Türkiye'de Pamuk Üretiminin Tahmini İçin Yapay Sinir Ağı Yöntemleri Uygulaması

Year 2021, Volume: 8 Issue: 4, 1018 - 1027, 24.10.2021

Nazire Mıkaıl Mehmet Fırat Baran

https://doi.org/10.30910/turkjans.947978

Abstract

Yetiştiriciler bitkisel üretimde verimi etkileyen faktörleri hep merak etmişlerdir. Bu faktörlerin belirlenmesi ilerideki verim hakkında bilgi verebilir. Bilginin güvenilirliği iyi bir tahmin modeline bağlıdır. Çalışma sürecine göre yapay sinir ağları, insandaki sinir ağını taklit eder. Yapay sinir ağlarında insanların farklı deneyimlerden edindiği bilgileri birleştirerek mevcut duruma yönelik tahminler yapabilme yeteneği tasarlanmıştır. Bu nedenle karmaşık problemlerde yapay sinir ağlarına göre daha iyi sonuç verir. Bu çalışmada, pamuk üretimini modellemek için yapay sinir ağı yöntemi kullanılmıştır. Türkiye, Diyarbakır'da 73 işletmeyi kapsayan kapsamlı bir veri koleksiyonundan, ortalama pamuk üretimi 559,19 kg/da olarak hesaplanmıştır. Bu modele temel girdi olarak seçilen dört faktör vardır. Sonuç olarak, nihai YSA modeli, işletme durumları (pamuk alanı ve sulama periyodu), makine kullanımı ve gübre tüketimi gibi unsurlara dayanan pamuk üretimini gösterebilmektedir. Çalışma sonunda pamuk verimi %84 doğrulukla tahmin edilmiştir.

Keywords

Tarla ürünü, verim, pamuk, ANN, SVM, Türkiye

Application of Artificial Intelligence Methods to Predict Cotton Production in Turkey

Abstract

Farmers are always curious about the factors affecting yield in plant production. Determining these factors can give information about the yield in the future. Reliability of information is dependent on a good prediction model. According to the operating process artificial neural networks imitate the neural network in humans. The ability to make predictions for the current situation by combining the information people have gained from different experiences is designed in artificial neural networks. Therefore, in complex problems, it gives better results than conventional statistical methods.
In this study, artificial neural networks and support vector machines methods of artificial intelligence were used in order to predict the production of cotton. From a comprehensive data collection spanning 73 farms in Diyarbakır, Turkey, the mean cotton production was prevised at 559.19 kg da-1. There is four factors that picked as pivotal input into this model. As a result, the ultimate artificial neural network model is able to foreshow cotton production, which is built on elements like: farm states (cotton area and irrigation periodicity), machinery usage and fertilizer consumption. At the end of the study, cotton yield was estimated with %84 accuracy.

Keywords

Crop fields, yield, cotton, ANN, SVM, Turkey

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