Karpuz (Citrullus lanatus (Thunb.) Matsum. & Nakai) Üretimini Etkileyen Faktörlerin Veri Madenciliği ile Tahmini

Yıl 2023, Cilt: 13 Sayı: 2, 1323 - 1334, 01.06.2023

Köksal Karadaş , İbrahim Hakkı Kadirhanoğulları , Meryem Konu Kadirhanoğulları

https://doi.org/10.21597/jist.1177194

Öz

Bu çalışmanın amacı Diyarbakır ilinde karpuz verimini etkileyen faktörleri belirlemektir. Veri Madenciliği Regresyon Ağacı yönteminden Ki-kare otomatik etkileşim detektörü (CHAID) algoritması kullanılan çalışmanın verileri Basit Tesadüfi Örnekleme Yöntemi’ne göre belirlenen 80 adet karpuz üreticisinden elde edilmiştir. Oluşturulan model de Bağımlı değişken WY (karpuz verimi), bağımsız değişkenler R (bölge), AF (çiftçinin yaşı), EL (eğitim düzeyi), CA (ekim alanı), FD (gübreleme zamanı), FA (gübre miktarı), DS (ilaçlama zamanı), AS (ilaç miktarı), NI (sulama sayısı), IT (sulama süresi), AN (çapa sayısı) ve HT (hasat zamanı) olarak belirlenmiştir. Dekar başına ortalama 4488.9 kg karpuz verimi elde edilmiş ve çapa sayısı karpuz verimini en çok etkileyen değişken olmuştur. Sonuç olarak birim alandan daha yüksek verim alabilmek için karpuz üreticilerinin 4 defadan fazla çapa, 2 saatten az olmak üzere 5-6 defa sulama yapmaları ve Mayıs ayında gübre uygulamaları yapmaları gerekmektedir. Ayrıca Çermik, Eğil, Yenişehir ve Bismil karpuz üretimi için daha uygun bölgeler olarak belirlenmiştir.

Anahtar Kelimeler

Veri madenciliği, Regresyon ağacı, Karpuz, Diyarbakır

Prediction of The Factors Affecting Watermelon (Citrullus lanatus (Thunb.) Matsum. & Nakai) Yield Using Data Mining

Öz

The aim of this study was to evaluate the factors of affecting watermelon yield in Diyarbakır province. The data was obtained from surveying of 80 watermelon farmers in Diyarbakır province, Turkey by Simple Random Sampling Method using the Chi-square automatic interaction detector (EXHAUSTIVE CHAID) algorithm of the Data Mining Regression Tree methods. In the model created, the dependent variable was WY (watermelon yield), and the independent variables were determined as R (region), AF (age of farmer), EL (education level), CA (cultivation are), FD (fertilization date), FA (amount of fertilization), DS (date of spraying), AS (amount of spraying), NI (number of irrigation), IT (irrigation time), AN (anchor number), HT (harvest time). As a result of the study, the factors that significantly affect the yield of watermelon; AN, NI, HT, CA, R has been determined. An average of 4488.9 kg watermelon yield per decare was obtained and the number of hoes was the variable that most affected the watermelon yield. As a result in order to get a higher yield per unit area, watermelon producers should anchor number more than 4 times, irrigate 5 to 6 times at less than 2 hours, and apply fertilizer in May. In addition, Çermik, Eğil, Yenişehir and Bismil were determined as more suitable regions for watermelon production.

Anahtar Kelimeler

Data mining, Regression tree, Watermelon, Diyarbakır

Kaynakça

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