Species Identification is an open-source repository that's part of Syngenta's Biodiversity Sensor Project. This repo shows the hardware used to collect imagens to be analysed by the model avaliable here: https://github.com/syngenta/BiodiversitySensorProject_SpeciesIdentificationCode/tree/main
This hardware was developed by IIT Ropar, as a prototype R&D biodiversity monitoring technology to gather insect species training data for building robust AI models. The device is autonomous, but hardy: weatherproof, solar-powered and cost-efficient. A camera is connected to capture the images of the insects, with data being transferred real-time to the cloud via 4G connectivity. The process of transferring data from device to cloud is from IOT core to S3 Bucket. The data of the camera are further stored in the SD card as well. A power section is provided to control and manage the power of the sensor.
We invite you to explore our open-sourced motion detection code (https://github.com/syngenta/DigitalEntomologist_MotionDetectionCode) and the machine-learning process described here, from training to species detection and identification, and freely develop this further to bring science, technology, data together to improve our collective responsibility - biodiversity.
Please, check our Contribution guide for more details (https://github.com/syngenta/DigitalEntomologist_MotionDetectionCode/blob/main/CONTRIBUTING.md).
This project adheres to the Code of Conduct (https://github.com/syngenta/BiodiversitySensorProject_SpeciesIdentificationCode/blob/main/CODE_OF_CONDUCT.md). We pledge to act and interact in ways that contribute to an open, welcoming, diverse, inclusive, and healthy community.
The project uses the MIT License. See LICENSE (https://github.com/syngenta/BiodiversitySensorProject_SpeciesIdentificationCode/blob/main/LICENSE) for details.