An Efficient Approach for Crops Pests Recognition and Classification Based on Novel DeepPestNet Deep Learning Model

Keyphrases

• Classification Basis 100%
• Deep Learning Model 100%
• Pest Recognition 100%
• Pest Categorisation 100%
• Crop Pest 100%
• Recognition-based 100%
• Insect Pests 50%
• Pest Detection 50%
• Pest Management 25%
• Aphids 25%
• Crop Yield Loss 25%
• Social Loss 25%
• Rotational Method 25%
• S. Exigua 25%
• Grasshopper 25%
• Economic Yield 25%
• Spodoptera Exigua 25%
• Agricultural Yield 25%
• Acrida Cinerea 25%
• Data-limited 25%
• Optimal Accuracy 25%
• Misclassification 25%
• Fully Connected Layer 25%
• Kaggle Dataset 25%
• Image Recognition 25%
• Mites 25%
• Automated System 25%
• Surrounding Environment 25%
• Bollworm 25%
• Dataset Augmentation 25%
• Image Augmentation 25%
• Crop Data 25%
• Armyworm 25%
• Stem Borer 25%
• Control Strategy 25%
• Economic Loss 25%
• Locusta Migratoria 25%
• Agriculture 25%
• Augmentation Techniques 25%
• Convolutional 25%
• Environmental Loss 25%
• Identification Approach 25%
• Pesticides 25%
• Popular 25%
• Parasa 25%
• Euproctis 25%
• Agricultural Professionals 25%
• Chrysochus 25%
• Image Rotation 25%
• Chinensis 25%
• Empoasca Flavescens 25%

Agricultural and Biological Sciences

• Insect Pests 100%
• Noctuidae 50%
• Grasshopper 50%
• Codling Moth 50%
• Mite 50%
• Empoasca 50%
• Locusta migratoria 50%
• Parasa 50%
• Spodoptera exigua 50%
• Euproctis 50%
• Sawflies 50%
• Pest Control 50%
• Bollworm 50%