Comparison of methods of obtaining visual data in the shape of obstacles in wastelands and forest areas for the purpose of automatic control of the stability of self-propelled machines

Szychta, Marek; Szymczyk, Sebastian; Gierz, Łukasz; Przybył, Krzysztof; Koszela, Krzysztof; Duda, Adamina

doi:10.1117/12.2602016

Cited by 3 publications

(3 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Observation of changes in plants using vision systems [11][12][13] , a photogrammetric method 14 and luminescence spectrometry are becoming more and more important in assessing the condition of plants and crops. Using appropriate image analysis methods, it is possible to obtain a lot of important information about the observed objects, such as quality parameters [15][16][17][18] , physical data 19,20 , selection data 21,22 or classification data 23,[26][27][28] .…”

Section: Discussionmentioning

confidence: 99%

Detecting of plant protection spraying by excited luminescence

Szychta

Szulc²,

Gierz³

et al. 2022

Fourteenth International Conference on Digital Image Processing (ICDIP 2022)

View full text Add to dashboard Cite

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.

show abstract

Section: Discussionmentioning

confidence: 99%

Detecting of plant protection spraying by excited luminescence

Szychta

Szulc²,

Gierz³

et al. 2022

Fourteenth International Conference on Digital Image Processing (ICDIP 2022)

View full text Add to dashboard Cite

show abstract

“…Research on the artificial intelligence system has become an interest of many scientists [37][38][39][40][41][42][43][44] as it has a high level of potential for the future. One such innovation is the present attempt to use artificial neural networks for the non-invasive recognition of the flavor of oranges using a color space model.…”

Section: Introductionmentioning

confidence: 99%

Predicting Fruit’s Sweetness Using Artificial Intelligence—Case Study: Orange

et al. 2022

View full text Add to dashboard Cite

The manual classification of oranges according to their ripeness or flavor takes a long time; furthermore, the classification of ripeness or sweetness by the intensity of the fruit’s color is not uniform between fruit varieties. Sweetness and color are important factors in evaluating the fruits, the fruit’s color may affect the perception of its sweetness. This article aims to study the possibility of predicting the sweetness of orange fruits based on artificial intelligence technology by studying the relationship between the RGB values of orange fruits and the sweetness of those fruits by using the Orange data mining tool. The experiment has applied machine learning algorithms to an orange fruit image dataset and performed a comparative study of the algorithms in order to determine which algorithm has the highest prediction accuracy. The results showed that the value of the red color has a greater effect than the green and blue colors in predicting the sweetness of orange fruits, as there is a direct relationship between the value of the red color and the level of sweetness. In addition, the logistic regression model algorithm gave the highest degree of accuracy in predicting sweetness.

show abstract

“…For the last ten or fifteen years, artificial intelligence is one of the most dynamically developing fields of science 1 . For many researchers it is an enormous chance for faster and faster development, lower costs of manufacturing products and increase in production efficiency 2 . The age of cognitive computation is approaching rapidly so the application of artificial intelligence for most entrepreneurs in the economy is only a matter of time.…”

Section: Introductionmentioning

confidence: 99%

Application of artificial neural networks in recognizing carrier based on the color of raspberry powders obtained in the spray-drying process

Przybył

Wawrzyniak²,

Samborska³

et al. 2022

Fourteenth International Conference on Digital Image Processing (ICDIP 2022)

View full text Add to dashboard Cite

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.

show abstract

Comparison of methods of obtaining visual data in the shape of obstacles in wastelands and forest areas for the purpose of automatic control of the stability of self-propelled machines

Cited by 3 publications

References 10 publications

Detecting of plant protection spraying by excited luminescence

Detecting of plant protection spraying by excited luminescence

Predicting Fruit’s Sweetness Using Artificial Intelligence—Case Study: Orange

Application of artificial neural networks in recognizing carrier based on the color of raspberry powders obtained in the spray-drying process

Contact Info

Product

Resources

About