Detection of Fungal Disease in Cabbage Images Using Adaptive Thresholding Technique Compared with Threshold Technique

Premkumar, S.

doi:10.47059/revistageintec.v11i4.2172

Search citation statements

Order By: Relevance

Paper Sections

Select...

Citation Types

Supporting

Mentioning

Contrasting

Year Published

2024

Publication Types

Select...

Other1

Article1

Relationship

Self Cite0

Independent2

Authors

Journals

Cited by 2 publications

References 36 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

Analysis and comparison of region-based segmentation technique for improving the accuracy of blood smear images over threshold technique

Bhargavi,

Devi,

Shibi

2024

AIP Conference Proceedings

View full text Add to dashboard Cite

Analysis and comparison of region-based segmentation technique for improving the accuracy of blood smear images over threshold technique

Bhargavi,

Devi,

Shibi

2024

AIP Conference Proceedings

View full text Add to dashboard Cite

Leveraging Convolutional Neural Networks for Disease Detection in Vegetables: A Comprehensive Review

Mahmood ur Rehman,

Liu,

Nijabat

et al. 2024

Agronomy

View full text Add to dashboard Cite

Timely and accurate detection of diseases in vegetables is crucial for effective management and mitigation strategies before they take a harmful turn. In recent years, convolutional neural networks (CNNs) have emerged as powerful tools for automated disease detection in crops due to their ability to learn intricate patterns from large-scale image datasets and make predictions of samples that are given. The use of CNN algorithms for disease detection in important vegetable crops like potatoes, tomatoes, peppers, cucumbers, bitter gourd, carrot, cabbage, and cauliflower is critically examined in this review paper. This review examines the most recent state-of-the-art techniques, datasets, and difficulties related to these crops’ CNN-based disease detection systems. Firstly, we present a summary of CNN architecture and its applicability to classify tasks based on images. Subsequently, we explore CNN applications in the identification of diseases in vegetable crops, emphasizing relevant research, datasets, and performance measures. Also, the benefits and drawbacks of CNN-based methods, covering problems with computational complexity, model generalization, and dataset size, are discussed. This review concludes by highlighting the revolutionary potential of CNN algorithms in transforming crop disease diagnosis and management strategies. Finally, this study provides insights into the current limitations regarding the usage of computer algorithms in the field of vegetable disease detection.

show abstract

Detection of Fungal Disease in Cabbage Images Using Adaptive Thresholding Technique Compared with Threshold Technique

Cited by 2 publications

References 36 publications

Analysis and comparison of region-based segmentation technique for improving the accuracy of blood smear images over threshold technique

Analysis and comparison of region-based segmentation technique for improving the accuracy of blood smear images over threshold technique

Leveraging Convolutional Neural Networks for Disease Detection in Vegetables: A Comprehensive Review

Contact Info

Product

Resources

About