Year 2021, Volume , Issue 23, Pages 540 - 546 2021-04-30

Detection of Peach Diseases with Deep Learning
Derin Öğrenme ile Şeftali Hastalıkların Tespiti

Muzaffer ASLAN [1]


Agricultural products are very important in meeting the nutritional needs of living creatures in the world. The rapid increase in the world population makes it necessary to increase the productivity in agricultural products. It is very important to ensure product productivity in limited agricultural areas and to detect diseases that can be seen in plants effectively and on time. Especially the short life of some fruit trees makes it more important to detect the diseases in these trees accurately, on time and quickly. Deep learning, which has been widely used in image processing recently, offers effective applications in agricultural activities. In this study, convolutional neural network method is proposed to detect peach tree diseases. In the proposed method, the detection of the disease with monilya laxa and sphaerolecanium prunastri in peach trees was made with the previously trained AlexNet model. Experimental studies were carried out with a dataset consisting of real disease images taken from the TRB1 region. In experimental studies, the disease was detected with an accuracy of 99.30%. Achieved 1.44% higher accuracy than existing studies. 

Tarım ürünleri, dünyadaki canlıların beslenme ihtiyaçlarının karşılanması bakımından oldukça önemlidir. Dünya nüfusundaki hızlı artış tarımsal ürünlerde verimliğin arttırılmasını zorunlu hale getirmektedir. Sınırlı tarım alanlarında ürün verimliliğinin sağlanabilmesi bitkilerde görülebilecek hastalıklarının etkili bir şekilde ve zamanında tespiti oldukça önemlidir. Özellikle bazı meyve ağaçlarının kısa ömürlü olması bu ağaçlardaki hastalıkların doğru, zamanında ve hızlı bir şekilde tespitini daha önemli hale getirmektedir. Son zamanlarda görüntü işlemede yaygın olarak kullanılan derin öğrenme, tarımsal faaliyetlerde etkili uygulamalar sunmaktadır. Bu çalışmada, şeftali ağacı hastalıklarının tespiti için evrişimli sinir ağ yöntemi önerilmiştir. Önerilen yöntemde, daha önceden eğitilmiş AlexNet modeli ile şeftali ağaçlarında görülen monilya ve koşnili hastalık tespiti yapılmıştır. Deneysel çalışmalarda, TRB1 bölgesinden alınan gerçek hastalık görüntülerinden oluşan veri seti ile gerçekleştirildi. Yapılan deneysel çalışmalarda %99,30 doğruluk oranında hastalık tespiti yapılmıştır. Mevcut çalışmalardan %1,44 daha yüksek doğruluk oranına sağlandı.
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Primary Language tr
Subjects Engineering
Journal Section Articles
Authors

Orcid: 0000-0002-2418-9472
Author: Muzaffer ASLAN (Primary Author)
Institution: BİNGÖL ÜNİVERSİTE MÜHENDİSLİK MİMARLIK FAKÜLTESİ, ELEKTRİK-ELEKTRONİK MÜHENDİSLİĞİ BÖLÜMÜ
Country: Turkey


Dates

Publication Date : April 30, 2021

APA Aslan, M . (2021). Derin Öğrenme ile Şeftali Hastalıkların Tespiti . Avrupa Bilim ve Teknoloji Dergisi , (23) , 540-546 . DOI: 10.31590/ejosat.883787