Innovative Approaches to Rice (Oryza sativa) Crop Health: A Comprehensive Analysis of Deep Transfer Learning for Early Disease Detection

Utpal Barman; Dulumani Das; Gunikhan Sonowal; Mala Dutta

Makale detayları

Innovative Approaches to Rice (Oryza sativa) Crop Health: A Comprehensive Analysis of Deep Transfer Learning for Early Disease Detection

Yazarlar:

Utpal Barman | Dulumani Das | Gunikhan Sonowal | Mala Dutta

Yayıncı:

Van Yuzuncu Yıl University

Ana Başlıklar:

Phytopathology | Plant Protection (Other)

Dergi:

Yuzuncu Yıl University Journal of Agricultural Sciences Vol.34 Issue 2, pp.314-322

Belge Tipi:

Makale

Yayın Tarihi:

6/2024

Belge Dili:

İngilizce

Document ID (Org No.):

2001319663

pISSN:

1308-7576

Anahtar kelimeler:

Özet:

 In this research, the primary objective is to tackle the pressing issue of identifying and effectively managing diseases in rice plants, a problem that can results in substantial crop losses and pose a severe threat to food security. The study employs Convolutional Neural Networks CNNs, a type of deep learning model widely used for image analysis, to conduct an extensive investigation using a sizable dataset comprising 5,932 RGB images. These images represent four distinct disease classes in rice plants: Bacterial Leaf Blight BLB, Blast, Brownspot, and Tungro. To conduct this research, the dataset is split into two subsets: a training set, which comprises 80 of the data, and a testing set, which makes up the remaining 20. This division allows for a systematic evaluation of the performance of four different CNN architectures: VGGNet, ResNet, MobileNet, and a simpler CNN model. The results of this study consistently show that ResNet and MobileNet outperform the other CNN architectures in terms of their ability to accurately detect diseases in rice plants. These two models consistently achieve remarkable accuracy in identifying these diseases. The research findings not only emphasize the potential of deep learning techniques in addressing the critical issue of rice crop diseases but also highlights the significant role that ResNet and MobileNet play in strengthening crop protection efforts and contributing to global food security.

 In this research, the primary objective is to tackle the pressing issue of identifying and effectively managing diseases in rice plants, a problem that can results in substantial crop losses and pose a severe threat to food security. The study employs Convolutional Neural Networks CNNs, a type of deep learning model widely used for image analysis, to conduct an extensive investigation using a sizable dataset comprising 5,932 RGB images. These images represent four distinct disease classes in rice plants: Bacterial Leaf Blight BLB, Blast, Brownspot, and Tungro. To conduct this research, the dataset is split into two subsets: a training set, which comprises 80 of the data, and a testing set, which makes up the remaining 20. This division allows for a systematic evaluation of the performance of four different CNN architectures: VGGNet, ResNet, MobileNet, and a simpler CNN model. The results of this study consistently show that ResNet and MobileNet outperform the other CNN architectures in terms of their ability to accurately detect diseases in rice plants. These two models consistently achieve remarkable accuracy in identifying these diseases. The research findings not only emphasize the potential of deep learning techniques in addressing the critical issue of rice crop diseases but also highlights the significant role that ResNet and MobileNet play in strengthening crop protection efforts and contributing to global food security.

 In this research, the primary objective is to tackle the pressing issue of identifying and effectively managing diseases in rice plants, a problem that can results in substantial crop losses and pose a severe threat to food security. The study employs Convolutional Neural Networks CNNs, a type of deep learning model widely used for image analysis, to conduct an extensive investigation using a sizable dataset comprising 5,932 RGB images. These images represent four distinct disease classes in rice plants: Bacterial Leaf Blight BLB, Blast, Brownspot, and Tungro. To conduct this research, the dataset is split into two subsets: a training set, which comprises 80 of the data, and a testing set, which makes up the remaining 20. This division allows for a systematic evaluation of the performance of four different CNN architectures: VGGNet, ResNet, MobileNet, and a simpler CNN model. The results of this study consistently show that ResNet and MobileNet outperform the other CNN architectures in terms of their ability to accurately detect diseases in rice plants. These two models consistently achieve remarkable accuracy in identifying these diseases. The research findings not only emphasize the potential of deep learning techniques in addressing the critical issue of rice crop diseases but also highlights the significant role that ResNet and MobileNet play in strengthening crop protection efforts and contributing to global food security.

 In this research, the primary objective is to tackle the pressing issue of identifying and effectively managing diseases in rice plants, a problem that can results in substantial crop losses and pose a severe threat to food security. The study employs Convolutional Neural Networks CNNs, a type of deep learning model widely used for image analysis, to conduct an extensive investigation using a sizable dataset comprising 5,932 RGB images. These images represent four distinct disease classes in rice plants: Bacterial Leaf Blight BLB, Blast, Brownspot, and Tungro. To conduct this research, the dataset is split into two subsets: a training set, which comprises 80 of the data, and a testing set, which makes up the remaining 20. This division allows for a systematic evaluation of the performance of four different CNN architectures: VGGNet, ResNet, MobileNet, and a simpler CNN model. The results of this study consistently show that ResNet and MobileNet outperform the other CNN architectures in terms of their ability to accurately detect diseases in rice plants. These two models consistently achieve remarkable accuracy in identifying these diseases. The research findings not only emphasize the potential of deep learning techniques in addressing the critical issue of rice crop diseases but also highlights the significant role that ResNet and MobileNet play in strengthening crop protection efforts and contributing to global food security.

Makale detayları

Innovative Approaches to Rice (Oryza sativa) Crop Health: A Comprehensive Analysis of Deep Transfer Learning for Early Disease Detection

Yazarlar:

Utpal Barman | Dulumani Das | Gunikhan Sonowal | Mala Dutta

Yayıncı:

Van Yuzuncu Yıl University

Ana Başlıklar:

Phytopathology | Plant Protection (Other)

Dergi:

Yuzuncu Yıl University Journal of Agricultural Sciences Vol.34 Issue 2, pp.314-322

Belge Tipi:

Makale

Yayın Tarihi:

6/2024

Belge Dili:

İngilizce

Document ID (Org No.):

2001319663

pISSN:

1308-7576

Transfer leaning

CNN

Rice Disease

Artificial Intelligence

Sustainable agriculture

Sayı

Makale detayları

Innovative Approaches to Rice (Oryza sativa) Crop Health: A Comprehensive Analysis of Deep Transfer Learning for Early Disease Detection

Yazarlar: Utpal Barman | Dulumani Das | Gunikhan Sonowal | Mala Dutta

Yayıncı: Van Yuzuncu Yıl University

Ana Başlıklar: Phytopathology | Plant Protection (Other)

Dergi: Yuzuncu Yıl University Journal of Agricultural Sciences Vol.34 Issue 2, pp.314-322

Belge Tipi: Makale

Yayın Tarihi: 6/2024

Belge Dili: İngilizce

Document ID (Org No.): 2001319663

pISSN: 1308-7576

Transfer leaning

CNN

Rice Disease

Artificial Intelligence

Sustainable agriculture

Sayı

Yazarlar:

Utpal Barman | Dulumani Das | Gunikhan Sonowal | Mala Dutta

Yayıncı:

Van Yuzuncu Yıl University

Ana Başlıklar:

Phytopathology | Plant Protection (Other)

Dergi:

Yuzuncu Yıl University Journal of Agricultural Sciences Vol.34 Issue 2, pp.314-322

Belge Tipi:

Makale

Yayın Tarihi:

6/2024

Belge Dili:

İngilizce

Document ID (Org No.):

2001319663

pISSN:

1308-7576