Predictive Modeling of Net Blotch Disease in Spring Barley Using Artificial Neural Networks (ANN) and XGBoost: A Comparative Study

Yıl 2025, Cilt: 41 Sayı: 2, 484 - 503, 30.08.2025

Recep Sinan Arslan , Nilüfer Akci , Dilara Çelik , Bahatdin Daşbaşı , Kadir Aytaç Özaydın , Teslima Daşbaşı

Öz

The detection of plant diseases and their application to agricultural production have the potential to increase productivity and regulate pesticide use. Time-dependent meteorological climate data play an active role in plant disease control practices.The aim of this study was to develop a prediction model for the presence of net blotch disease in barley plants in Turkey. The machine learning and artificial neural network-based model was developed and evaluated using a three-year disease development period (2021-2023) as the training and testing dataset. The model was trained using nine different meteorological climate data, including temperature(min, max, avg), humidity, rainfall, wind speed, sun exposure time, actual vapor pressure, dew point temperature. The outcomes of the evaluation process yielded an 88.9% accuracy for the XGBoost classifier and a noteworthy 92.16% accuracy value for the Artificial Neural Network (ANN).The precision, recall and f-score values for the ANN model were found to be 96.10%, 89.16% and 92.50%, respectively. The findings indicated that the model exhibited superior performance in detecting the absence of the disease when compared to existing studies. Furthermore, the impact of nine distinct inputs on the manifestation of the disease and the interrelationships between these inputs were analysed. The analysis revealed that sun exposure time, temperature, wind speed and humidity factors exerted the most substantial influence on environmental variables. The high performance obtained from the model lends further credence to the hypothesis that artificial intelligence models can be successful in combating the disease and that an increase in productivity can be achieved in the product by reducing the negative effects of the disease.

Anahtar Kelimeler

Barley , Net Blotch , Machine Learning , Artificial Neural Network , Advanced Feature Analysis

Destekleyen Kurum

Scientific and Technological Research Council of Turkey (TUBITAK)

Proje Numarası

124F251

Teşekkür

This study was supported by Scientific and Technological Research Council of Turkey (TUBITAK) under the Grant Number 124F251. The authors thank to TUBITAK for their supports.

Yapay Sinir Ağları (YSA) ve XGBoost Kullanılarak İlkbahar Arpasında Net Leke Hastalığı Tahmin Modeli: Karşılaştırmalı Bir Çalışma

Öz

Bitki hastalıklarının tespiti ve bunun tarımsal üretime uygulanmasıyla üründe hem verimlilik artışının sağlanması hemde pestisit kullanımının düzenlenmesi mümkün olabilecektir. Bitki hastalıkları ile yapılacak mücadele uygulamalarında zamana bağlı meteorolojik iklim verileri etkin rol oynamaktadır. Bu çalışma kapsamında Türkiye’de arpa bitkisinde net blotch hastalığının varlığına ilişkin bir tahmin modelinin geliştirilmesi amaçlanmıştır. Sınıflandırma için sıcaklık, nem, yağış miktarı, rüzgar hızı, güneşlenme şiddeti, gerçek buhar basıncı ve çiğ noktası sıcaklığı olarak verilen 9 farklı meteorolojik iklim verilerinin makine öğrenmesi ve yapay sinir ağı tabanlı modelin eğitim ve test süreçlerlerinde kullanılması yaklaşımı benimsenmiştir. 2021-2023 yılları arasındaki 3 yıllık bir hastalık gelişimi dönemine ait veriler kullanılmıştır. Yapılan testler sonucunda XGBoost sınıflandırıcı ile 88.9% Accuracy değeri elde edilirken, Yapay sinir ağı (YSA) ile 92.16% Accuracy değeri elde edilmiştir. YSA modeli için precision, recall ve f-score değerleri sırasıyla 96.10%, 89.16% ve 92.50% olmuştur. Sonuçlar modelin hastalığın yokluğunu tespit etmede literatürdeki çalışmalara kıyasla en iyi başarıma sahip çalışmalarıdan birisi olduğunu göstermiştir. Bunun yanında 9 farklı girdinin hastalığın meydana gelmesine etkileri ve girdilerin kendi aralarındaki ilişkileri de analiz edilmiştir. Sonuçta, sıcaklık, güneşlenme süresi ve nem faktörlerinin, çevresel değişkenler üzerinde en belirgin etkiye sahip olduğu anlaşılmıştır. Elde edilen yüksek başarım, hastalığın mücadelesinde yapay zeka modellerinin başarılı olabileceğini ve hastalığın olumsuz etkisini düşürerek üründe verimlik artışının yakalanabileceğini kanıtlamıştır.

Anahtar Kelimeler

Arpa , Net Leke , Makine Öğrenmesi , Yapay Sinir ağları , Gelişmiş Özellik Analizi

Proje Numarası

124F251

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