In the domain of empirical analysis the quality of data defines the quality of work. Scientists may have theorised many important questions long before they were answers, but it is only when sensors convert atoms into bits, or pixels. As Richard Feynman once said: "Everything is made of atoms." In many lines of scientific and empirical inquiry, everything is being replicated in the digital realm with impressive resolution. Among the most remarkable applications is the ability to spot diffuse methane gas leaks from space. Hard to imagine a more stunning scientific achievement.
The human vision system is an impressive bit of engineering. Well-trained agronomists can spot pests in the field. Viticulturists spot leaf and stem disease. Physicians are masters of screening for subtle changes in a patient. In some cases, direct observation does the trick. However, what if we want to sort coffee or cocoa beans not by supplier or country of origin but by the flavour profile of individual beans? What if we want to map brain cancer during surgery in real-time? What if we want to predict the freshness level or sugar content of fruit and vegetables, at the distribution centre or at the retail location? Visual inspection and the smartphone can take us only so far.
If the smartphone is, as Elon Musk says, turning humans into "cyborgs" then what of hyperspectral imaging? Hyperspectral cameras combined with advanced artificial intelligence can rapidly and non-invasively scan individual units across a range of parameters (e.g. sugar, cancer, protein) for zero marginal cost. For organisations, operators, scientists, engineers and clinicians who are paid to know their objects of analysis, this is a powerful tool to transform hypotheses and risk into high confidence predictions so that you can unlock maximum value.