Diagnosis of bacterial leaf blight, leaf smut, and brown spot in rice leafs using VGG16

Mannepalli, Praveen Kumar; Pathre, Ayonija; Chhabra, Gunjan; Ujjainkar, Priyanka Anup; Wanjari, Shrutika

doi:10.1016/j.procs.2024.04.022

Procedia Computer Science

2024

DOI: 10.1016/j.procs.2024.04.022

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Diagnosis of bacterial leaf blight, leaf smut, and brown spot in rice leafs using VGG16

Praveen Kumar Mannepalli,

Ayonija Pathre,

Gunjan Chhabra

et al.

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Deep Learning-Based Methods for Multi-Class Rice Disease Detection Using Plant Images

Li,

Chen,

Yin

et al. 2024

Agronomy

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Rapid and accurate diagnosis of rice diseases can prevent large-scale outbreaks and reduce pesticide overuse, thereby ensuring rice yield and quality. Existing research typically focuses on a limited number of rice diseases, which makes these studies less applicable to the diverse range of diseases currently affecting rice. Consequently, these studies fail to meet the detection needs of agricultural workers. Additionally, the lack of discussion regarding advanced detection algorithms in current research makes it difficult to determine the optimal application solution. To address these limitations, this study constructs a multi-class rice disease dataset comprising eleven rice diseases and one healthy leaf class. The resulting model is more widely applicable to a variety of diseases. Additionally, we evaluated advanced detection networks and found that DenseNet emerged as the best-performing model with an accuracy of 95.7%, precision of 95.3%, recall of 94.8%, F1 score of 95.0%, and a parameter count of only 6.97 M. Considering the current interest in transfer learning, this study introduced pre-trained weights from the large-scale, multi-class ImageNet dataset into the experiments. Among the tested models, RegNet achieved the best comprehensive performance, with an accuracy of 96.8%, precision of 96.2%, recall of 95.9%, F1 score of 96.0%, and a parameter count of only 3.91 M. Based on the transfer learning-based RegNet model, we developed a rice disease identification app that provides a simple and efficient diagnosis of rice diseases.

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Deep Learning-Based Methods for Multi-Class Rice Disease Detection Using Plant Images

Li,

Chen,

Yin

et al. 2024

Agronomy

View full text Add to dashboard Cite

show abstract

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Diagnosis of bacterial leaf blight, leaf smut, and brown spot in rice leafs using VGG16

Cited by 1 publication

References 24 publications

Deep Learning-Based Methods for Multi-Class Rice Disease Detection Using Plant Images

Deep Learning-Based Methods for Multi-Class Rice Disease Detection Using Plant Images

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