Sustainable pest management technologies to improve yield and support net zero in apple production

Spotta开发了全天候智能昆虫监测系统，并将其作为一项服务销售，在有害害虫造成损害之前向种植者发出警报，使他们能够在正确的时间、正确的地点和正确的技术进行干预。昆虫害虫是农业中的一个关键挑战。到目前为止，监测昆虫害虫在实践中一直很困难，而且劳动密集型，目前这进一步受到劳动力成本增加和短缺的挑战。我们的技术可以持续监测昆虫。我们通过低成本的物联网监视器来实现这一目标，这些监视器可以吸引目标害虫并对其进行成像。图像被传输到我们的服务器，来自多个监视器的数据被组合在一起，以对人群进行实时跟踪。该系统向种植者发送真实的实时警报，防止损害，限制农药的使用，让农民安心并降低成本。该项目的总体愿景是通过先进的物联网技术减少农业投入的使用和虫害损失，将价值670亿英镑的苹果种植行业的碳排放量减少约6%。我们将对苹果园中的3种蛾类害虫（苹果蠹蛾、夏果蠹蛾、浅棕色苹果蛾）进行优化管理，以最大限度地减少农药使用，提高英国农民的生产力、作物产量和盈利能力，并减少碳排放。这将通过使用Spotta的昆虫监测技术平台来实现，该平台包括直接昆虫感测沿着使用间接信息源，如局部天气监测，可以与昆虫种群建模相结合，以预测种群动态，进一步提高服务的准确性。

Spotta develops 24/7 smart insect monitoring systems and sells these as a service that alerts growers to harmful pests before they cause damage, enabling them to intervene at the right time, in the right place and with the right techniques.Insect pests are a key challenge in agriculture. Monitoring insect pests has until now been difficult in practice and labour intensive, this is currently further challenged by increasing labour costs and shortage. Our technology allows continuous monitoring of insects. We achieve this with low cost IoT monitors that attract and image the target pests. Images are transmitted to our server where data from multiple monitors are combined to give real-time tracking of populations. The system sends real time alerts to growers which prevents damage, limits the use of pesticides, gives farmers peace of mind and reduces cost.The overall vision for the project is to reduce carbon emissions by ~6% in the £67Bn apple cultivation industry by reducing both the use of agricultural inputs and losses to insect pests through advanced IoT technology. We will enable the optimal management of 3 moth pests (codling moth, summer fruit tortrix moth, light brown apple moth) in apple orchards to minimise pesticide use, increase productivity, crop yield and profitability for UK farmers, and reduce carbon emissions. This will be achieved through the use of Spotta's insect monitoring technology platform, which consists of direct insect sensing along with the use of indirect sources of information like localised weather monitoring which can be combined with modelling of insect populations to predict population dynamics further increasing the accuracy of the service.