Early prediction of helminth infection in small ruminants with accelerometers and machine learning

Published: 5 March 2026| Version 1 | DOI: 10.17632/wgnfhgm7pm.1
Contributors:
Axel Montout,
,
,

Description

This upload (PredictionOfDHealthInSR-master.zip) contains a Python-based research pipeline for early prediction of helminth infection / health status in small ruminants (goats and sheep) using accelerometer time‑series data and machine‑learning models. The code supports preprocessing of accelerometry signals, construction of training/validation datasets (including optional frequency‑domain representations such as wavelet-based features), and training/evaluation of classifiers to distinguish healthy vs. unhealthy animals. It also includes utilities for working with associated clinical/label data (e.g., FAMACHA scoring) and optional environmental covariates (weather variables). Where to find source code in the ZIP: - Top-level scripts: main.py (run to reproduce the full pipeline) and ml.py (command-line entry point for running the ML workflow with configurable options). - Core modules: pipeline/ (data preparation and training-set generation), preprocessing/ (signal preprocessing), model/ (model training/loading code), cwt/ (wavelet/CWT routines), utils/ (helper scripts), and dataset/ (scripts used to build/assemble datasets). Where to find data in the ZIP: - datasets/ contains the dataset files used by the pipeline (e.g., accelerometry-derived CSV/JSON datasets and label/metadata JSON files, including FAMACHA-related files under datasets/famacha/). - weather_data/ contains weather covariate CSV files (and weather_data/src/ contains code used to work with those files).

Files

Steps to reproduce

1) Download and unzip PredictionOfDHealthInSR-master.zip. 2) Open a terminal and go into the unzipped folder: cd PredictionOfDHealthInSR-master 3) Create and activate a Python virtual environment: python3 -m venv venv source venv/bin/activate 4) Install dependencies: make environment (If Make is not available, install from requirements.txt instead: pip install -r requirements.txt) 5) Reproduce the pipeline run (as described by the project): python main.py 6) (Optional) Run the ML workflow via the CLI: python ml.py --help Then run with your desired arguments, pointing --dataset-folder to a dataset under: datasets/

Institutions

Categories

Veterinary Medicine, Signal Processing, Parasitology, Machine Learning, Accelerometer, Livestock Species, Wearable Sensor, Animal Health Monitoring System

Funders

  • Red Meat Research and Development South Africa

Licence