PhenoSelect: Training a Neural Network Because I Refuse to Click on 100,000 Leaves

An introduction to PhenoSelect, an open-source deep learning pipeline designed to automate leaf segmentation and trait classification in High-Throughput Plant Phenotyping (HTPP). Built on the YOLOv11 framework , this tool processes RGB-NIR and hyperspectral imagery to extract quantitative leaf-level data.

Expanding the Pipeline: Incorporating Canopy Height and Canopy Temperature

This blog post explores how to expand a drone-based pipeline for crop analysis. By incorporating height and thermal data, along with canopy coverage, a more comprehensive understanding of crop health and growth can be achieved.

From Drone Images to Insights: Using Neural Networks for Canopy Coverage Detection and Measurement

This blog explores the development of a complete workflow—from drone images to accurate canopy coverage—using accessible tools and AI-driven techniques.