Precision Agriculture Web Portal Server for Canadian Farmers

尼亚加拉大学研究和创新部门，尼亚加拉研究，是迈克邓肯博士，NSERC工业研究椅子大学（IRCC）在精准农业和环境技术的中心。随着郊区的扩大，安大略的可耕地变得越来越宝贵，拥有当地/国内的食物供应至关重要。2012年，安大略的主要农业部门在国内生产总值（GDP）中产生了39.8亿美元。最大限度地提高目前耕地产量的最佳机会是提高产量。精准农业的重点是提高产量，同时减少肥料投入，从而降低成本，增加收入，并最大限度地减少对环境的影响。邓肯博士和他的团队开发了各种网络应用程序，提供真实的时间数据，地图，地图分析，和/或增值农民数据。目前，该团队的精准农业工作正处于可以与种植者和种植者顾问一起应用的阶段，以提高他们的生产力和竞争力。使用类似方法的种植者发现，投入节省高达20%，这对于现代种植操作来说是非常重要的。为了正确应用和测试精准农业理论，需要15-30名种植者，将需要最先进的服务器和磁盘系统，特别是由于种植者需要在将投入应用到他们的田地之前快速周转数据。在项目结束时，算法和系统将被开发成一个自动化的实体，有能力及时处理来自数千个农场（安大略有28,000个粮农）的数据。这笔资金将提供一个世界级的计算机引擎，用于开发该系统。

The Niagara College Research and Innovation division, Niagara Research, is the hub for Dr. Mike Duncan, the NSERC Industrial Research Chair for Colleges (IRCC) in Precision Agriculture and Environmental Technologies. With the suburbs expanding, arable land in Ontario is becoming increasingly valuable, and it is essential to have a local/domestic supply of food. Ontario's primary agriculture sector produced 3.98 Billion dollars in Gross Domestic Product (GDP) in 2012. The best opportunity to maximize the production from the currently farmed land is by increasing the yield. Precision agriculture is focused on increasing the yield, while decreasing the fertilizer inputs, which reduces costs, increases revenue, and minimizes environmental impact. Dr. Duncan and his team have developed a variety of web applications that provide real time data, mapping, map analysis, and/or value added farmer data. Currently, the team's Precision Agriculture work is at the stage where it can be applied with growers and grower consultants to increase their productivity and competitiveness. Growers using similar methods have discovered input savings as high as 20%, which is significant for a modern growing operation. To properly apply and test precision agriculture theories, with 15-30 growers as is required, a state of the art server and disk system will be required, particularly due to growers' needs for quick data turnaround prior to applying inputs to their fields. At the end of the project, the algorithms and the system will be developed into an automated entity that has the capacity to process data from thousands of farms (there are 28,000 grain farmers in Ontario) in a timely manner. The funding will provide a world-class computer engine that will be used to develop this system.