Akademik Veri Yönetim Sistemi
Iğdır Üniversitesi Fen Bilimleri Enstitüsü Dergisi, cilt.12, sa.4, ss.1-12, 2022 (Hakemli Dergi)
Sunflower constitutes
an important source of protein, mineral, vitamin, fatty acid, and offer a
balanced source of amino acids. Machine learning is mostly performed for the
prediction of descriptive attributes in the quality evaluation of foods. In this
study physical attributes of two different sunflower varieties (Metinbey and
İnegöl Alası) were determined and algorithms were applied for size and shape
prediction of these varieties. In addition, five different machine learning
predictors were used as Multilayer Perceptron (MLP), Gaussian Processes (GP),
Random Forest (RF), k-Nearest Neighbors (kNN), and Support Vector Regression (SVR).
The prediction of surface area, volume, geometric mean diameter, aspect ratio,
elongation, and shape index were based on the main physical attributes. İnegöl
Alası variety had the greatest physical attributes. The seed length, width and
thickness were obtained from İnegöl Alası variety as 23.89, 8.80 and 4.15 mm
and from Metinbey as 17.88, 6.20 and 4.01 mm. All varieties were determined as
significant in terms of the selected attributes as reported by Pillai Trace and
Wilks’ Lambda (p<0.01). In the Wilks’ Lambda statistics, unexplained of the
similarities or differences among the groups was 12.30%. Present findings
revealed that MLP and SVR algorithms had the greatest correlation coefficients
for all predicted attributes. In the study, the best predicted attributes were
geometric mean diameter with an R value of 0.9989 (SVR), followed by volume and
elongation with an R value of 0.9988 (MLP). Present findings revealed that MLP
and SVR algorithms could potentially be used for size and shape prediction of
sunflower varieties.