Crowdsourcing accurate and low cost detection of weed infestations

UMBC's Mobile, Pervasive and Sensor Systems Laboratory focuses on three key areas: renewable energy, healthcare applications and mobile phone systems. Their crowdsourcing-based technology for accurate and low cost detection of weed infestations was cited recently as one of the top ten technologies changing farm machinery by Farm Industry News.

D. Saraswat and N. Banerjee, Crowdsourcing App for Precision Agriculture Decision Making, ASABE Annual International Meeting, Dallas TX, August 2012.

The research was begun while Professor Banerjee was at the University of Arkansas, where the complex software system was  implemented by students Brenna Blackwell and Mahbub Rahman, who is continuing his PhD studies at UMBC.

Like the MPSSL Facebook page to follow their work or visit the MPSSL page