Smartphone-based hierarchical crowdsourcing for weed identification

Weed infestation is a common problem in agriculture that adversely affects crop production. Given severe constraints on the budget of many land-grant universities due to the economic downturn, extension services or agencies responsible for educating farmers and assisting them with the application of advancements in agricultural research, have taken a hit. To adapt to the current economic climate without adversely affecting the quality of programs for weed management, we present a hierarchical system that uses images captured using a smartphone application, a backend image processing algorithm, and two levels of crowdsourcing to identify weed images. The first of the two crowdsourcing levels consist of a non-expert crowd contributed by Amazon Mechanical Turk (AMT) and the second level consists of a crowd composed of experts such as county extension agents. We present a probabilistic decision engine to determine the suitability of two levels of crowdsourcing for identifying the weed image. We have evaluated the designed system using test weed images and we show that 80% of the weeds in our test set can be identified using the low cost AMT crowd while incurring a maximum latency of 3 h. Our system can help reduce the loses caused by the delay in identifying weeds, and hence, lead to quick remedial control practices applied to contain weed infestations.