ISCF WAVE 1 AGRI TECH Agronomic Big Data Analytics for improved crop management

农业系统是复杂的，如果我们要实现粮食安全和保持环境质量，就必须对其进行管理。通过收集、处理和分析大数据集，正在改善工商复杂系统的管理。多年来，农民已经有可能使用联合收割机或拖拉机上的GPS驱动监视器、来自卫星传感器的数据以及对土壤的直接采样和分析来收集关于他们的作物和土壤的大数据集。这就提出了一个问题，农业能否进入大数据时代，以便比通过在有限数量的试验点进行田间试验的传统方法更快、更有力地解决管理问题。我们认为，这是可能的，但只有使用具有生物学意义的数据分析方法，而不是盲目挖掘数据以寻找相关性。这是一项可行性研究，目的是在英国各地的耕地上的大型数据集上测试两种量身定制的大数据分析方法。将使用两种通用方法，这两种方法都已经开发并发表在同行评议的文献中，并用作研究工具。第一种是所谓的边界线分析，这是一种确定作物能够达到的最高产量的方法，它是某种土壤或作物特性的函数，代表了可能限制潜在产量的因素(养分供应、冠层发育)。边界线分析需要大数据集，但与许多数据挖掘中使用的相对粗糙的工具相比，它有可能对作物系统提供更多的生物学洞察力，并有助于管理决策以消除限制效应。第二种方法集中于分析配备GPS的联合收割机上的产量监视器产生的产量图。这些地图显示了复杂的空间变化模式，通常很难进行有用的解释。当两个或更多季节的地图重叠时，变化就更复杂了。在过去的研究中，我们已经表明，一种称为k-均值聚类的模式识别方法可以用来将一块田地细分为产量季节间波动大致一致的区域。一个地区可能表现出持续的高产，而另一个地区可能表现出持续的低产量，而其他地区则在不同季节之间波动。这样的区域很可能代表作物受到类似限制的田地部分。例如，在土壤有效水含量相对较小的地方，干旱年份的产量可能会下降。一个出现养分缺乏的地区可能会在一系列季节中表现出产量的稳步下降。通过将田地的区划和每个地区的特征产量随时间的变化与土壤和其他环境信息联系起来，我们可以希望确定子田规模的关键限制因素，并通过在大数据集上进行这些分析，也应该可以得出农场和区域的规模模式。在这个项目中，我们将展示如何最有效地分析大型农艺数据集，以使最有把握的农艺公司向他们的客户提供建议，并从他们收集的数据中获得最大价值。这将有助于支持改进农场规模的管理，可能包括使用精准农业方法来应对田内差异。

Agricultural systems are complex, and must be managed if we are to achieve food security and maintain environmental quality. The management of complex systems in industry and commerce is being improved by the collection, processing and analysis of "big data" sets. For some years farmers have had the potential to collect big data sets on their crops and soils using GPS-driven monitors on the combine or tractor, data from satellite-borne sensors and the direct sampling and analysis of soils. This raises the question of whether agriculture can enter the big data era in order to solve management problems more quickly and robustly than through the conventional approach of field trials at a limited number of experimental sites. We contend that this is possible, but only by using methods to analyse the data that are biologically meaningful rather than by blindly mining data for correlations. This is a feasibility study to test two tailored big-data analytical methods on a large data set on arable fields from across the U.K. Two general approaches will be used, both of which have already been developed and published in the peer-reviewed literature, and used as research tools. The first is called boundary line analysis, a method to identify the maximum yield that a crop can achieve as a function of some soil or crop property that represents a factor (nutrient supply, canopy development) that may limit the potential yield. Boundary line analysis requires big data sets, but has the potential to give greater biological insight into the crop system, and to facilitate management decisions to remove limiting effects, than the relatively crude tools that are used in much data mining. The second approach is focussed on the analysis of yield maps produced by yield monitors on combine harvesters equipped with GPS. These maps show complex patterns of spatial variation, which are often hard to interpret usefully. When maps for two or more seasons are overlaid, the variability is even more complex. In past research we have shown that a pattern-recognition method called k-means clustering can be used to subdivide a field into regions within which the season-to-season fluctuations in yield are more or less uniform. One region may show consistent high yields, and another consistent low yields, while others fluctuate between seasons. Such regions are likely to represent parts of the field where the crop is subject to similar limitations. For example, where the soil available water content is relatively small yields may drop in drier years. A region with an emerging nutrient deficiency may show a steady decline in yield over a series of seasons. By relating the regionalization of the field, and each regions characteristic yield variations over time, to soil and other environmental information, we can hope to identify the key limiting factors at subfield scales, and by doing these analyses on big data sets, farm and regional scale patterns should also emerge. Within this project we shall show how a big agronomic data set can be most effectively analysed to allow the agronomy company which holds it best to advise their customers and obtain maximum value from the data that they collect. This will help to support improved management at farm scale, possibly including the use of precision agriculture methods to respond to within-field variation.