CET teams up with Catholicate College to develop phycoscraper

April 25, 2022 08:57 pm | Updated 08:57 pm IST - THIRUVANANTHAPURAM

The prototype of the phycoscraper developed by the College of Engineering Thiruvananthapuram and Catholicate College

The prototype of the phycoscraper developed by the College of Engineering Thiruvananthapuram and Catholicate College

College of Engineering Thiruvananthapuram (CET) has signed a Memorandum of Understanding (MoU) with with Catholicate College, Pathanamthitta, to develop Phycoscraper, an app-controlled research instrument used to study terrestrial algae.

The agreement was recently signed by former CET Principal Jiji C.V. and Catholicate College Principal Philipose Omman.

A team from CET had initially developed a prototype for the instrument for the Phycotechnology Lab of the Catholicate College. The current MoU will expedite development of the phycoscraper for various research studies initiated by the laboratory and other research organisations.

The project is being led by Rajeev Rajan of CET, Binoy T. Thomas and Thomas V.P. of Catholicate College, and supported by students of both colleges.

Phycoscraper has a number of sensors to detect the growing conditions of the algae including temperature, light intensity, humidity and take photographs that can be controlled by the user using a mobile application developed for this device. The user’s phone can be connected to the system, via Bluetooth and WiFi. The application also enables the user to send commands to the system to read data from the sensors and also take photographs.

The device consists of an extendable rod that can collect samples from a height of up to 7 metres. The sample collected in the rod can be used for further analysis in the laboratory.

According to official sources, the phycoscraper can be used in the extensive study of other terrestrial organisms as well. The device can be further improved for classifying the collected samples with the help of pictures. The developers intend to incorporate the use of machine learning to enhance its features.

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