Comparison of Different Machine Learning Methods for Estimating Agricultural Products

Year 2022, Volume: 3 Issue: 1, 12 - 21, 29.06.2022

Halit Çetiner

Abstract

Dünya ekonomisi üzerinde farklı etkileri olan mahsullerin verim tahminlerini gerçekleştirmek için iki farklı makine öğrenmesi yöntemi ile regresyon analizi yapılmıştır.Regresyon analizinde kullanılan iki yöntem DVM (Destek Vektör Makinesi) ve HTGA (Histogram Tabanlı Gradyan Artırma) regresyon analiz yöntemleridir. Her iki regresyon analiz yöntemi de çok farklı tarımsal problemlerin temelinde yatan verim tahminini en az hata ile bulmaya çalışmaktadır. Bu anlamda deneysel çalışmalar gerçekleştirebilmek için 1961 yılından başlayarak 2016 yılına kadar ki 35 yıllık aralıkta Dünya Veri Bankası ve FAO dünya tarım örgütü veri tabanlarında bulunan yağış, sıcaklık, pestisit girdi değerleri kullanarak verim tahmininde bulunulmuştur. Verim tahminleri sonucunda HTGA ile 94% R2 puanına ulaşılırken, DVM Poly çekirdeği ile 91% R2 puanına erişilmiştir. DVM yönteminde ise 3 farklı çekirdek kullanarak regresyon analizi gerçekleştirilmiştir. Poly çekirdek değeri sonuçları 91% R2 puanına ulaşırken RBF ve Linear çekirdek değerleri sırasıyla 81% ve 69% R2 puanına ulaşmıştır.

Keywords

DVM, HTGA, Verim tahmini, Regresyon analizi

Comparison of Different Machine Learning Methods for Estimating Agricultural Products

Abstract

Regression analysis was carried out with two different machine learning methods in order to realize the yield estimates of crops that have different effects on the world economy. The two methods used in regression analysis are SVM (Support Vector Machine) and HTGA (Histogram Based Gradient Augmentation) regression analysis methods. Both regression analysis methods try to find the yield estimation underlying many different agricultural problems with the least error. In this sense, to carry out experimental studies, yield estimations were made using precipitation, temperature, pesticide input values found in the World Data Bank and FAO World Agricultural Organization databases in the 35-year interval from 1961 to 2016. As a result of efficiency estimation, the 94% R2 score was reached with HTGA, while the 91% R2 score was reached with the SVM Poly core. In the SVM method, regression analysis was performed using 3 different kernels. The results of the Poly core values reached 91% R2 scores, while the RBF and linear core values reached 81% and 69% R2 scores, respectively.

Keywords

SVM, HTGA, Yield estimation, Regression analysis

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