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.

Phenotype to Genotype Gap

This report explores the genetic and phenotypic components of ideotypes, emphasizing their role in bridging the phenotype-genotype gap. Through the lens of quantitative genetics and the integrative approach of Crop Systems Biology (CSB), the report examines the complex interaction between genetic information, environmental factors, and crop physiology, offering insights into improving crop breeding strategies.