Research notebook
Practical field notes at the intersection of artificial intelligence, plant phenotyping, imaging, and scientific software.
Latest notes
Concise project records and technical guides, revised to separate verified results from experimental workflows.
A WeChat Mini Program for plant phenotyping fieldwork: leaf-angle and land-area measurement, site weather, image analysis, AI-assisted charting and data management in one mobile toolkit.
A web application and batch pipeline that removes plant-image backgrounds without task-specific training, using a multimodal image model with a structured prompt, aspect-ratio matching and file-signature format detection.
A desktop platform that processes hyperspectral, LiDAR, RGB and thermal phenotyping data with one interface, interactive parameter tuning, batch processing and structured exports.
A measuring and analysis framework that predicts leaf BRDF parameters from phenotypic traits in four species, combining directional spectroscopy, Cook–Torrance fitting, ensemble learning and canopy ray tracing (Plant Phenomics, 2025).
A practical path from a local Ollama assistant to retrieval and controlled tool use, with explicit privacy, security, versioning, and deployment boundaries.
A local OpenCV method that segments separated plant samples on a uniform background and measures size, shape and colour in calibrated units, with an overlay for every image.
A concise, publisher-neutral guide to journal selection, manuscript preparation, submission, peer review, publication status, research integrity, and post-publication maintenance.
Generating 3D cotton plants from single photographs with 3D generative AI and evaluating them organ by organ against multi-view SfM reconstructions of the same plants.
A UAV workflow for plot-level 3D crop phenotyping: Cross-Circular Oblique flight design, SfM reconstruction, canopy point-cloud processing with CanopyPC, and machine-learning models of crop traits.
