Using Adaptive Neuro-Fuzzy Inference Systems (Anfis) Technique to Estimating the Density in Pepper (Capsicum Annuum L.) Seed

Adamowski J & C Karapataki. 2010. Comparison of multivariate regression and artificial neural networks for peak urban water-demand forecasting: eva¬luation of different ANN learning algorithms, J. Hydrol. Eng. 15:729–743. https://doi.org/ 10.1061/(asce)he.1943-5584.0000245.

Ahmadianfar I, S Shirvani Hosseini, J He, A Samadi Koucheksaraee & ZM Yaseen. 2022. An improved adaptive neuro fuzzy inference system model using conjoined metaheuristic algorithms for electrical conductivity pre¬diction. Scientific Reports, 14:4934. https://doi.org/10.1038/s41598-022- 08875-w.

Al-Amoudia RH, O Taylana, G Kutlub, A Muslu Canc, O Sagdicd, E Dertlie & MT Yilmaz. 2019. Characterization of chemical, molecular, thermal and rheological properties of medlar pectin extracted at optimum conditions as determined by BoxBehnken and ANFIS models. Food Chemistry, 271:650- 662.

Alzoubi I, MR Delavar, F Mirzaei & BN Arrabi, 2018. ComparingANFIS and integrating algorithm models (ICA-ANN, PSO-ANN, and GA-ANN) for prediction of energy consumption forirrigation land leveling. Geosyst. Eng. 21 (2), 81–94.

Anonymous, 2022. https://www.hortiturkey.com/yazilar/dunya-biber-ureti¬mi-36-milyon-tonu-asti. Date of Acess: 07.10.2022.

Azadbakht M, M Vahedi Torshizi, A Ziaratban & E Ghajarjazi. 2016. Applica¬tion of artificial neural network (ann) in predicting mechanical properties of canola stem under shear loading. Agric. Eng. Int. CIGR 18(2):413-425.

Daneshmand H, T Tavousi, M Khosravi & S Tavakoli. 2015. Modeling mini¬mum tempereture using adaptive neuro-fuzzy inference system based on spectral analysis of climate indices: A case study in Iran. Journal of the Saudi Society of Agricultural Sciences. 14, 33-40. https://doi.org/10.1016/j. jssas.2013.06.001

Dogan, O. 2016. Uyarlamalı Sinirsel Bulanık Çıkarım Sisteminin (ANFIS) Talep Tahmini İçin Kullanımı ve Bir Uygulama. Dokuz Eylül Üniversitesi, İktisa¬di ve idari Bilimler Fakültesi Dergisi. 31:257-288.

Farzaneh V, A Ghodsvali, H Bakhshabadi, M Ganje, Z Dolatabadi & I Carvalho. 2016. Modelling of the selected physical properties of the fava bean with various moisture contents usingfuzzy logic design. Journal of Food Process Engineering, 65, 247–258.

Genuino DAD, BG Bataller, SC Capareda & MDG De Luna. 2017. Application of artificial neural network in the modeling and optimization of humic acid extraction from municipal solid waste biochar, J. Environ. Chem. Eng. 5: 4101–4107, https://doi.org/10.1016/j.jece.2017.07.071.

Gorzelany J, J Belcar, P Kuźniar, G Niedbała & K Pentoś. 2022. Modelling of mechanical properties of fresh and stored fruit of large cranberry using multiple linear regression and machine learning. Agriculture, 12(2), 200.

Güvenç İ. 2020. The Production, Foreign Trade and Comparative Advantages of Peppers in Turkey. KSÜ J. Agric Nat 23(2):441-445.

Jang, JSR. 1993. ANFIS: adaptive-network based fuzzy inference system. IEEE Transactions on Systems, Man, and Cybernetics 23:3, 665–685.

Mohsenin NN. 1970. Physical properties of plant and animal materials. Gordon and Breach Science Publishers, New York, pp: 51-83.

Najafi BS & Faizollahzadeh Ardabili. 2018. Application of ANFIS, ANN, and logistic methods in estimating biogas production from spent mushroom compost (SMC), Resour. Conserv. Recycl. 13:2018 169–178, https://doi.or¬g/10.1016/j. resconrec.2018.02.025.

Özkan Sivritepe H &B Şentürk. 2011. Comparison of Hydro and Halopriming with Dehydration Treatments for Physiological Enhancement of Pepper Seeds. Journal of Agricultural Faculty of Uludag University. 25:(1), 53-64.

Sabzi S, P Javadiki., H Rabani & A Adelkhani. 2013. Mass modeling of Bam orange with ANFIS and SPSS methods for using in machine vision. Measurement. 46:3333-3341. http://dx.doi.org/10.1016/j.measurement.2013.06.005.

Seifi A, M Ehteram & M Dehghani. 2021a. A robust integrated Bayesian multi-model uncertainty estimation framework (IBMUEF) for quantifying the uncertainty of hybrid meta-heuristic in global horizontal irradiation predictions. Energy Convers. Manag. 241, 114-292.

Seifi, A, M Ehteram, F Nayebloei, F Soroush, B Gharabaghi & A Torabi Hag¬highi. 2021b. GLUE uncertainty analysis of hybrid models for predicting hourly soil temperature and application wavelet coherence analysis for correlation with meteorological variables. Soft Computing, 25(16), 10723- 10748.

Senthilselvi A, J Duela, R Prabavathi & D Sara. 2021. Performance evaluation of adaptive neuro fuzzy system (ANFIS) over fuzzy inference system (FIS) with optimization algorithm in de-noising of images from salt and pepper noise. Journal of Ambient Intelligence and Humanized Computing, 1-6.

Sugeno M & GT Kang. 1988. Structure identification of fuzzy model. Fuzzy Sets Syst., vol. 28, pp. 15–33, 1988.

ŞEN Z. 2004. Mühendislikte Bulanık Mantık (Fuzzy) İle Modelleme Prensiple¬ri, Su Vakfı Yayınları, İstanbul.

Takagi T & M Sugeno. 1985. Fuzzy identification of systems and its applicati¬ons to modeling and control. IEEE Trans. Syst., Man, Cybern., vol. 15, pp. 116–132.

Wu, SW, XG Zhou, GM Cao, ZY Liu & GD Wang. 2017. The improvement on constitutive modeling of Nb-Ti micro alloyed steel by using intelligent al¬gorithms. Materials & Design, 116, 676-685.

Yeşiloğlu E. D Yıldırım & YB Öztekin. 2016. Effect of loading position and storage duration on the mechanical properties of abate fetel pear variety. 6th International Conference on Trends in Agricultural Engineering (TAE). pp.714-718.