Classification Of Rice Diseases Using Deep Convolutional Neural Networks

Erkan Vezıroglu; Ishak Pacal; Ahmet Coşkunçay

Makale detayları

Classification Of Rice Diseases Using Deep Convolutional Neural Networks

Yazarlar:

Erkan Vezıroglu | Ishak Pacal | Ahmet Coşkunçay

Yayıncı:

Igdir University

Ana Başlıklar:

Bilgisayar Yazılımı

Dergi:

Iğdır Üniversitesi Fen Bilimleri Enstitüsü Dergisi Vol.13 Issue 2, pp.792-814

Belge Tipi:

Makale

Yayın Tarihi:

1/2023

Belge Dili:

Türkçe

Document ID (Org No.):

2001289854

pISSN:

2536-4618

Anahtar kelimeler:

Özet:

 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.

 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.

 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.

 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.

Makale detayları

Classification Of Rice Diseases Using Deep Convolutional Neural Networks

Yazarlar:

Erkan Vezıroglu | Ishak Pacal | Ahmet Coşkunçay

Yayıncı:

Igdir University

Ana Başlıklar:

Bilgisayar Yazılımı

Dergi:

Iğdır Üniversitesi Fen Bilimleri Enstitüsü Dergisi Vol.13 Issue 2, pp.792-814

Belge Tipi:

Makale

Yayın Tarihi:

1/2023

Belge Dili:

Türkçe

Document ID (Org No.):

2001289854

pISSN:

2536-4618

Crop disease classification

Rice disease detection

Convolutional neural networks

Deep learning

Transfer learningBitki hastalığı sınıflandırması

Pirinç hastalığı tespiti

Evrişimli sinir ağları

Derin öğrenme

Öğrenme aktarımı

Sayı

Makale detayları

Yazarlar: Erkan Vezıroglu | Ishak Pacal | Ahmet Coşkunçay

Yayıncı: Igdir University

Ana Başlıklar: Bilgisayar Yazılımı

Dergi: Iğdır Üniversitesi Fen Bilimleri Enstitüsü Dergisi Vol.13 Issue 2, pp.792-814

Belge Tipi: Makale

Yayın Tarihi: 1/2023

Belge Dili: Türkçe

Document ID (Org No.): 2001289854

pISSN: 2536-4618

Sayı

Yazarlar:

Erkan Vezıroglu | Ishak Pacal | Ahmet Coşkunçay

Yayıncı:

Igdir University

Ana Başlıklar:

Bilgisayar Yazılımı

Dergi:

Iğdır Üniversitesi Fen Bilimleri Enstitüsü Dergisi Vol.13 Issue 2, pp.792-814

Belge Tipi:

Makale

Yayın Tarihi:

1/2023

Belge Dili:

Türkçe

Document ID (Org No.):

2001289854

pISSN:

2536-4618