Rice Leaf Diseases Detector Based on AlexNet

Zakzouk, Salma; Ehab, Mohamed; Atef, Silvana; Yousri, Retaj; Tawfik, Rania M.; Darweesh, M. Saeed

doi:10.1109/jac-ecc54461.2021.9691435

Cited by 4 publications

(1 citation statement)

References 14 publications

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“…In terms of attention mechanism, Zhu ( Zhu et al., 2021 ) added an attentional mechanism module combining Convolutional Block Attention Module (CBAM) and ECA-Net module to the model, which improved the accuracy of the model by 3.4%. Zakzouk, S. ( Zakzouk et al., 2021 ) used AlexNet to classify new rice diseases with an accuracy of 99.71%. The accuracy of the results indicated the feasibility of the automatic rice disease classification system.…”

Section: Introductionmentioning

confidence: 99%

Research on cassava disease classification using the multi-scale fusion model based on EfficientNet and attention mechanism

2022

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Cassava disease is one of the leading causes to the serious decline of cassava yield. Because it is difficult to identify the characteristics of cassava disease, if not professional cassava growers, it will be prone to misjudgment. In order to strengthen the judgment of cassava diseases, the identification characteristics of cassava diseases such as different color of cassava leaf disease spots, abnormal leaf shape and disease spot area were studied. In this paper, deep convolutional neural network was used to classify cassava leaf diseases, and image classification technology was used to recognize and classify cassava leaf diseases. A lightweight module Multi-scale fusion model (MSFM) based on attention mechanism was proposed to extract disease features of cassava leaves to enhance the classification of disease features. The resulting feature map contained key disease identification information. The study used 22,000 cassava disease leaf images as a data set, including four different cassava leaf disease categories and healthy cassava leaves. The experimental results show that the cassava leaf disease classification model based on multi-scale fusion Convolutional Neural Network (CNN) improves EfficientNet compared with the original model, with the average recognition rate increased by nearly 4% and the average recognition rate up to 88.1%. It provides theoretical support and practical tools for the recognition and early diagnosis of plant disease leaves.

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Section: Introductionmentioning

confidence: 99%

Research on cassava disease classification using the multi-scale fusion model based on EfficientNet and attention mechanism

2022

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A Comprehensive Review of Advanced Deep Learning Approaches for Food Freshness Detection

Singh,

Nickhil,

R.Nisha

et al. 2024

Food Eng Rev

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A Systematic Literature Review on Leaf Disease Recognition Using Computer Vision and Deep Learning Approach

Yani,

Fauzi,

Zaki

et al. 2024

J. Inf. Syst. Eng. Bus. Intell.

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Background: Plant diseases affect agricultural output, quality and profitability, making them serious obstacles for agriculture. It is essential to detect diseases early in order to reduce losses while retaining sustainable practices. Plant disease detection has benefited greatly from the use of computer vision and deep learning in recent years because of their outstanding precision and computing capability. Objective: In this paper, we intend to investigate the role of deep learning in computer vision for plant disease detection while looking into how these techniques address complex disease identification problems. A variety of deep learning architectures were reviewed, and the contribution of frameworks such as Tensorflow, Keras, Caffe and PyTorch to the researchers' model construction was studied as well. Additionally, the usage of open repositories such as PlantVillage and Kaggle along with the customized datasets were discussed. Methods: We gathered the most recent developments in deep learning techniques for leaf disease detection through a systematic literature review of research papers published over the past decade, using reputable academic databases like Scopus and Web of Science, following the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) method for guidance. Results: This study finds that researchers consistently enhance existing deep learning architectures to improve prediction accuracy in plant disease detection, often by introducing novel architectures and employing transfer learning methods. Frameworks like TensorFlow, Keras, Caffe, and PyTorch are widely favored for their efficiency in development. Additionally, most studies opt for public datasets such as PlantVillage, Kaggle, and ImageNet, which offer an abundance of labelled data for training and testing deep learning models. Conclusion: While no singular ‘best' model emerges, the adaptability of deep learning and computer vision demonstrates the dynamic nature of plant disease recognition area, and this paper provides a comprehensive overview of deep learning's transformative impact on plant disease recognition by bringing together information from different studies. Keywords: Deep learning, Computer vision, Plant disease, Systematic literature review

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Rice Leaf Diseases Detector Based on AlexNet

Cited by 4 publications

References 14 publications

Research on cassava disease classification using the multi-scale fusion model based on EfficientNet and attention mechanism

Research on cassava disease classification using the multi-scale fusion model based on EfficientNet and attention mechanism

A Comprehensive Review of Advanced Deep Learning Approaches for Food Freshness Detection

A Systematic Literature Review on Leaf Disease Recognition Using Computer Vision and Deep Learning Approach

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