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Deep learning-based detection of seedling development
Salma Samiei1, Pejman Rasti1,2, Joseph Ly Vu3
1Laboratoire Angevin de Recherche en Ingénierie des Système (LARIS),UMR INRAe IRHS, Université d'Angers, Angers, France.
Plant Methods
|August 4, 2020
Summary
This study developed a computer vision pipeline to track early seedling development, accurately identifying key growth stages like emergence and leaf appearance for red clover and alfalfa. The method achieves over 90% accuracy, offering a scalable solution for plant science research.
Area of Science:
- Plant Science
- Computational Biology
- Agricultural Technology
Background:
- High-throughput phenotyping using computer vision is crucial for monitoring seedling emergence and early development.
- Current research often overlooks seed-to-seed kinetics of early developmental events, focusing instead on metrics like leaf area index or single event detection.
Purpose of the Study:
- To develop and validate a comprehensive image processing and machine learning pipeline for classifying early seedling growth stages.
- To accurately identify and track developmental events on a seed-to-seed basis using computer vision.
Main Methods:
- Utilized top-view imaging systems to capture the entire seedling growth process.
- Precisely annotated key developmental stages: emergence, cotyledon opening, and first leaf appearance.
- Trained deep neural networks, exploring strategies to integrate prior knowledge of developmental stage order, with a deep neural network and long short-term memory cell combination yielding the best performance.
Main Results:
- Achieved over 90% accuracy in correctly detecting and classifying three distinct early growth stages (emergence, cotyledon opening, first leaf appearance) plus soil.
- Demonstrated the pipeline's effectiveness on red clover and alfalfa accessions.
Conclusions:
- The developed pipeline offers a robust solution for automated monitoring of early plant development.
- The methodology is adaptable for various crops and can be extended to analyze additional developmental stages.
