应用太阳能发电厂进行智能农场农业设计

R. Alfanz, Aqbal Hasnain, Wahyuni Affan, Martiningsih
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引用次数: 1

摘要

每年随着世界人口的增加,土地越来越满,不足以用于农业。各种各样的技术发展被滥用于种植作物。本研究的目的是通过无土种植和利用城市的技术进步来设计一个智能农场农业系统。智能农业是一种利用物联网(IoT)帮助农民和公众从菜园中轻松获取数据的农业技术。水培法是一种无土耕作,使用溶解在水中的矿物质或肥料。营养膜技术(NFT)是一种水培方法,利用一层薄薄的营养物通过管道装置流动。需要持续电力的水泵可以利用太阳能发电厂作为能源。太阳能电池板上的离网系统在不接入国家电网的情况下,根据需要的功率产生电能。该设计应用了一个多传感器系统,包括营养传感器和pH传感器,以及自动溶液泵,温度pzm - 004t传感器,以及一个数据记录器,收集数据并通过可视化应用程序连接到互联网服务器。IO作为监控平台。结果,pH值在6.5到7.5之间。TDS传感器测试的pH传感器误差为0.313%,精度为99.69%;TDS传感器测试的TDS传感器误差为1.18%,精度为98.82%。农业农场系统测试中,1 ~ 2周的pH溶液测试误差为38%,营养液测试误差为38.73%。此外,太阳能电池板产生的总输出功率为1700.56 W,而总负载需求为1165.74 W。根据测试结果,智能农业系统可以监测营养和PH溶液水平,自动泵控制稳定的溶液,PLTS电源可以为泵提供适当的电源
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Smart Farm Agriculture Design by Applying a Solar Power Plant
Every year as the world's population increases, land is getting full and not enough to be used in agriculture. Various types of technological developments have been abused to grow crops. The purpose of this research is to design a smart farm agriculture system by planting without soil and utilizing technological advances in the city. Smart farming is a technology in agriculture with the Internet of Things (IoT) to help farmers public people get data easy from the garden. Hydroponics is soil-less farming that uses minerals or fertilizers dissolved in water. Nutrient Film Technique (NFT) is one of the hydroponic methods using a thin layer on the flow of nutrients through pipe installations. Pumps that require continuous electrical power can harness solar power plants as an energy source. Off-grid system on solar panel plan produce electrical energy according to the required power without being connected to state electricity network. The design applies a multi-sensor system that includes nutrient sensors and pH sensors, as well as automatic solution pumps, temperature PZEM-004T sensors, and a data logger that collects data and connected to internet server with visual on app Thinger.io as monitoring platform. The results, pH is ranging from 6.5 to 7.5. The TDS sensor testing resulted in a 0.313% pH sensor error with an accuracy of 99.69%, and the TDS sensor testing resulted in a 1.18% TDS sensor error with an accuracy of 98.82% also the agriculture farm system testing, the testing in 1 until 2 weeks showed an error percentage of 38% in the pH solution and 38.73% in the nutrient solution. In addition, the solar panels generated a total power output of 1700.56 W, while the total load demand was 1165.74 W. Based on the testing results, the smart farming system can monitor nutrient and PH solution levels, the automatic pump controls a stable solution, and the power sourced from PLTS can supply the pump properly
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