TY  - JOUR
T1  - Comparison of Predictive Performance of Data Mining Algorithms in Predicting Tomato Yield with the A Case Study in Igdir
TT  - Farklı Veri Madenciliği Algoritmalarının Domates Verimindeki Tahmin Performanslarının Karşılaştırılması: Iğdır İli Örneği
AU  - Karadaş, Köksal
AU  - Bulut, Osman Doğan
PY  - 2024
DA  - April
Y2  - 2023
DO  - 10.18016/ksutarimdoga.vi.1215856
JF  - Kahramanmaraş Sütçü İmam Üniversitesi Tarım ve Doğa Dergisi
JO  - KSU J. Agric Nat.
PB  - Kahramanmaras Sutcu Imam University
WT  - DergiPark
SN  - 2619-9149
SP  - 443
EP  - 452
VL  - 27
IS  - 2
LA  - en
AB  - Among the vegetable species in the world, the plant with the most cultivation area is tomato. Increasing tomato yield is important in terms of contributing more to the world economy, producer’s income and human health. With the advancement in software technologies, the importance of data mining algorithms is increasing due to the fact that these algorithms can produce more sophisticated solutions for regression and classification problems. Determining the factors affecting tomato yield and comparing different data mining algorithms on prediction of tomato yield are the purpose of this study. For this purpose, survey study was conducted with the 105 farmers, selected by Simple Random Sampling Method in Igdir province in 2016. Different data mining algorithms including Classification and Regression Tree, Exhaustive CHAID, Chi-Square Automatic Interaction Detector, Artificial Neural Network Algorithm, Multivariate Adaptive Regression Splines and General Linear Model were developed and compared their predictive performance. MARS decision tree has been able to build a model with greatest predictive accuracy, and the others are respectively ANN, GLM, CART, CHAID and Exhaustive CHAID. In the MARS model, number of irrigation , amount of chemical fertilizer , age of farmer , number of seedlings , education level , soil analysis status , sowing region were found statistically significant (P˂0.05). Preferring the MARS model could give an opportunity to detect factors affecting tomato yield and their interactions with higher accuracy. Moreover, results can be easily interpreted and the rules are understandable.
KW  - Data mining algorithms
KW  - Production economics
KW  - Tomato yield
KW  - Igdir
N2  - Domates sebze türleri arasında en fazla ekim alanına sahip bitkidir. Domates veriminin artırılması dünya ekonomisi ve çiftçi gelirine daha fazla katkı sağlaması açısından önemlidir. Yazılım teknolojilerinin ilerlemesi ile regresyon ve sınıflandırma problemlerine daha gelişmiş çözümlerin sunulması veri madenciliğinin önemi artırmaktadır. Bu çalışmada domates verimini etkileyen faktörlerin belirlenmesi ve domates veriminin tahmininde farklı veri madenciliği algoritmalarının karşılaştırılması amaçlanmıştır. Bu amaç ile Iğdır ilinde 105 çiftçi ile anket çalışması yapılmıştır. Sınıflandırma ve Regresyon Ağacı (CART), Ki-Kare Otomatik Etkileşim Dedektörü (CHAID), Exhaustive CHAID, Yapay Sinir Ağı Algoritması (ANN), Çok Değişkenli Uyarlamalı Regresyon Analizi (MARS) ve Genel Doğrusal Model (GLM) gibi farklı veri madenciliği algoritmaları kullanılarak tahmin performansları karşılaştırılmıştır. MARS karar ağacı, en yüksek tahmin doğruluğuna sahip modeli oluşturmuştur. Tahmin performanslarına göre diğer algoritmalar ANN> GLM> CART>  CHAID> Exhaustive CHAID’dır. MARS modelinde, sulama sayısı, kimyasal gübre miktarı, çiftçi yaşı, fide sayısı, eğitim düzeyi, toprak analiz durumu ve ekim bölgesi değişkenleri istatistiksel olarak anlamlı bulunmuştur (P˂0.05). MARS modelinin tercih edilmesi, domates verimini etkileyen faktörleri ve bunların etkileşimlerini daha yüksek doğrulukla tespit edilmesini sağlayacaktır. Verim artışı için dekara en az 1450 fide dikilmeli ve en az 5 defa sulama yapılmalıdır.
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UR  - https://doi.org/10.18016/ksutarimdoga.vi.1215856
L1  - https://dergipark.org.tr/en/download/article-file/2818086
ER  -