Plant Leaf Disease Detection Using an Optimized Evolutionary Gravitational Neocognitron Neural Network

Goyal, Praveen; Verma, Dinesh Kumar; Kumar, Shishir

doi:10.1007/s40009-023-01370-4

Natl. Acad. Sci. Lett.

2024

DOI: 10.1007/s40009-023-01370-4

|View full text |Cite

Plant Leaf Disease Detection Using an Optimized Evolutionary Gravitational Neocognitron Neural Network

Praveen Goyal,

Dinesh Kumar Verma,

Shishir Kumar

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...

Citation Types

Supporting

Mentioning

Contrasting

Year Published

2024

Publication Types

Select...

Article2

Relationship

Self Cite0

Independent2

Authors

Journals

Cited by 2 publications

References 20 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

Optimal Prognostic Accuracy: Machine Learning Approaches for COVID-19 Prognosis with Biomarkers and Demographic Information

Hussain,

Songhua,

Aslam

et al. 2024

New Gener. Comput.

View full text Add to dashboard Cite

Optimal Prognostic Accuracy: Machine Learning Approaches for COVID-19 Prognosis with Biomarkers and Demographic Information

Hussain,

Songhua,

Aslam

et al. 2024

New Gener. Comput.

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

scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.

Contact Info

customersupport@researchsolutions.com

10624 S. Eastern Ave., Ste. A-614

Henderson, NV 89052, USA

This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.

Blog Terms and Conditions API Terms Privacy Policy Contact Cookie Preferences Do Not Sell or Share My Personal Information

Made with 💙 for researchers

Part of the Research Solutions Family.

Plant Leaf Disease Detection Using an Optimized Evolutionary Gravitational Neocognitron Neural Network

Cited by 2 publications

References 20 publications

Optimal Prognostic Accuracy: Machine Learning Approaches for COVID-19 Prognosis with Biomarkers and Demographic Information

Optimal Prognostic Accuracy: Machine Learning Approaches for COVID-19 Prognosis with Biomarkers and Demographic Information

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

Contact Info

Product

Resources

About