M. Zaborowicz

Neural identification of selected apple pests

The study was based on neural network modeling methods, including image analysis.Artificial neural networks as a powerful tool to identify pests.The color of pests as the dominant input variable of a neural model.The best classification ability was achieved by MLP topology. The subject of this study was to investigate the possibility of using artificial neural networks as a tool for classification, designed to identify apple orchard pests. The paper presents a classification neural model using optimized learning sets acquired on the basis of the information encoded in the form of digital images of selected pests. This study predominantly deals with the problem of the identification of 6 selected apple pests which are most commonly found in Polish orchards. Neural modeling techniques, including digital image analysis, were used to classify the pests.The qualitative analysis of neural models produced, indicates that multi-layered perceptron (MLP) neural network topology achieve the best classification ability. Representative features, allowing for effective pest identification are 23 visual parameters in the form of 7 selected coefficients of shape and 16 color characteristic of pests. The dominant input variables of a neural model, determining the correct identification of the features, contain information about the color of pests.Our results support the hypothesis that artificial neural networks are an effective tool that supports the process of identification of pests in apple orchards. The resulting neural classifier has been created to assist in the decision-making processes that take place during the production of apples, in the context of protection against pests.

Use of artificial neural networks in the identification and classification of tomatoes

The project aimed to produce a classification model of neural network that would allow automatic evaluate quality of greenhouse tomatoes. The project used computer image analysis and artificial neural networks. Authors based on the analysis of biological material selected set of features that are describing the physical parameters allowing the quality class identification. Image analysis of tomatoes digital photographs samples allowed to choose characteristics features. Obtained characteristics from the images were used as learning data for artificial neural network.

Computer image analysis in the quality in procedure for selected carrot varieties

In our daily lives we often assess our surroundings to classify the situations we encounter. We do so based on the observations we make of our surroundings and information we obtain from other sources, using our knowledge and abilities. While this process is natural to us, if we want to give a similar task to a computer system then we have to take various steps in order to enable our computers to partially emulate the human capacity for observation, learning and making final decisions based on knowledge. As information complexity increases, there is an increasing demand for systems which can recognize and classify the objects presented to them. Recently there has been an increase in interest in application of computer image analysis in various research areas. One of these applications is food quality assessment, which aims to replace traditional instrumental methods. A computer visual system was developed to assess carrot quality, based on a single variety. Characteristic qualities of the variety were chosen to describe a suitable root. In the course of the study, digital photographs of carrot roots were taken, which were used as input data for the assessment performed by a dedicated computer program created as a part of the study.

Quality assessment of microwave-vacuum dried material with the use of computer image analysis and neural model

The farming area for vegetables in Poland is constantly changed and modified. Each year the cultivation structure of particular vegetables is different. However, it is the cultivation of carrots that plays a significant role among vegetables. According to the Main Statistical Office (GUS), in 2012 carrot held second position among the cultivated root vegetables, and it was estimated at 835 thousand tons. In the world we are perceived as the leading producer of carrot, due to the fourth place in the ranking of global producers. Poland is the largest producer of this vegetable in the EU [1]. It is also noteworthy, that the demand for dried vegetables is still increasing. This tendency affects the development of drying industry in our country, contributing to utilization of the product surplus. Dried vegetables are used increasingly often in various sectors of food products industry, due to high nutrition value, as well as to changing alimentary preferences of consumers [2-3]. Dried carrot plays a crucial role among dried vegetables, because of its wide scope of use and high nutrition value. It contains a lot of carotene and sugar present in the form of crystals. Carrot also undergoes many different drying processes, which makes it difficult to perform a reliable quality assessment and classification of this dried material. One of many qualitative properties of dried carrot, having important influence on a positive or negative result of the quality assessment, is color and shape. The aim of the research project was to develop a method for the analysis of microwave-vacuum dried carrot images, and its application for the classification of individual fractions in the sample studied for quality assessment. During the research digital photographs of dried carrot were taken, which constituted the basis for assessment performed by a dedicated computer programme developed as a part of the research. Consequently, using a neural model, the dried material was classified [4-6].

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

The aim of the project was to make the software which on the basis on image of greenhouse tomato allows for the extraction of its characteristics. Data gathered during the image analysis and processing were used to build learning sets of artificial neural networks. Program enables to process pictures in jpeg format, acquisition of statistical information of the picture and export them to an external file. Produced software is intended to batch analyze collected research material and obtained information saved as a csv file. Program allows for analysis of 33 independent parameters implicitly to describe tested image. The application is dedicated to processing and image analysis of greenhouse tomatoes. The program can be used for analysis of other fruits and vegetables of a spherical shape.

The use of image analysis to investigate C:N ratio in the mixture of chicken manure and straw

The aim of the study was to determine the possibility of analysis of C:N ratio in the chicken manure and wheat straw mixture. This paper presents preliminary assumptions and parameters of extraction characteristics process. It also presents an introduction of digital image analysis of chicken manure and wheat straw mixture. This work is an introduction to the study on develop computer system that could replace chemical analysis. Good understanding the value of dependence C:N on the basis of image analysis will help in selection of optimal conditions for biological waste treatment.

Organoleptic damage classification of potatoes with the use of image analysis in production process

In the agro-food sector security it is required the safety of a healthy food. Therefore, the farms are inspected by the quality standards of production in all sectors of production. Farms must meet the requirements dictated by the legal regulations in force in the European Union. Currently, manufacturers are seeking to make their food products have become unbeatable. This gives you the chance to form their own brand on the market. In addition, they use technologies that can increase the scale of production. Moreover, in the manufacturing process they tend to maintain a high level of quality of their products. Potatoes may be included in this group of agricultural products. Potatoes have become one of the major and popular edible plants. Globally, potatoes are used for consumption at 60%, Poland 40%. This is due to primarily advantages, consumer and nutritional qualities. Potatoes are easy to digest. Medium sized potato bigger than 60 mm in diameter contains only about 60 calories and very little fat. Moreover, it is the source of many vitamins such as vitamin C, vitamin B1, vitamin B2, vitamin E, etc. [1]. The parameters of quality consumer form, called organoleptic sensory properties, are evaluated by means of sensory organs by using the point method. The most important are: flavor, flesh color, darkening of the tuber flesh when raw and after cooking. In the production process it is important to adequate, relevant and accurate preparing potatoes for use and sale. Evaluation of the quality of potatoes is determined on the basis of organoleptic quality standards for potatoes. Therefore, there is a need to automate this process. To do this, use the appropriate tools, image analysis and classification models using artificial neural networks that will help assess the quality of potatoes [2, 3, 4].

Identification of selected apple pests based on selected graphical parameters

The aim of this work was a neural identification of selected apple tree orchard pests. 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 sets acquired on the basis of information contained in digital photographs of pests. In particular, the problem of identifying 6 selected apple pests, the most commonly encountered in Polish orchards, has been addressed. In order to classify the agrofags, neural modelling methods were utilized, supported by digital analysis of image techniques.

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

One of the purposes to employ modern technologies in agricultural and food industry is to increase the efficiency and automation of production processes, which helps improve productive effectiveness of business enterprises, thus making them more competitive. Nowadays, a challenge presents itself for this branch of economy, to produce agricultural and food products characterized by the best parameters in terms of quality, while maintaining optimum production and distribution costs of the processed biological material. Thus, several scientific centers seek to devise new and improved methods and technologies in this field, which will allow to meet the expectations. A new solution, under constant development, is to employ the so-called machine vision which is to replace human work in both quality and quantity evaluation processes. An indisputable advantage of employing the method is keeping the evaluation unbiased while improving its rate and, what is important, eliminating the fatigue factor of the expert. This paper elaborates on the topic of quality evaluation by marking the contamination in malting barley grains using computer image analysis and selected methods of artificial intelligence [4-5].

Neural image analysis in the process of quality assessment: domestic pig oocytes

The questions related to quality classification of animal oocytes are explored by numerous scientific and research centres. This research is important, particularly in the context of improving the breeding value of farm animals. The methods leading to the stimulation of normal development of a larger number of fertilised animal oocytes in extracorporeal conditions are of special importance. Growing interest in the techniques of supported reproduction resulted in searching for new, increasingly effective methods for quality assessment of mammalian gametes and embryos. Progress in the production of in vitro animal embryos in fact depends on proper classification of obtained oocytes. The aim of this paper was the development of an original method for quality assessment of oocytes, performed on the basis of their graphical presentation in the form of microscopic digital images. The classification process was implemented on the basis of the information coded in the form of microphotographic pictures of the oocytes of domestic pig, using the modern methods of neural image analysis.