problem that the authors have failed to take into consideration so far, and that is presented now, is the case of intermediate tables occurrence, which are used in great measure to eliminate many to many relations on the level of relational bases as well as to eliminate relations based on complex keys. Another improvement of the presented application is new user interface, which eliminates the necessity for user of having Cypher language skill in order to query graph database. At the same time the application ensures results visualization in richer form than client program with the engine Neo4j that is available and offered.
The aim of this work was to develop a non-invasive method for the quality assessment of oocytes, performed on the basis of graphic information encoded in the form of monochromatic digital images obtained via microscopy techniques. The classification process was conducted based on the information presented in the form of microphotography pictures of domestic pig oocytes, using advanced methods of neural image analysis. The quality classification process was conducted based on the information presented in the form of microphotography pictures of domestic pig oocytes, using advanced methods of neural image analysis. In order to do that, the discriminative features of oocytes, presented in the digital photographs, were identified and extracted. This was necessary to create empirical training sets required in the process of generating neural classifiers.
Civilization development has reduced the number of farming areas, which has led to the situation in which farmers are forced to obtain a higher yield from much smaller acreage. It has resulted in the growth of new methods in agriculture, including precise grain drilling. Under the LIDER project developing the tube clogging control system, there has been identified the need for determining caryopses speed affecting a sensor. Therefore in the article, following the review of all available methods of measuring particle speed it was concluded that there was no speed measurement method for caryopses flying out of a tube as a result of the aerodynamic force which could register the flight trajectory. The aim of the article is to introduce the method and a test stand to measure caryopsis speed by the image analysis method. The method involves applying series of images taken with a high-speed LAVISION camera: HighSpeedStar5 and the FOTOPOMIARY programme. The error difference between the developed innovative method and an analytical method was at the acceptable level of 2%. The developed method and the stand are universal as they make it possible to measure speed for various grains and crops in a seed drill tube.
In order to construct highly productive drilling machines in which sowing material is transported using air, the manner in which a seed behaves in seed pneumatic transportation requires deeper comprehension. Under the LIDER VIII project, the agreement number: LIDER/24/0137/L-8/16NCBR/2017, it is essential to simulate sowing material movement in seed drill tubes so as to identify an optimal position of sensors in a drill coulter. In order to perform simulating tests, the behaviour of caryopses in pneumatic channels needs to be identified first, especially of the drag coefficient Cx which determines the aerodynamic force. The literature presents various methods of aerodynamic characteristics measurements but none of them allow the drag coefficient to be determined in every plane. The aim of the work is to develop the method and a test stand for measuring seed drag force in every plane – helping to determine Cx. According to the accepted methodology, aerodynamic measurements at a tailored-made stand applying the programme called “Displacement” to analyse taken pictures i.e. pendulum inclinations are going to be performed. The work presents possibilities to apply a new method of the aerodynamic force measurement and determination of the coefficient Cx at a special stand in the form of micro aerodynamic channel along with the software.
Potato is a major crop all over the world. New farming trends involve modern agrotechnical techniques which significantly minimize the use of chemicals. The problem of selection of the measure and its environment emerged in the validated method used for assessment of the degree of coverage based on image analysis. The aim of this study was to indicate an adequate representative (staining) liquid, background and lighting. In search of a representative liquid enabling distinction between a sprayed and non-sprayed surface the problem of diversity in the brightness of contrasting surfaces was taken into consideration. Having conducted preliminary analyses, the diversity of the spectral composition of reflected light was used. The study resulted in finding an adequate representative working (staining) liquid in the form of diluted ink. The study also confirmed that white emulsion paint was the best background and that white halogen light was optimal for object illumination.
Research was conducted for the purpose of qualitative identification of convection-dried strawberries using artificial neural networks. 2 samples of raw material were subjected to a drying process, each representing different qualitative classes: ripe and overripe fruit. The generated MLP neural network was based on shape and color characteristics; 11 parameters of the quality of dried strawberry were specified. Empirical data was obtained from digital images which served as learning sets for the artificial neural networks simulator. The created neural network was to identify individual learning cases as one of the following cases: "good" - ripe or "bad" - overripe strawberry. Furthermore, a correlation analysis was performed, which showed a strong relationship between some variables.
The aim of this research was investigate the possibility of using methods of computer image analysis and neural modeling for assess the amount of dry matter in the tested corn cobs. The research lead to the conclusion that the neural image analysis may be a useful tool in determining the quantity of dry matter in this material. Generated neural models may be the beginning of research into the use of neural image analysis assess the content of dry matter in individual corn fractions. The presented models: RBF 31:31-20-1:1 characterized by RMS test error 0.244136 and RBF 18:22-1-1:1 characterized by RMS test error 0.230206 may be more efficient for more learning data. PiAO software and STATISTICA software were used in this work.
The aim of this work was a neural identification of selected apple tree orchard pests in Poland. The classification was conducted on the basis of graphical information coded in the form of selected geometric characteristics of agrofags, presented on digital images. A neural classification model is presented in this paper, optimized using learning files acquired on the basis of information contained in digital photographs of pests. There has been identified 6 selected apple pests, the most commonly encountered in Polish orchards, has been addressed. In order to classify the chosen agrofags, neural networks type Self-Organizing Feature Map (SOFM) methods supported Learned Vector Quantization (LVQ) algorithm were utilized, using by digital analysis of image techniques.
This thesis presents the process of designing and manufacturing an unmanned aerial vehicle in purpose of collecting research materials in the form of photos from air. Priority of the project was to analyze the available materials and manufacture a low-weight construction, using appropriate electronic components, so that the flight time on a single battery charge was as long as possible. An important aspect was also the reduction of manual operating - automatic flight by use of marked geographical coordinates and automatic shutter-release of camera in very specific points. All of these has been analyzed to verify the assumptions.
Self-Organizing Feature Map (SOFM), has been used for the qualitative identification of strawberry juice powders. The research was based on image recognition using powders obtained through an industrial spray-drying process. Results demonstrated that the color features were able to effectively distinguish the research material consisting of spray-dried powders of strawberry juice. The adequate model in terms of the lowest error value RMS (Root Mean Square) contained 46 neurons in the input layer and neurons in the output layer. The model is an effective tool for classifying wrong color changes in strawberry powders.
KEYWORDS: Image processing, Image analysis, Photography, Image quality, Visualization, Potassium, Life sciences, Vegetation, Chemical analysis, RGB color model
In recent years, requirements on potato tuber quality and minimization of chemical protection measures have steadily increased. Increasingly, the precautionary measures in the protection of potato during its vegetation, using the dressing of seed material, are increasingly taking place. Growing awareness of potato producers and increasingly restrictive plant protection standards force the use of new technologies. The quality of the process of applying chemicals to the surface of tubers of seed potato material affects the subsequent quantity and quality of the crop. The aim of the study was to validate the method of evaluating the quality of seed dressing coverage in the process of spraying tubers with a chemical based on computer image analysis.
This article describes data processing in neural analysis of the images of pork half carcass. Parameters of pork halfcarcass obtained from three-dimensional analysis, was processed into form of 130 files. These files has been used as learning sets for the artificial neural network simulator - STATISTICA. Next, we obtained the set of neural models from which the best was chosen. For all data processing activities in this research process were used applications developed in C # in the Visual Studio 2015 development environment.
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