Semantic Segmentation for Detecting Phragmites australis in Remote Sensing Imagery
A scalable AI pipeline that uses UAV imagery and semantic segmentation to accurately detect and monitor invasive Phragmites australis in wetland ecosystems.
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Technical Reports
Semantic Segmentation for Detecting Phragmites australis in Remote Sensing Imagery 
Evolution of Segmentation Models in Our Computer Vision Pipeline Evolution of Segmentation Models in Our Computer Vision Pipeline
This report outlines the progression of segmentation models used in a computer vision pipeline, moving from SegDecNet to RTMDet and eventually SegFormer. Each transition reflects growing project demands for better accuracy, faster inference, and improved handling of complex visual scenes, highlighting how modern architectures combine efficiency, scalability, and contextual understanding.
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Evolution of the Object Detection Pipeline Evolution of the Object Detection Pipeline
Migration from Detectron2/YOLO to RFDetR for Enhancing Precision, Latency and Architectural Efficiency
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Waterhemp Detection in Corn Field by using a Semantic Segmentation model Waterhemp Detection in Corn Field by using a Semantic Segmentation model
This article explains how a semantic segmentation model was used to detect waterhemp in corn fields using drone imagery. The approach identifies three categories—waterhemp, healthy corn, and stressed corn—providing farmers with detailed insights for better crop management. It covers data collection, image analysis, annotation, model results, and future improvements such as using multispectral imagery.
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14 Free Datasets for Agricultural Crop Detection 14 Free Datasets for Agricultural Crop Detection
This article presents a curated collection of 14 free, drone-based agricultural datasets useful for training machine learning models in crop detection and segmentation. It highlights dataset sizes, classes, licenses, preprocessing steps, and augmentation methods for crops such as corn, potato, soybean, tobacco, cotton, rice, cabbage, radish, and sugar beet. The list is designed to help researchers and developers working on agricultural AI applications find suitable training data.
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