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Plant Management


by earlier agritech successes, IoT is seen as a long-overdue solution to driving further efficiencies into the production of our food.


Using low-cost IoT sensors, crop fields and greenhouses are having sensors installed to collect data on things such as soil moisture and temperature, nutrient levels, atmospheric conditions and solar irradiance. By applying machine learning techniques, patterns and trends can be identified to calculate yields, optimise fertiliser and irrigation needs and warn of potential infestation risks. Early work in this area is already looking promising although even from Seneseye’s own trials, the agriculture sensor market needs to mature further before the full potential can be realised. That’s not an issue in the second hot area: manufacturing.


Manufacturing The potential of IoT in manufacturing (or any business that operates machines) is clear. Indeed, big businesses have been


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instrumenting their expensive machines for years so that impending failures can be identified before they occur – various terms such as condition monitoring, asset management and predictive maintenance have been used. Such systems have been the key ingredient to running a pre-emptive maintenance campaign and decreasing unplanned downtime.


The problem with these earlier systems, though, was the high cost, long deployment phases and inherent complexity that required an engineering team to interpret the data and decide on suitable actions. This has meant that smaller business in particular – or those in the ‘wrong’ sector – have been simply locked out. Moreover, those systems tend to be quite niche and offer limited scope in terms of being part of a more holistic approach involving inventory management, spares and logistics control, training, asset management and continuous improvement techniques.


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