16AGRITECHCAT5: Feasibility of a Hyper Spectral Crop Camera (HCC) for agriculture optimisation

农民和园艺师面临着不同的困难，需要多年积累的关于他们的田地和作物的经验和知识。这些困难包括但不限于：田地生长/产量不平衡；化肥使用不准确和估计不准确；灌溉不均匀以及病虫害/杂草的局部变异。此外，最佳的收获时间仍然是推测的，而且往往不准确。面对众多变数，农民无法避免每年的成本和作物产量的巨大变化。帮助农民优化的工具，例如化肥和水的使用或疾病的早期发现，将为最佳控制作物生长提供有用的诊断和管理能力。目前，确实存在针对这些挑战的解决方案，然而，当前的系统体积大、重量重、不便于携带，因此不容易部署。它们的价格也高得令人望而却步--通常是10,000 GB-150,000 GB--通常只适用于机载或卫星成像应用或实验室分析。实际上，针对上述农业挑战的现有解决方案仅限于大规模耕作和/或高价值作物。在这些昂贵的系统中，光谱仪扫描或拍摄作物的图像是在可见光和/或红外波长上进行的，分析显示与作物生长条件有关的光谱图像特征变化。不同的工厂和不同的原因，利益的签名各不相同。作物的“颜色”(可见光和红外线)在接近成熟期时也会发生变化，光谱仪扫描为与作物水合、化肥使用、疾病进展和收获有关的明智管理决策提供科学信息。高光谱成像(HSI)可以捕捉到这些变化：HSI系统捕获大量场景图像，每个图像位于传感器技术确定的一定范围内的不同波长，以产生所谓的高光谱数据立方体，其中空间域中的每个像素都包含被观测对象的光谱轮廓。对于我们的应用，可以分析这些光谱信息，以做出关于最大限度提高作物产量的挑战的诊断/管理决策。提出的高光谱作物相机将具有低成本、紧凑便携、操作简单和坚固耐用的特点。相机外壳将包含传感器、电池和电子设备，以生产一个简单、轻巧的小设备。这种设备将适合手持使用，或者可能安装在低成本的无人机上，用于当地的空中分析。农业和农业中的HSI技术，成本从GB 10K到GB 150K不等。农民和/或农学家可以通过应用连片灌溉系统来：-通过提供优化或局部灌溉来节约用水--及时识别病虫害/杂草区域以便早期干预--优化化肥的使用--确定最佳收获期，帮助提高作物产量--改善整个田野地区作物产量的均衡性--减少工时、手工测量田地等--减少对技术农学培训/知识的需求。

Farmers and horticulturists face varying difficulties that require experience and knowledge of their fields and crops, gained over many years. These difficulties include, but are not limited to: uneven growth/yield of their fields; inexact and estimated fertiliser application; uneven irrigation and local variations in pests/diseases/weeds. Additionally, the optimum harvest timing is still speculated and often inexact. Faced with numerous variables, farmers cannot avoid high variations in costs and crop yields from year to year. Tools to assist farmers to optimise e.g. fertiliser & water applications or early detection of disease will provide a useful diagnostic and management capability for optimum control of crop growth. Currently, solutions for these challenges do exist, however, current systems are large, heavy, not portable and as such are not readily deployable. They are also prohibitively expensive - typically £10,000 - £150,000 each - and are generally only suitable for use in airborne or satellite imaging applications or laboratory analysis. In effect, the current solutions available for the aforementioned agricultural challenges are limited to large scale farming and/ or high value crops. In these expensive systems, a spectrometer scan or image of the crop is taken at visible and/or infrared wavelengths with analysis showing spectral image signature changes relating to crop growth conditions. The signatures of interest varies from plant to plant and from cause to cause. The "colour" of a crop (visible and IR) also changes as it approaches maturity, with spectrometer scans providing scientific information for informed management decisions in relation to crop hydration, fertiliser application, disease progression and harvesting. Hyperspectral Imaging (HSI) can capture these changes: HSI systems capture a large number of images of the scene, each at a different wavelength within some range determined by the sensor technology, to produce a so called hyperspectral data cube in which each pixel in the spatial domain contains a spectral profile of the object observed. For our application, this spectral information can be analysed to make decisions about the diagnostics/management of challenges in maximising crop yield. The proposed Hyperspectral Crop Camera (HCC) will be: low-cost, compact & portable, simple in operation and robust. A camera housing will contain the sensor, battery and electronics to produce one small simple lightweight device. This device would be suitable for handheld use or potentially mountable in a low cost drone for local airborne analysis. HSI technology in farming and agriculture which can cost anything from £10k - £150k. Application of HCC can allow a farmer and/ or agriculturists to: - Save water by providing optimised or localised irrigation - Timely identify areas of pests/diseases/weeds for early intervention - Optimise use of fertiliser - Determine optimum harvest time and help increase crop yield - Improve evenness of crop yield across field area - Reduced man hours, manually surveying fields etc - Reduce need for technical agronomy training/knowledge.