20.500.12556/DKUM-64866 Možnost uporabe metod strojnega učenja pri napovedovanju razvoja krovne barve jabolk (malus domestica borkh.) Using machine learning methods for apple skin color prediction (Malus domestica Borkh.) Napovedovanje razvoja parametrov kakovosti je v kmetijski proizvodnji ob zahtevah sodobnega potrošnika postalo zelo pomembno. Tipičen parameter, ki ga potrošnik neposredno povezuje s kakovostjo, je barva kožice plodov. V raziskavi smo se osredotočili na možnosti napovedovanja razvoja in intenziviranja krovne barve jabolk, katere intenzivnost smo določili z barvnim parametrom a*. Za metodo strojnega učenja smo uporabili nadzorovano učenje, modele pa generirali s šestimi različnimi odločitvenimi drevesi (Decision Stump, J48, LMT, Random Forest, Random Tree in Rep Tree). Namen raziskave je bil zgraditi modele, ki bodo na podlagi vremenske napovedi omogočali sprejemljivo natančnost (< 60-% natančnost) napovedi razvoja krovne barve oz. barvnega parametra a* za tri obravnavane sorte jabolk (‘Gala Brookfield’, ‘Fuji Kiku 8’ in ‘Braeburn Mariri red’). Pri sorti ‘Gala Brookfield’ smo najbolj natančne modele generirali z uporabo odločitvenega drevesa J48 (89,13-% natančnost). Pri sorti ‘Fuji Kiku 8’ smo najbolj natančen model dobili z uporabo odločitvenega drevesa LMT (91,73-% natančnost), s tem modelom (LMT) smo dobili najbolj natančen model napovedi barvnega parametra a* tudi za sorto ‘Braeburn Mariri red’ (96,65-% natančnost). Model LMT je izmed vseh generiranih modelov (36) in obravnavanih sort (3) dal najbolj natančno napoved barvnega parametra a*, kar pripisujemo predvsem najbolj natančnim podatkom, ki smo jih s statistično obdelavo dobili pri sorti ‘Braeburn Mariri red’. Ugotovili smo, da je uporabnost napovedovalnih modelov odvisna predvsem od natančnosti vremenske napovedi. Pri 7-dnevni uradni vremenski napovedi, ki je bila uporabljena pri naših ekspertnih modelih, se natančnost modelov v povprečju zniža za 10,73 %. Znižanje natančnosti napovedovalnih modelov pripisujemo zelo variabilni vremenski napovedi, s katero smo v času raziskave razpolagali. Prediction of the development of quality parameters in agricultural production with the requirements of the modern consumer has become very important. A typical parameter which the consumer directly relates to the quality is the colour of the fruit skin. In this thesis, we focused on the possibility of predicting the development and intensification of colour of apples, whose intensity was determined by the colour parameter a *. We used supervised learning as a machine learning method. Models generated six different decision trees (Decision Stump, J48, LMT, Random Forest, Random Tree and Tree Rep). The purpose of the research was to build models that will base on weather forecasts which allow for acceptable accuracy (<60% accurate) of predictions on the development of the fruit colour or colour parameter a * of the three apple varieties ('Brookfield Gala', 'Fuji Kiku 8' and 'Braeburn Mariri red'). Regarding the variety 'Gala Brookfield', we have generated the most accurate models using a decision tree J48 (89.13-% accuracy). In addition, the variety 'Fuji Kiku 8' obtained the most accurate model by using a decision tree LMT (91.73-% accuracy). While using this model (LMT), we get the most accurate prediction model of colour parameter a * for the variety 'Braeburn Mariri red' (96.65-% accuracy). Model LMT, out of all generated models (36) and the varieties (3) considered, predicted colour parameters a * the most accurately. That can be ascribed to the most accurate data, statistically processed with variety 'Braeburn Mariri red'. We can conclude that the applicability of the predictive models depends mainly on the accuracy of weather forecasts. Using a 7-day official weather forecast with our expert models, shows that the accuracy of the models is reduced by 10.73% on average. A reduction in accuracy of predictive models is attributed to the very variable weather forecast, which was at disposal during our research. strojno učenje napovedovanje kakovostni parametri jabolka odločitvena drevesa machine learning prediction quality parameters apple decision trees true true false Slovenski jezik Angleški jezik Doktorsko delo/naloga 2017-01-05 20:09:37 2017-02-21 11:45:05 2022-07-06 03:06:58 2019-07-11 15:00:01 2017 0 Maribor 0 0000-00-00 NiDoloceno NiDoloceno NiDoloceno 0000-00-00 0000-00-00 0000-00-00 URN:SI:UM:DK:N3RA7T08 DOK_Germsek_Blaz_2017.pdf DOK_Germsek_Blaz_2017.pdf 1 E58D67FD32558E4FBE0B124AC591828E 0541ef18b1101f46605e3eac8605c8453eb2bc7c553a002dbceb1011f6971e5a f4abc6e3-7c0d-11eb-bb7a-00155d0001ca https://dk.um.si/Dokument.php?lang=slv&id=109439 Fakulteta za kmetijstvo in biosistemske vede 0 0 0