The advent of ‘big data technologies’ such as hyperspectral imaging means that Precision Agriculture (PA) developers now have access to superabundant and highly  heterogeneous data.  The authors explore the limitations of the classic science model in this situation and propose a new non-linear process that is not based on the premise of controlled data scarcity. The study followed a science team tasked with developing highly advanced hyperspectral techniques for a ‘low tech’ sector in which non-adoption by farmers is a significant risk. Hyperspectral imaging creates multi-layered, geo-referenced data early in the science process in superabundance.  This data is created at high speed in near real-time and does not require expensive ground sampling.  The data is extremely versatile and has the potential for many different measurements from one record. These data traits increase the likelihood of producing ‘surplus science’, that is, science that exceeds what was judged necessary to solve the problem as defined at project launch. The production of superabundant and highly versatile data early in the science process increases the possibility of discovering new forms of valuable knowledge (methods and solutions) during the course of an investigation. However, realizing the value of these opportunities requires a departure from the classic science model. Under data-scarcity conditions, such surplus science would be classified as undesirable ‘project creep’. In response we propose an alternative process based on a non-linear, iterative approach that utilizes heterogeneous actors to refine value from hyperspectral data. The paper documents how a ‘big-data’ setting generates surplus science and unexpected value possibilities. We outline the challenges that science teams face if they are to realize these possibilities. These challenges include the linearity of project design and set up, which limits the ability to identify unexpected opportunities and re-organize in response. Moreover, the science team may not have either sufficient time or appropriate expertise to exploit an opportunity. In light of these findings, it is proposed that for innovation in the PA sector to make the necessary rapid advances both technically and in terms of adoption, changes are needed in the way research projects are funded and structured. In addition, we suggest changes to the make-up of science teams and the inclusion of a variety of end-user perspectives during the research and development process.