Crop Yield Prediction by Integrating Meteorological and Pesticides Use Data with Machine Learning Methods: An Application for Major Crops in Turkey

Year 2022, , 1 - 18, 24.10.2022

Hasan Arda Burhan

https://doi.org/10.30784/epfad.1148948

Cited By: 3

Abstract

Agriculture, as one of the most important and vital human activity, is highly vulnerable to global, local and environmental issues. This fragility also surfaced in the initial stages of the COVID-19 pandemic. Accordingly, such matters are considered to have dramatic impacts on demand and pricing dynamics of agricultural products. Nonetheless, improving crop yield and its estimation is the fundamental goal of agricultural activities. To cope with the rapidly changing circumstances, Turkey needs to keep developing data-based agricultural information systems which is also stated as one of the main objectives of the 11th development plan. Therefore, accurate crop yield prediction appears to be a critical task. In this context, using meteorological parameters, pesticides use and crop yield values during 1990-2019, evaluation of machine learning regression methods in the yield prediction of nine major crops in Turkey can be stated as the main aim of this research. After the training, all models are used to predict crop yields and acquired values were compared with actual figures. The results showed that successful predictions were obtained by using the Decision Tree Regression (DTR) and Random Forest Regression (RFR) especially for wheat, barley and maize yields; however, Support Vector Regression (SVR) showed inconsistent predictions.

Keywords

Crop Yield Prediction, Machine Learning, Decision Tree Regression, Random Forest Regression

Meteoroloji ve Tarım İlacı Kullanım Verilerinin Makine Öğrenmesi Yöntemlerine Entegre Edilmesi Yoluyla Tarımsal Üretim Tahmini: Türkiye’deki Başlıca Mahsuller İçin Bir Uygulama

Abstract

En önemli ve hayati insan faaliyetlerden biri olarak tarım, küresel, yerel ve çevresel sorunlara karşı oldukça savunmasızdır. Bu kırılganlık COVID-19 pandemisinin ilk aşamalarında da görülmüştür. Bu bağlamda, söz konusu durumların tarımsal ürünlerin talep ve fiyatlama dinamikleri üzerinde önemli etkilerinin olduğu söylenebilmektedir. Yine de tarımsal faaliyetlerin temel amacı, mahsul verimi ve üretimini iyileştirmek olduğu ifade edilebilir. Türkiye'nin hızla değişen koşullarla başa çıkabilmesi için, 11. Kalkınma Planının da ana hedeflerinden biri olarak belirtilen veriye dayalı tarımsal bilgi sistemlerini geliştirmeye devam etmesi gerekmektedir. Dolayısıyla doğru üretim miktarı tahmini, kritik bir görev olarak öne çıkmaktadır. Bu doğrultuda, 1990-2019 dönemi için meteorolojik parametreler, tarım ilacı kullanımı ve rekolteye dayalı veri setlerini kullanarak, Türkiye'deki dokuz ana mahsulün üretim miktarı tahmininde makine öğrenmesi yöntemlerinin geçerliliğinin değerlendirilmesi, bu çalışmanın temel amacı olarak ifade edilebilir. Eğitim aşamasından sonra tüm modellerle üretim miktarı tahmini yapılmış, elde edilen sonuçlar gerçek değerlerle karşılaştırılmıştır. Sonuçlara göre Karar Ağacı Regresyon (KAR) ve Rastgele Orman Regresyon (ROR) yöntemleriyle, bilhassa buğday, arpa ve mısır için başarılı tahminler alınmış, Destek Vektör Regresyon (DVR) yönteminin ise tutarsız tahminler verdiği görülmüştür.

Keywords

Tarımsal Üretim Tahmini, Makine Öğrenmesi, Karar Ağacı Regresyon, Rastgele Orman Regresyon

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