I-Corps: Data Analytics for Hand-Picked Agriculture

I-Corps项目的更广泛影响/商业潜力在于，通过个性化地理空间数据收集，改善手工收获粮食作物的食品安全和保障。就像高速公路上的摄像头能够通过每个司机和他们的手机的个性化数据来告诉给定高速公路上的交通状况，手工收获的食物可以从提高可追溯性和透明度中受益，从块级数据到单点收获，回顾交叉的地图数据。地理特定数据还可以比文本块数据更好地测量土地生产力。个性化的、个人拥有的数据使员工拥有信息的可移植性，从而建立一份可靠性和绩效的简历。此外，它还成为一个可行的透明度计划的组成部分。该项目将有可能提高收割机的经济地位，提高农民更好地了解其劳动力和土地的能力，为营销人员提供改进的数据，以确保食品安全出现故障，并在不增加系统步骤的情况下提高消费者的透明度。I-Corps项目进一步发展了高价值手工采摘特种作物的数据收集方法。所讨论的数据是关于谁在何时何地收获了什么作物的精确记录。背景数据（质量、灌溉/施肥数据、文化实践地图等）被添加并为农民可视化。该项目将测试数据集之间的关系，以提高决策技能；更好的管理取决于更好的测量。它提供了对空间和时间的非均匀性的更好理解。解决这些不同质性导致调查和减少方差。该系统将能够理解劳动过程中的瓶颈和工作流程问题，并允许动态和实时决策。该系统还将允许在系统的几个部分中找到最佳决策的定量方法，并为基于公共数据的分散决策提供平台。

The broader impact/commercial potential of this I-Corps project is to improve food safety and security of hand harvested food crops through individualized geo-spatial data collection. Much like highway cameras have an ability to tell the status of traffic on a given freeway are vastly improved through the individualized data of each driver and their phones, hand harvested food can benefit from improving traceability and transparency from block level data to single points of harvest, recalling intersecting mapping data. Geo-specific data can also enable better land productivity measurement over textual block data. Individualized, personally owned, data enables workers to have portability of information, building a resume of dependability and performance. In addition, it becomes an integral component of a viable transparency scheme. The project will potentially improve the economic standing of harvesters, improve the capability of farmers to better understand their workforce and land, assist marketers with improved data to insure against breakdowns in food safety, and enable consumers transparency without added steps to the system.This I-Corps project further develops improved data collection methods in high-value hand-picked specialty crops. The data in question is a precise record of who harvests what, where and when. Contextual data (quality, irrigation/fertilization data, cultural practice maps, etc.) are added and visualized for farmers. The project will test the relationship of data sets toward improved decision skills; better management depends on better measurement. It provides a better understanding of inhomogeneities in space and time. Addressing these inhomogeneities leading to investigation and reductions of variance. The system will enables comprehension of bottlenecks and workflow problems in labor process and allow dynamic and real-time decisions. The system will also allow quantitative approaches toward finding optimal decisions in several parts of the system and provide a platform for decentralized decision-making based on common data.