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Pregled bibliografske jedinice broj: 1085461

Fruit firmness prediction using multiple linear regression

Ivanovski, Tomislav; Zhang, Guoxiang; Jemrić, Tomislav; Gulić, Marko; Matetić, Maja
Fruit firmness prediction using multiple linear regression // Proceedings of the 43rd International Convention MIPRO, Conference on Business Intelligence Systems / Skala, Karolj (ur.).
Opatija: Hrvatska udruga za informacijsku i komunikacijsku tehnologiju, elektroniku i mikroelektroniku - MIPRO, 2020. str. 1570-1575 (predavanje, međunarodna recenzija, cjeloviti rad (in extenso), znanstveni)

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Naslov
Fruit firmness prediction using multiple linear regression

Autori
Ivanovski, Tomislav ; Zhang, Guoxiang ; Jemrić, Tomislav ; Gulić, Marko ; Matetić, Maja

Vrsta, podvrsta i kategorija rada
Radovi u zbornicima skupova, cjeloviti rad (in extenso), znanstveni

Izvornik
Proceedings of the 43rd International Convention MIPRO, Conference on Business Intelligence Systems / Skala, Karolj - Opatija : Hrvatska udruga za informacijsku i komunikacijsku tehnologiju, elektroniku i mikroelektroniku - MIPRO, 2020, 1570-1575

Skup
43rd International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO 2020) ; Business Intelligence Systems (miproBIS 2020)

Mjesto i datum
Opatija, Hrvatska, 28.09.2020. - 02.10.2020

Vrsta sudjelovanja
Predavanje

Vrsta recenzije
Međunarodna recenzija

Ključne riječi
smart agriculture ; BP neural networks ; machine learning ; linear regression ; prediction models ; fruit firmness

Sažetak
Smart agriculture is a term used to describe the utilization of digital technologies used in optimizing agricultural food production systems. In order to increase the efficiency of manufacturing process, modern tools for collecting, storing and analyzing electronic data are used. The focus of this paper is creation and comparison of peach firmness prediction models using various machine learning algorithms. The size of the data set, which is used to construct machine learning models described in this paper, is small. Because size of the data set has a large impact on the performance of the machine learning algorithm, models of different complexities were developed to tackle this problem. Simple linear regression is used as fundamental techniques for predicting numerical outcome variable, the peach firmness using a single predictor variable. By extending simple linear regression model so that is can accommodate multiple predictors, multiple linear regression model is obtained, which is the top performing model when applied to the dataset described in this paper. The backpropagation neural network model is developed and its performance is compared to the performance of regression models.

Izvorni jezik
Engleski

Znanstvena područja
Računarstvo, Poljoprivreda (agronomija), Informacijske i komunikacijske znanosti



POVEZANOST RADA

Projekti:
--uniri-drustv-18-122 - Dubinska analiza tokova podataka za pametno upravljanje hladnim lancem (SmaCC) (SMACC) (Matetić, Maja) ( CroRIS)

Ustanove:
Pomorski fakultet, Rijeka,
Agronomski fakultet, Zagreb,
Fakultet informatike i digitalnih tehnologija, Rijeka

Profili:

Avatar Url Maja Matetić (autor)

Avatar Url Marko Gulić (autor)

Avatar Url Tomislav Jemrić (autor)

Citiraj ovu publikaciju:

Ivanovski, Tomislav; Zhang, Guoxiang; Jemrić, Tomislav; Gulić, Marko; Matetić, Maja
Fruit firmness prediction using multiple linear regression // Proceedings of the 43rd International Convention MIPRO, Conference on Business Intelligence Systems / Skala, Karolj (ur.).
Opatija: Hrvatska udruga za informacijsku i komunikacijsku tehnologiju, elektroniku i mikroelektroniku - MIPRO, 2020. str. 1570-1575 (predavanje, međunarodna recenzija, cjeloviti rad (in extenso), znanstveni)
Ivanovski, T., Zhang, G., Jemrić, T., Gulić, M. & Matetić, M. (2020) Fruit firmness prediction using multiple linear regression. U: Skala, K. (ur.)Proceedings of the 43rd International Convention MIPRO, Conference on Business Intelligence Systems.
@article{article, author = {Ivanovski, Tomislav and Zhang, Guoxiang and Jemri\'{c}, Tomislav and Guli\'{c}, Marko and Mateti\'{c}, Maja}, editor = {Skala, K.}, year = {2020}, pages = {1570-1575}, keywords = {smart agriculture, BP neural networks, machine learning, linear regression, prediction models, fruit firmness}, title = {Fruit firmness prediction using multiple linear regression}, keyword = {smart agriculture, BP neural networks, machine learning, linear regression, prediction models, fruit firmness}, publisher = {Hrvatska udruga za informacijsku i komunikacijsku tehnologiju, elektroniku i mikroelektroniku - MIPRO}, publisherplace = {Opatija, Hrvatska} }
@article{article, author = {Ivanovski, Tomislav and Zhang, Guoxiang and Jemri\'{c}, Tomislav and Guli\'{c}, Marko and Mateti\'{c}, Maja}, editor = {Skala, K.}, year = {2020}, pages = {1570-1575}, keywords = {smart agriculture, BP neural networks, machine learning, linear regression, prediction models, fruit firmness}, title = {Fruit firmness prediction using multiple linear regression}, keyword = {smart agriculture, BP neural networks, machine learning, linear regression, prediction models, fruit firmness}, publisher = {Hrvatska udruga za informacijsku i komunikacijsku tehnologiju, elektroniku i mikroelektroniku - MIPRO}, publisherplace = {Opatija, Hrvatska} }

Časopis indeksira:


  • Scopus

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