Classification of Citrus Diseases with Convolutional Neural Network Based Deep Learning Model

Abstract

Plant diseases are vital for crop yield in agricultural production. It is difficult and tiring to detect diseases in plants at an early stage due to the similarity of features such as colour, shape and texture in plants. Detecting diseases in plants at an early stage and taking precautions is a necessary step to prevent damage to the crop. For this reason, a deep learning-based model has been developed within the scope of the study to classify leaf diseases that affect citrus imports and cause great financial losses to producers. In addition, leaf diseases were classified with three different models based on DenseNet121, MobileNetV2 and ResNet50 architectural models. Fine-tuned transfer learning technique was used in the creation of these models. With the 15-layer CNN model proposed within the scope of the study, 99% accuracy rates were achieved with the Adamax optimization method and 97% with the RMSProp optimization method. Blackspot (citrius black spot (CBS)), canker (citrius bacterial cancer (CBC)), greening (huanglongbing (HLB)) and (healthy), which are the most common citrus leaf diseases, are 100%, 100%, 98%, respectively, in Health classes. and 100% success rates have been reached.

Keywords

• CNN
• Citrus leaf disease
• Classification
• Transfer learning

Konvolüsyonel Sinir Ağı Tabanlı Derin Öğrenme Modeli ile Narenciye Hastalıklarının Sınıflandırılması

Abstract

Tarımsal üretimde mahsul verimi için bitki hastalıkları hayati öneme sahiptir. Bitkilerde bulunan renk, şekil, doku gibi özelliklerin birbirine benzemesinden dolayı bitkilerdeki hastalıkların erken aşamada tespiti zor ve yorucu olmaktadır. Bitkilerdeki hastalıkların erken aşamada tespit edilerek önlem alınması mahsule gelen zararın engellemesi için gerekli bir adımdır. Bu nedenle, narenciye ithalatını etkileyerek üreticileri maddi olarak büyük zararlara uğratan yaprak hastalıklarını sınıflandırmak için yapılan çalışma kapsamında derin öğrenme tabanlı bir model geliştirilmiştir. Buna ek olarak DenseNet121, MobileNetV2 ve ResNet50 mimari modellerini temel alan üç ayrı model ile de yaprak hastalıkları sınıflandırılmıştır. Bu modellerin oluşturulmasında ince ayarlı transfer öğrenme tekniği kullanılmıştır. Yapılan çalışma kapsamında önerilen 15 katmanlı CNN modeli ile Adamax optimizasyon yöntemi ile %99, RMSProp optimizasyon yöntemi ile de %97 doğruluk oranlarına ulaşılmıştır. En sık karşılaşılan narenciye yaprak hastalıklarından olan Blackspot (citrius siyah nokta (CBS)), canker (citrius bakteriyel kanseri (CBC)), greening (huanglongbing (HLB)) ile (sağlıklı) Health sınıflarında ise sırasıyla %100, %100, %98 ve %100 başarı oranlarına erişilmiştir.

Keywords

• CNN
• Narenciye yaprak hastalığı
• Sınıflandırma
• Transfer öğrenme

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