🚁 Research Focus
Research focuses on computer vision, multimodal AI, and precision agriculture. Works on UAV-based crop monitoring, disease detection, and spray decision support — with emphasis on robust and interpretable models for real field conditions. Also studies scene text understanding and cross-lingual image retrieval to connect visual content with language across diverse settings.
Research Details
Autonomous Precision Agriculture: Data-Driven Pest Detection, Disease Identification and Prediction, and Growth Analysis Using Computer Vision and Deep Learning
Published Paper
UAV-based Crop Monitoring
Aerial drone imaging for real-time soybean and crop disease detection, pest identification, and field health mapping.
Spray Decision Support
AI-driven systems to optimise pesticide spray decisions based on UAV imagery and disease severity maps.
Cross-lingual Visual AI
Scene text understanding and cross-lingual image retrieval to connect visual content with diverse language settings.
Interpretable Models
Building robust and interpretable deep learning models suitable for real agricultural field conditions.
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