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Developing custom computer vision models with Njobvu-AI: A collaborative, user-friendly platform for ecological

Cara L Appel1,2,3, Ashwin Subramanian4, Jonathan S Koning4

  • 1Department of Fisheries, Wildlife, and Conservation Sciences, Oregon State University, Corvallis, Oregon, USA.

Ecological Applications : a Publication of the Ecological Society of America
|September 12, 2025
PubMed
Summary
This summary is machine-generated.

Njobvu-AI is a no-code tool that enables researchers to train and deploy custom computer vision models for ecological data analysis. This platform accelerates conservation research by simplifying image labeling, model training, and classification from camera trap data.

Keywords:
AfricaMalawiartificial intelligencecamera trapsmachine learningsoftwarewildlife monitoring

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Area of Science:

  • Ecology
  • Computer Vision
  • Conservation Technology

Background:

  • Computer vision models offer significant potential for accelerating ecological data processing from sources like camera traps.
  • User-friendly, collaborative, and accessible workflows are crucial for wider adoption of these models in conservation and research.

Purpose of the Study:

  • To introduce Njobvu-AI, a no-code, multiuser platform for image labeling, model training, and classification.
  • To demonstrate the efficacy of Njobvu-AI by training and deploying a YOLO multiclass detector for camera trap data.

Main Methods:

  • Developed Njobvu-AI, a no-code tool supporting collaborative image labeling, model training, and review.
  • Trained a YOLO multiclass detector using 33,664 camera trap images from Malawi, covering 37 animal species.
  • Applied the trained model to an independent dataset to evaluate performance in image filtering, species classification, richness estimation, and animal counts.

Main Results:

  • The model effectively filtered over 3 million empty images, showing comparable sensitivity but lower specificity than MegaDetector.
  • High classification performance (average precision, recall, F1 > 0.70) was achieved for species with over 1000 training images; overall performance was moderate (macro-averaged F1 = 0.63).
  • Site-level species richness estimations were highly concordant with manual review (R² = 0.91 at a 0.95 score threshold), and per-image animal counts were accurate, though underestimated for large groups.

Conclusions:

  • Njobvu-AI provides a viable all-in-one, no-code solution for researchers to implement custom computer vision models, even with modest ecological datasets.
  • The platform facilitates a faster transition from data collection to analysis, enhancing the efficiency of conservation and research programs.
  • The demonstrated model performance indicates the potential of Njobvu-AI for various ecological analyses, including species identification and population estimation.