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Pregled bibliografske jedinice broj: 1165334

Utilization of Explainable Machine Learning Algorithms for Determination of Important Features in ‘Suncrest’ Peach Maturity Prediction


Ljubobratović, Dejan; Vuković, Marko; Brkić Bakarić, Marija; Jemrić, Tomislav; Matetić, Maja
Utilization of Explainable Machine Learning Algorithms for Determination of Important Features in ‘Suncrest’ Peach Maturity Prediction // Electronics, 10 (2021), 24; 3115, 18 doi:10.3390/electronics10243115 (međunarodna recenzija, članak, znanstveni)


CROSBI ID: 1165334 Za ispravke kontaktirajte CROSBI podršku putem web obrasca

Naslov
Utilization of Explainable Machine Learning Algorithms for Determination of Important Features in ‘Suncrest’ Peach Maturity Prediction

Autori
Ljubobratović, Dejan ; Vuković, Marko ; Brkić Bakarić, Marija ; Jemrić, Tomislav ; Matetić, Maja

Izvornik
Electronics (2079-9292) 10 (2021), 24; 3115, 18

Vrsta, podvrsta i kategorija rada
Radovi u časopisima, članak, znanstveni

Ključne riječi
machine learning ; imbalanced datasets ; peach maturity ; variable importance ; interpretable machine learning ; random forest ; ground color

Sažetak
Peaches (Prunus persica (L.) Batsch) are a popular fruit in Europe and Croatia. Maturity at harvest has a crucial influence on peach fruit quality, storage life, and consequently consumer acceptance. The main goal of this study is to develop a machine learning model that will detect the most important features for predicting peach maturity by first training models and then using the importance ratings of these models to detect nonlinear (and linear) relationships. Thus, the most important peach features at a given stage of its ripening could be revealed. To date, this method has not been used for this purpose, and at the same time, it has the potential to be applied to other similar peach varieties. A total of 33 fruit features are measured on the harvested peaches, and three imbalanced datasets are created using firmness thresholds of 1.84, 3.57, and 4.59 kg·cm−2. These datasets are balanced using the SMOTE and ROSE techniques, and the Random Forest machine learning model is trained on them. Permutation Feature Importance (PFI), Variable Importance (VI), and LIME interpretability methods are used to detect variables that most influence predictions in the given machine learning models. PFI shows that the h° and a* ground color parameters, COL ground color index, SSC/TA, and TA inner quality parameters are among the top ten most contributing variables in all three models. Meanwhile, VI shows that this is the case for the a* ground color parameter, COL and CCL ground color indexes, and the SSC/TA inner quality parameter. The fruit flesh ratio is highly positioned (among the top three according to PFI) in two models, but it is not even among the top ten in the third.

Izvorni jezik
Engleski

Znanstvena područja
Računarstvo, Poljoprivreda (agronomija)



POVEZANOST RADA


Projekti:
MZOS-123 - Inteligentna optimizacija tehnologije upravljanja logistikom hladnog lanca za hranu pomoću Interneta stvari (IPOC) (IPOC) (Matetić, Maja, MZOS - Hrvatsko-kineski znanstveno-istraživački projekt) ( POIROT)
NadSve-Sveučilište u Rijeci-uniri-drustv-18-122 - Dubinska analiza tokova podataka za pametno upravljanje hladnim lancem (SmaCC) (SMACC) (Matetić, Maja, NadSve - Natječaj za dodjelu sredstava potpore znanstvenim istraživanjima na Sveučilištu u Rijeci za 2018. godinu - projekti iskusnih znanstvenika i umjetnika) ( POIROT)

Ustanove:
Agronomski fakultet, Zagreb,
Sveučilište u Rijeci - Odjel za informatiku

Poveznice na cjeloviti tekst rada:

doi www.mdpi.com

Citiraj ovu publikaciju:

Ljubobratović, Dejan; Vuković, Marko; Brkić Bakarić, Marija; Jemrić, Tomislav; Matetić, Maja
Utilization of Explainable Machine Learning Algorithms for Determination of Important Features in ‘Suncrest’ Peach Maturity Prediction // Electronics, 10 (2021), 24; 3115, 18 doi:10.3390/electronics10243115 (međunarodna recenzija, članak, znanstveni)
Ljubobratović, D., Vuković, M., Brkić Bakarić, M., Jemrić, T. & Matetić, M. (2021) Utilization of Explainable Machine Learning Algorithms for Determination of Important Features in ‘Suncrest’ Peach Maturity Prediction. Electronics, 10 (24), 3115, 18 doi:10.3390/electronics10243115.
@article{article, author = {Ljubobratovi\'{c}, Dejan and Vukovi\'{c}, Marko and Brki\'{c} Bakari\'{c}, Marija and Jemri\'{c}, Tomislav and Mateti\'{c}, Maja}, year = {2021}, pages = {18}, DOI = {10.3390/electronics10243115}, chapter = {3115}, keywords = {machine learning, imbalanced datasets, peach maturity, variable importance, interpretable machine learning, random forest, ground color}, journal = {Electronics}, doi = {10.3390/electronics10243115}, volume = {10}, number = {24}, issn = {2079-9292}, title = {Utilization of Explainable Machine Learning Algorithms for Determination of Important Features in ‘Suncrest’ Peach Maturity Prediction}, keyword = {machine learning, imbalanced datasets, peach maturity, variable importance, interpretable machine learning, random forest, ground color}, chapternumber = {3115} }
@article{article, author = {Ljubobratovi\'{c}, Dejan and Vukovi\'{c}, Marko and Brki\'{c} Bakari\'{c}, Marija and Jemri\'{c}, Tomislav and Mateti\'{c}, Maja}, year = {2021}, pages = {18}, DOI = {10.3390/electronics10243115}, chapter = {3115}, keywords = {machine learning, imbalanced datasets, peach maturity, variable importance, interpretable machine learning, random forest, ground color}, journal = {Electronics}, doi = {10.3390/electronics10243115}, volume = {10}, number = {24}, issn = {2079-9292}, title = {Utilization of Explainable Machine Learning Algorithms for Determination of Important Features in ‘Suncrest’ Peach Maturity Prediction}, keyword = {machine learning, imbalanced datasets, peach maturity, variable importance, interpretable machine learning, random forest, ground color}, chapternumber = {3115} }

Časopis indeksira:


  • Current Contents Connect (CCC)
  • Web of Science Core Collection (WoSCC)
    • Science Citation Index Expanded (SCI-EXP)
    • SCI-EXP, SSCI i/ili A&HCI
  • Scopus


Citati:





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