From one question to a finished thesis and research
From thesis writing to journal and conference papers, we help you finish work you can explain with confidence. Pick a topic to explore.
Insect Pest Classification with ViT, DeiT & Ensemble Fusion
An agriculture AI study that classifies six crop-damaging insect pests from field images, fine-tuning Vision Transformer and DeiT with early/late fusion, majority voting, and ViT-to-EfficientNet-B4 knowledge distillation.
Plant Disease Detection with Optimized YOLOv8, GhostNet & Coordinate Attention
A comparison study on real-field plant disease detection, enhancing YOLOv8n/s with GhostNet backbone compression and Coordinate Attention across multiple plant species and disease classes.
Rice Leaf Disease Classification with AlexNet Snapshot Ensemble & Hybrid ViT
A comparative study of deep learning architectures on rice leaf imagery, modified AlexNet with snapshot ensembling, Hybrid ViT, EfficientNet-B4, VGG16, and Inception V3 across ten disease and healthy classes.
Five-Class Rice Leaf Disease Classification with EfficientNet-B4 & AlexNet
A comparative study of Basic CNN, Modified AlexNet, EfficientNet-B0, and EfficientNet-B4 on a balanced five-class rice leaf dataset, Leaf Scald, Healthy, Brown Spot, Leaf Blast, and Rice Hispa, for automated paddy disease diagnosis.
Multi-Crop Plant Disease Classification with EfficientNet-B4 & AlexNet
A comparative study of Basic CNN, Modified AlexNet, EfficientNet-B0, and EfficientNet-B4 on a balanced multi-crop leaf dataset, apple, grape, corn, cherry, and blueberry across 15 disease and healthy classes.
Bangladeshi Vegetable Classification with VGG16, Random Forest & XGBoost
A comparative study of hand-crafted ML (SVM, Random Forest, XGBoost, Logistic Regression) against transfer-learning CNNs (VGG16, VGG19, ResNet50, DenseNet-121, MobileNet) on a balanced 15-class vegetable dataset for supermarket and farm automation.
Talk to us
Tell us what you need. We’ll reply on WhatsApp with a clear next step.