Five IoT Smart Agriculture Use Cases

2023-02-06 03:54:52
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Five IoT Smart Agriculture Use Cases
Illustration: © IoT For All

Traditionally, the agricultural industry has been manually intense and largely reactive. Recent technical advances, such as IoT, have empowered farmers to dramatically change their modus operandi. With IoT, farmers can add intelligence to analog and mechanical devices, streamline processes, gain efficiency, and overall, build stronger businesses. Smart farming is the term for this new approach in agriculture, and there are many examples in the industry.

“With IoT, farmers can add intelligence to analog and mechanical devices, streamline processes, gain efficiency, and overall, build stronger businesses.”

-Wittra

Collecting information, such as environmental conditions, improves the quality and quantity of the produce while minimizing risk and waste. The technology can also be adapted to specific machinery and systems, e.g., tractors and sprinkler systems, and use the data collected to provide a complete real-time view of operations. Smart farming impacts every aspect of the agriculture process. The tools track inventory as it makes its way to the farmer, soil conditions as they prepare for planting, crop growth, weather conditions, harvesting, and distribution. As a consequence, smart farming solutions have attracted growing interest, and purchases are on the rise. The global smart agriculture market reached $14.1 billion in 2021 and is expected to increase to $25.25 billion in 2027, exhibiting a CAGR of 9.8 percent. Here are five examples of how smart agriculture is changing farming.

Smart Agriculture Examples

#1: Soil Management

In the agricultural industry, soil can be seen as the very foundation of everything. Growing and harvesting crops constantly fluctuate and can therefore greatly impact a business. With IoT, farmers can do all of the following:

  • Gain insight into soil composition, precipitation, and temperature to maximize soil performance.
  • Decide if pesticides or fertilizers need to be added or removed.
  • Rely on irrigation sensors that can monitor the dryness of the soil and operate sprinklers accordingly.

Agricultural businesses gain real-time visibility into soil viability and unique ground conditions, in order to best utilize the land. IoT offers farmers the means to gain more information about what is happening so they can manage proactively and not reactively.

#2: Crop Monitoring

Farmers want to achieve consistent crop quality and avoid various aberrations, and IoT can help.  Sensors constantly monitor items, such as leaf quality, color, and, root strength; then compare current measurements with historical data, and determine how well crops are growing. With it, farmers can do all of the following:

  • Better forecast the production runs.
  • See crop growth.
  • Note any anomalies, such as diseases, pest infestations, or harsh climate that will lower the yield.
  • Understand what their final output will be.
  • Set better expectations.
  • Enhance product distribution.
  • Monitor business expenses more accurately.
  • Know when to schedule the next shipment of seeds and grains.

In essence, the business flows more consistently. Once the finished product is out for distribution, the next batch is ready to be planted. The insights lower production risks and empower farmers, so they do not face product shortages and income disruptions.

#3: Predictive Maintenance

Another critical smart agriculture example is predictive maintenance. The advent of intelligent IoT sensors enables suppliers to collect device performance information as their equipment functions. Artificial intelligence, machine learning, and data analytics gauge an asset’s typical efficiency and wear and tear based on items like vibration analysis, oil analysis, and thermal imaging. Predictive models feature algorithms that identify when an asset will need to be maintained, or repaired. The benefits include:

  • Lengthened machinery lifecycles.
  • Lowered downtime.
  • Increased employee productivity.

Data from the U.S. Department of Energy indicates that predictive maintenance is extremely cost-effective. Putting a predictive maintenance program in place yields:

  • Tenfold increase in ROI
  • 25-30 percent reduction in maintenance costs
  • 70-75 percent decrease in breakdowns.
  • 35-45 percent reduction in downtime.

In essence, farmers gain a much better way of ensuring that their equipment functions at peak performance.

#4: Livestock Management

Traditional methods of livestock monitoring relied on individuals manually inspecting animals and looking for signs of disease or injury, a costly, highly unreliable, and inefficient method. IoT livestock management solutions take the guesswork out of determining an animal’s health. How does IoT livestock management work? Using a wearable collar or tag, battery-powered sensors monitor an animal’s location, temperature, blood pressure, and heart rate.

The information is wirelessly sent to an application in near-real-time. Farmers access information via mobile devices and so they can do the following:

  • Check the health and location of each animal in their herds from anywhere.
  • Receive alerts if a metric falls outside of the normal range.
  • Know immediately which livestock is affected and which is not.

Also, farmers no longer need to physically examine each animal’s vitals to see if an illness has spread. Temperature tracking helps to determine the peak of mating season. Livestock monitoring solutions also use tracking to gather and store historical data on preferred grazing spots. Keeping livestock healthy is important because if they become ill, their development falls behind their cohorts. Such animals typically do not catch up to the rest of the herd, and they become less valuable to the farmer. With this smart agriculture example, farmers gain more insight into their animals’ health and well-being.

#5: Process Automation

Farmers need to increase efficiency. Decades ago, farmers began replacing manual work with machines. Now, IoT offers them the next step in that process:

  • Computer technology to take on work typically done by hired hands.
  • Streamline repetitive manual tasks, such as irrigation, fertilization, pest control, and even seed planting.
  • Sift through large volumes of performance data, like crop growth, herd eating, and soil conditions.
  • Find aberrations.
  • Send alerts automatically to staff smartphones, as needed.

Famers become more informed and more proactive with these capabilities. They see problems, dig into the issue, troubleshoot, create workarounds, and work faster and more efficiently.

A Competitive Industry

Farming is a mature, highly competitive, manually intensive industry. The above smart agriculture examples highlight this. Emerging IoT technology streamlines operations in areas like soil management, predictive maintenance, and automation. Using smart sensors to collect environmental and machine metrics enables farmers to make informed decisions and improve just about every aspect of their daily workflow.

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  • Agriculture
  • Animal Tracking
  • Automation
  • Farming
  • Predictive Maintenance

  • Agriculture
  • Animal Tracking
  • Automation
  • Farming
  • Predictive Maintenance

参考译文
五个物联网智能农业用例
**图示:© 物联网(IoT)所有** → 传统上,农业行业劳动密集,且多半是被动应对的。最近的技术进步,例如物联网(IoT),使农民能够彻底改变其运作方式。借助物联网,农民可以向模拟和机械设备添加智能,优化流程,提高效率,整体上打造更强大的业务。这种农业的新方法被称为“智能农业”,业内有许多实例。“借助物联网,农民可以向模拟和机械设备添加智能,优化流程,提高效率,整体上打造更强大的业务。” — Wittra 收集环境条件等信息,可以提高作物质量和产量,同时最小化风险和浪费。这项技术还可以适应特定的机械和系统,例如拖拉机和洒水系统,并利用收集的数据提供对操作的实时完整视图。智能农业影响农业过程的方方面面。这些工具可以追踪从农作物到农民手中的库存情况,在种植准备阶段监测土壤状况,作物生长情况,天气状况,收割和配送。因此,智能农业解决方案日益受到关注,相关采购也在增长。2021年,全球智能农业市场达到141亿美元,预计到2027年将增长至252.5亿美元,年复合增长率(CAGR)为9.8%。以下是五个智能农业如何改变农业的例子。 **智能农业实例 #1:土壤管理** 在农业行业,土壤可以被视为一切的基础。种植和收割作物是一个不断波动的过程,因此会对业务产生重大影响。借助物联网,农民可以做到以下几点: - 了解土壤成分、降水量和温度,以最大化土壤表现。 - 决定是否需要添加或移除农药或肥料。 - 依靠灌溉传感器来监测土壤干湿度,并相应地操作洒水系统。 农业企业可以获得土壤可用性和地面状况的实时视图,以最大程度地利用土地。物联网为农民提供了获取更多信息的手段,使他们能够主动管理,而不是被动应对。 **#2:作物监测** 农民希望实现一致的作物质量,避免各种异常情况,而物联网可以提供帮助。传感器不断监测诸如叶片质量、颜色和根部强度等项目,然后将当前数据与历史数据进行比较,以判断作物生长情况。借助物联网,农民可以做到以下几点: - 更好地预测生产周期。 - 观察作物生长情况。 - 注意异常情况,例如病害、虫害或恶劣气候,这些都会降低产量。 - 了解最终产出情况。 - 设定更合理的预期。 - 优化产品配送。 - 更准确地监测业务支出。 - 知道何时安排下一批种子和谷物的运输。 本质上来说,业务流程变得更一致。一旦成品投入配送,下一批次的作物就已准备就绪。这些洞察降低了生产风险,使农民能够避免产品短缺和收入波动。 **#3:预测性维护** 预测性维护是另一个关键的智能农业实例。智能物联网传感器的出现使供应商能够在设备运行过程中收集性能数据。人工智能、机器学习和数据分析技术根据振动分析、油液分析和热成像等数据评估资产的典型效率和损耗情况。预测模型中的算法能够识别资产何时需要维护或修理。其优势包括: - 延长机械设备的使用寿命。 - 降低停机时间。 - 提高员工的生产效率。 美国能源部的数据表明,预测性维护极具成本效益。实施预测性维护计划可带来以下成果: - 投资回报率(ROI)提高十倍。 - 维护成本降低25%-30%。 - 故障减少70%-75%。 - 停机时间减少35%-45%。 简而言之,农民获得了更有效的方式来确保其设备以最佳性能运行。 **#4:牲畜管理** 传统的牲畜监测方法依赖于人工检查动物,并寻找疾病的迹象,这种方法成本高、可靠性差且效率低。物联网牲畜管理解决方案可以消除判断动物健康状况的猜测成分。物联网牲畜管理如何运作?通过佩戴式项圈或标签,电池供电的传感器可监测动物的位置、体温、血压和心率。这些信息通过无线方式几乎实时地发送到应用程序中。农民可以通过移动设备访问这些信息,从而做到以下几点: - 从任何地方查看其畜群中每只动物的健康和位置情况。 - 如果指标超出正常范围,接收警报。 - 立即了解哪些牲畜受到影响,哪些未受影响。 - 此外,农民不再需要逐一检查牲畜的生命体征以判断是否疾病已扩散。体温追踪有助于确定发情高峰期。牲畜监测解决方案还利用追踪功能来收集和存储关于动物偏好的放牧地点的历史数据。保持牲畜健康非常重要,因为如果它们生病了,其发育会落后于同伴。这类动物通常无法跟上畜群的进度,从而对农民的价值也较低。通过这个智能农业实例,农民可以获得对动物健康和福祉的更多洞察。 **#5:流程自动化** 农民需要提高效率。几十年前,农民开始用机械取代人工。如今,物联网为他们提供了这一过程的下一步:用计算机技术代替人工。优化重复性的人工任务,例如灌溉、施肥、病虫害控制,甚至种子种植。分析大量性能数据,例如作物生长、畜群饮食和土壤状况。发现异常。需要时自动向员工的智能手机发送警报。具备这些能力后,农民将变得更为知情且更具主动性。他们可以发现问题、深入调查、解决故障、制定替代方案,并且更快、更高效地工作。 **竞争激烈的行业** 农业是一个成熟、竞争激烈、劳动密集的行业。上述智能农业实例凸显了这一点。新兴的物联网技术优化了土壤管理、预测性维护和自动化等领域的运作。利用智能传感器收集环境和设备指标,使农民可以做出明智决策,并改善其日常工作的几乎所有方面。 **推文分享 邮件** 农业 动物追踪 自动化 种植业 预测性维护 → 农业 动物追踪 自动化 种植业 预测性维护
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