EcoTransLearn

EcoTransLearn implements transfer learning for ecological image classification, adapting ImageNet-pretrained deep learning models to domain-specific tasks such as plankton identification.

Key Features:

• Transfer Learning Integration: Leverages transfer learning to adapt knowledge from large, diverse image datasets such as ImageNet to ecological domains with limited labeled data.
• Image Source Support: Supports classification of images acquired from FlowCam, ZooScan, and standard photographic sources.
• R-package Implementation: Implemented as an R package that orchestrates analysis and data handling within R.
• Interoperability with Python and TensorFlow: Utilizes Python scripts for image classification invoked via the reticulate package to run TensorFlow-based deep learning operations.
• Automation of Classification Processes: Automates the image classification workflow by integrating R scripts with Python/TensorFlow execution for batch processing of image datasets.

Scientific Applications:

• Plankton classification: Adapts ImageNet-pretrained models to classify plankton images from imaging systems such as FlowCam and ZooScan.
• Ecological image classification with limited labels: Improves classification performance in ecological studies where labeled datasets are scarce by transferring features from large-scale datasets.
• High-throughput imaging analysis: Applies deep learning classification to high-volume image streams generated by instruments like FlowCam and ZooScan.

Methodology:

Pre-training on large-scale image datasets (e.g., ImageNet); applying transfer learning by fine-tuning pre-trained deep learning models on smaller, domain-specific ecological datasets; automating classification via R scripts that interface with Python-based TensorFlow operations using reticulate.