利用无线传感器网络在地理定位环境下提高稻米产量的KPI部署

Oyibo Uchechukwu Moses, Nosiri Onyebuchi Chikezie
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引用次数: 0

摘要

水稻生产在全球粮食安全中发挥着重要作用。考虑到消费率的巨大增长,大米生产的自动化仍然是满足消费者需求的范式转变。本文旨在通过解决稻农(尤其是尼日利亚稻农)面临的五大挑战,实施一些选定的关键绩效指标(kpi),以提高水稻产量。观察到的制约因素包括:无法获得全年种植所需的水/雨水、过度施肥、杂草控制/预防、虫害/疾病控制以及啮齿动物和鸟类的入侵。基于zigbee的增强型无线传感器网络(eWSN)用于模拟各种网络场景,以演示给定农田中不同环境变量的数据感知。这是通过使用OPNET模拟器在不同场景下改变网络设备并研究网络性能来实现的。每一组新的网络设备都集成到Zigbee协调器(ZC)中,该协调器为其成员分配地址并形成个人局域网(PAN),从而表示对特定环境变量的数据感知。研究中设计并模拟了三种不同的场景。每个温度、湿度、运动和土壤养分传感器都产生了大约29bps的流量。在协调员处,车辆络绎不绝。温湿度协调器的数据流量为64bps,土壤养分协调器的数据流量为96bps。设计的结果展示了不同网络组件之间的有效通信,并提供了WSN如何同时用于监控农田上许多不同环境变量的见解。通过实施kpi,模拟结果提供了每公顷水稻农场产量从2.2公吨增加到8.7公吨的估计。
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KPI Deployment for Enhanced Rice Production in a Geo-Location Environment using a Wireless Sensor Network
Rice production plays a significant role in food security in the globe. The automation of rice production remains the paradigm shift to meet up with the consumer demand considering the tremendous increase in consumption rate. The paper aimed at implementing some selected key performance indicators (KPIs) for enhanced rice production by addressing five major challenges that face rice farmers, especially in Nigeria. The Non-availability of water/rain for year-round cultivation, disproportionate application of fertilizer, weed control/prevention, pest/disease control, and rodents and bird’s invasion are outlined as observed constraints. A Zigbee-based Enhanced Wireless Sensor Network (eWSN) was used to model various network scenarios to demonstrate data sensing of different environmental variables in a given farm land. This was achieved by varying network devices at different scenarios using OPNET simulator and understudying the network performances. Each new set of network devices was integrated to a Zigbee Coordinator (ZC) which assigns an address to its members and forms a personal area network (PAN), thus representing data sensing of a particular environmental variable. Three different scenarios were designed and simulated in the study. Each of the temperature and humidity, motion and soil nutrient sensors generated about 29bps of traffic. At the Coordinators, steady stream of traffic was received. The temperature and humidity Coordinators, received a traffic of 64bps each, while the soil nutrient Coordinator received data traffic of 96bps. The outcome of the design demonstrates effective communication between different network components and provides insight on how WSN could be used simultaneously to monitor a number of different environmental variables on a farm field. By implementing the KPIs, the simulation result provided an estimated yield increase from 2.2 to 8.7 metric ton per hectare of a rice farm.
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