Classification Of Rice Diseases Using Deep Convolutional Neural Networks

Abstract

Rice is a primary food source and is one of the rare plants commonly used in industry. Early diagnosis of leaf diseases in rice is crucial to minimize crop damage. Recently, deep learning-based computer-aided systems have gained importance in the agricultural sector and have played an effective role in various applications. These systems not only help with early disease diagnosis but also serve as a secondary aid to those working in agriculture. This study aims to investigate the effectiveness of deep learning methods in the early diagnosis of diseases in rice leaves. To this end, the most popular convolutional neural networks (CNNs), such as VGG, ResNet, DenseNet, EfficientNet, Inception and Xception, were evaluated on the public Paddy Doctor dataset. Current techniques, such as data preprocessing, data augmentation, hyperparameter optimization, and transfer learning, were applied to each model to increase the diagnostic accuracy of the test set. Additionally, the success of each model in diagnosing diseases in rice leaves was compared in detail to other models. The experimental results showed that the EfficientNetv2_Small model performed better than all other models with a test accuracy of 98.01% and F1-score of 97.99%, outperforming other studies in the literature. This study demonstrates that CNN architectures perform well and can effectively assist agricultural engineers and farmers in the early diagnosis of such diseases

Keywords

• Crop disease classification
• Rice disease detection
• Convolutional neural networks
• Deep learning
• Transfer learning

Derin Evrişimli Sinir Ağları Kullanılarak Pirinç Hastalıklarının Sınıflandırılması

Abstract

Çeltik, temel bir gıda kaynağıdır ve endüstride sıkça kullanılan nadir bitkilerden biridir. Çeltik yaprak hastalıklarının erken teşhisi, ekin hasarını en aza indirmek için büyük önem taşımaktadır. Son yıllarda, derin öğrenme tabanlı bilgisayar destekli sistemler, ziraat sektöründe oldukça önem kazanmış ve çeşitli uygulamalarda etkin rol almıştır. Bu sistemler, hastalıkların erken teşhis edilmesine yardımcı olmakla kalmayıp, aynı zamanda tarım alanında çalışanlara da ikincil bir yardımcı olarak katkı sağlamaktadır. Bu çalışma, çeltik yapraklarında bulunan hastalıkların erken teşhisinde derin öğrenme yöntemlerinin etkinliğini araştırmayı amaçlamaktadır. Bu amaç doğrultusunda, VGG, ResNet, DenseNet, EfficientNet, Inception ve Xception gibi en popüler evrişimsel sinir ağları (CNN), halka açık Paddy Doctor veri seti üzerinde değerlendirilmiştir. Her bir modele, veri ön işleme, veri artırma, hiper-parametre optimizasyonu ve öğrenme aktarımı gibi güncel teknikler uygulanarak test setindeki teşhis doğruluğunun başarımı arttırılmıştır. Ayrıca her bir mimarideki modellerin birbirine ve diğer mimarilerdeki modellere göre çeltik yapraklarındaki hastalıkların teşhisindeki başarımları detaylı bir şekilde karşılaştırılmıştır. Deneysel sonuçlar, EfficientNetv2_Small modelinin %98.01 test doğruluğu ve %97.99 F1-skor değerleriyle tüm modellerden daha iyi performans sergilediğini ve literatürdeki diğer çalışmaları geride bıraktığını göstermiştir. Bu çalışma, CNN mimarilerinin yüksek bir performans gösterdiğini ve bu tür hastalıkların erken teşhisinde ziraat mühendislerine ve çiftçilere etkili bir şekilde yardımcı olabileceğini göstermektedir

Keywords

• Bitki hastalığı sınıflandırması
• Pirinç hastalığı tespiti
• Evrişimli sinir ağları
• Derin öğrenme
• Öğrenme aktarımı

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