Bitki Hastalıklarının Tespiti için Ön İşlemli Görsel Dönüştürücü Tabanlı bir Yaklaşım

Öz

İnsanlığın ihtiyaç duyduğu gıda miktarı, dünya nüfusuna paralel olarak artmalıdır. Gıda yetiştiriciliğinin en önemli noktası sağlıklı yetişen bitkilerdir. Bu çalışma, tarımsal verimliliği ve sürdürülebilirliği artırmak için gerekli olan bitki hastalıklarının tespitinin kritik konusunu ele almaktadır. Çalışma, bitki hastalıklarını yüksek doğrulukla tespit etmek için derin öğrenme modelleri ve bölütlemeye dayalı Vision Transformer (Vit 16B) modeli ile birleştirilmiş görüntü işleme tekniklerini kullanan yeni bir yöntem sunmaktadır. Modelin akıllı tarımda uygulanması, daha iyi hastalık yönetimine ve ekonomik faydalar sağlayacağı ortaya koymuştur. Çalışmada aynı zamanda önerilen modelin performansını, 8 farklı derin öğrenme mimarileriyle karşılaştırarak önerilen modelin başarımı ortaya konmuştur. Önerilen model, derin öğrenme modellerinden daha iyi performans gösterdiği ve %99,87'lik etkileyici bir doğruluk elde edildiği saptanmıştır. Sonuçlar, her sınıf için hassasiyet, hatırlama ve F1 puanları gibi ayrıntılı ölçümlerle desteklenmiştir.

Anahtar Kelimeler

• Derin Öğrenme
• Görüntü Dönüştürücü
• Görüntü İşleme
• Akıllı Tarım
• Tarımsal Verimlilik
• Bitki Hastalıkları

A Pre-Processed Visual Transformer Based Approach for Plant Disease Detection

Öz

The amount of food needed by humanity is increasing along with the world population. For healthier and higher yields, plants need to grow healthy. Diseases that occur in plants reduce the yield in crop cultivation. This study addresses the detection of plant diseases, which is necessary to increase agricultural productivity and sustainability. The study presents a new method that uses image processing techniques combined with deep learning models and segmentation-based Vision Transformer (Vit 16B) model to detect plant diseases with high accuracy. The aim of the model is to use it in smart agriculture, to enable early and accurate plant disease detection and to obtain high economic efficiency. In the study, the performance of the proposed model was compared with 8 different deep learning (AlexNet, SqueezeNet, ResNet18, ResNet50, ResNet101, GoogleNet, Vgg16, Vgg19) models and its success was demonstrated. It was determined that the proposed model outperformed the deep learning models and achieved an accuracy of 99.87%. For each class, the performances are demonstrated with the evaluation metrics of precision, recall and F1 scores.

Anahtar Kelimeler

• Deep Learning
• Vision Transformer
• Image Processing
• Smart Agriculture
• Agricultural Productivity
• Plant Disease

Kaynakça

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