Advancements in on-board technologies and AI integration hold great potential to further enhance the existing drone-based crocodilian monitoring method. Improvements in drone hardware, such as hybrid models with extended flight times and enhanced camera resolutions, allow for broader habitat coverage and the capture of more detailed imagery in complex environments. Integrating artificial intelligence (AI) represents a significant opportunity to streamline image analysis by automating crocodile detection and size estimation using allometric models. These AI-driven enhancements could provide near real-time data processing, reducing reliance on time consuming manual analysis.

This improvements are currently under development. We conducted an experimental study in Cameroon in April 2025 with students and young researchers from the University of Ngaoundéré and local NGOs, using drones equipped with thermal cameras and searchlights, and including AI-assisted automated data processing.

This building block on capacity building on local stakeholders including Indigenous including Peoples and Local Communities (IPLCs) to operate drones, enabling them to take active roles in conservation. 

Ease of Use of the method devellopted:

1. Minimal Technical Skills Required:
   Users only need basic training in drone operation and measurement extraction from high-resolution images. The process is straightforward:
   • Fly the drone following the standardized flight protocol.
   • Marke crocodilians on overhead images.
   • Measure the visible head length using accessible image analysis tools (e.g., ImageJ, QGIS).
   • Apply the corresponding allometric equation or lookup from pre-prepared tables (abaques) for total length estimation.
2. Readily Adaptable:
   The framework uses easy-to-read tables (abaques), making it accessible to both specilialists and non-specialists for operators can quickly apply the method without requiring advanced scientific expertise.
3. Accessible Equipment:
   The approach relies on consumer-grade drones and widely available software, ensuring affordability and reducing barriers to adoption.

Why It’s Effective:

The framework’s simplicity, scalability, and reliability make it ideal for diverse contexts, from remote wetlands to urban-adjacent habitats. It empowers a broad range of users to generate scientifically robust data.

The allometric framework is a non-invasive tool designed to estimate the total body length of crocodilians based on the measurement of their head length, captured through high-resolution drone imagery. By leveraging established species-specific head-to-body length ratios, this method eliminates the need for physical capture or handling, reducing risks for both researchers and wildlife. Validated for 17 of the 27 crocodilian species, the framework allow to provides reliable demographic data essential for population monitoring and conservation management.

The framework uses easy-to-read tables (abaques), making it accessible to non-specialists, operators can quickly apply the method without requiring advanced scientific expertise.

Understanding the demographic structure is vital for wildlife research and conservation. For crocodylians, accurately estimating total length and demographic class usually necessitates close observation or capture, often of partially immersed individuals, leading to potential imprecision and risk. Drone technology offers a bias-free, safer alternative for classification. This study evaluated the effectiveness of drone photos combined with head length allometric relationships to estimate total length, and propose a standardized method for drone-based crocodylian demographic classification. 

This building block establishes standardized flight parameters for effective crocodilian monitoring