Using Anova and Anfis Approaches in Statistical Modeling of Agricultural Experiments

Year 2022, , 574 - 597, 23.07.2022

Zeynep Gökkuş , Sevil Şentürk , Firat Alatürk , Hülya Hanoğlu Oral , Ahmet Gökkuş

https://doi.org/10.30910/turkjans.1101600

Cited By: 1

Abstract

Adaptif Ağ Tabanlı Bulanık Çıkarım Sistemi (ANFİS), teknoloji, üretim, sağlık, sosyal ve eğitim gibi pek çok branşta, ilgilenilen konuyu etkileyen faktörleri ve faktör düzeylerini, oluşturduğu çok sayıda kurala bağlı olarak ve çok küçük bir deneysel hata ile analiz edebilmekte ve modelleyebilmektedir. Tarım alanında da özellikle tarımsal alan seçimi ve teknolojik ürün geliştirme gibi problemlerin çözümü için uygulanmaktadır. Ürün yetiştirilmesi gibi belirli bir zaman aralığındaki durum tespit çalışmalarında ise genellikle klasik istatistik yöntemlere başvurulmaktadır. Bu yöntemlerin başında da deney tasarımı yöntemleri veya başka bir deyişle varyans analizi (ANOVA) yöntemleri gelmektedir. ANOVA ile modellenen deneyler ile ilgilenilen konuyu etkileyen faktörler ve bu faktörlerin düzeyleri, kullanılan yönteme ait tek bir kurala göre analiz edilir. ANOVA’nın tek kuralına karşılık ANFİS’in çok sayıda kuralı ile oluşturduğu modelin deneysel hatası (RMSE) çok daha küçük olduğundan daha güçlü sonuçlar vermektedir. Tarımsal ürünlerin zamana bağlı olarak ANFİS ile modellenmesi, bu alanda veri madenciliği çalışmalarını destekleyebilecektir. Bu çalışmada tarım alanında gerçekleştirilen bir durum tespit çalışması hem ANOVA hem de ANFİS ile modellenmiş ve benzer bulgular elde edilmiştir. Bununla birlikte çoğunlukla ANFİS’e ait RMSE değerleri ANOVA’dan küçük bulunmuştur. Ayrıca ANFİS çıktıları ile gerçek ölçümler arasındaki ilişkiler incelenmiştir.

Keywords

Modeling of agricultural experiments, ANFIS, ANOVA

Using ANOVA and ANFİS Approaches in Modelling Agricultural Experiments

Abstract

Adaptive Neuro-Fuzzy Inference System (ANFIS) can analyze the factors and factor levels affecting the subject of interest in many branches such as technology, production, health, social and education, depending on the many rules it creates and with a very small experimental error (RMSE). and modelling. It is also applied in the field of agriculture, especially for the solution of problems such as agricultural field selection or technological product development. On the other hand, classical statistical methods are generally used in due diligence studies in a certain time period, such as product cultivation. Experimental design methods or in other words analysis of variance (ANOVA) methods come first among these methods. With the experiments modeled by ANOVA, the factors affecting the subject of interest and the levels of these factors are analyzed according to a single rule of the method used. Since the Root Mean Square Error (RMSE) of the model formed by the multiple rules of ANFIS versus the single rule of ANOVA is much smaller, it gives stronger results. Modeling agricultural products with ANFIS depending on time will support data mining studies in this field. In this study, first both ANOVA and ANFIS methods were briefly explained, and then the data of a due diligence study carried out in agriculture were modeled by both methods and similar findings were obtained. However, mostly the standard deviation (RMSE) values of ANFIS were found to be smaller than ANOVA. In addition, the relationships between ANFIS outputs and real measurements were examined.

Keywords

Modeling of agricultural experiments, ANFIS, ANOVA.

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