Analysis of Pre-Trained CNN Models for Pepper and Potato Leaf Disease Prediction

UmaRani, V.; Thirisaa, S.

doi:10.1109/esci59607.2024.10497250

Cited by 2 publications

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

A Comprehensive Review of Convolutional Neural Networks based Disease Detection Strategies in Potato Agriculture

Gülmez

2024

Potato Res.

View full text Add to dashboard Cite

This review paper investigates the utilization of Convolutional Neural Networks (CNNs) for disease detection in potato agriculture, highlighting their pivotal role in efficiently analyzing large-scale agricultural datasets. The datasets used, preprocessing methodologies applied, specific data collection zones, and the efficacy of prominent algorithms like ResNet, VGG, and MobileNet variants for disease classification are scrutinized. Additionally, various hyperparameter optimization techniques such as grid search, random search, genetic algorithms, and Bayesian optimization are examined, and their impact on model performance is assessed. Challenges including dataset scarcity, variability in disease symptoms, and the generalization of models across diverse environmental conditions are addressed in the discussion section. Opportunities for advancing CNN-based disease detection, including the integration of multi-spectral imaging and remote sensing data, and the implementation of federated learning for collaborative model training, are explored. Future directions propose research into robust transfer learning techniques and the deployment of CNNs in real-time monitoring systems for proactive disease management in potato agriculture. Current knowledge is consolidated, research gaps are identified, and avenues for future research in CNN-based disease detection strategies to sustain potato farming effectively are proposed by this review. This study paves the way for future advancements in AI-driven disease detection, potentially revolutionizing agricultural practices and enhancing food security. Also, it aims to guide future research and development efforts in CNN-based disease detection for potato agriculture, potentially leading to improved crop management practices, increased yields, and enhanced food security.

show abstract

A Comprehensive Review of Convolutional Neural Networks based Disease Detection Strategies in Potato Agriculture

Gülmez

2024

Potato Res.

View full text Add to dashboard Cite

show abstract

Automating Potato Leaf Disease Detection with Lightning-Fast CNNs: Precision using PyTorch Lightning

Kaur,

Gupta

2024

2024 5th International Conference on Smart Electronics and Communication (ICOSEC)

View full text Add to dashboard Cite

Analysis of Pre-Trained CNN Models for Pepper and Potato Leaf Disease Prediction

Cited by 2 publications

References 10 publications

A Comprehensive Review of Convolutional Neural Networks based Disease Detection Strategies in Potato Agriculture

A Comprehensive Review of Convolutional Neural Networks based Disease Detection Strategies in Potato Agriculture

Automating Potato Leaf Disease Detection with Lightning-Fast CNNs: Precision using PyTorch Lightning

Contact Info

Product

Resources

About