Five IoT Smart Agriculture Use Cases

2023-02-06
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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
传统上,农业一直是手工密集型的,而且在很大程度上是被动的。最近的技术进步,如物联网,使农民能够极大地改变他们的工作方式。通过物联网,农民可以为模拟设备和机械设备添加智能,简化流程,提高效率,并在总体上建立更强大的业务。智能农业是这种农业新方法的术语,在这个行业中有很多例子。“通过物联网,农民可以为模拟和机械设备添加智能,简化流程,提高效率,并在总体上建立更强大的业务。”收集信息,如环境条件,可以提高产品的质量和数量,同时最大限度地减少风险和浪费。该技术还可以适用于特定的机械和系统,例如拖拉机和自动喷水灭火系统,并使用收集的数据提供完整的实时操作视图。智能农业影响着农业生产过程的方方面面。这些工具可以跟踪到农民手中的库存、他们准备种植时的土壤状况、作物生长、天气状况、收获和分配。因此,智能农业解决方案吸引了越来越多的兴趣,购买量也在上升。2021年,全球智能农业市场规模达到141亿美元,预计到2027年将增至252.5亿美元,复合年增长率为9.8%。以下是智能农业如何改变农业的五个例子。在农业中,土壤可以被视为一切的基础。作物的种植和收获不断波动,因此会对业务产生很大影响。通过物联网,农民可以做到以下所有事情:农业企业实时了解土壤活力和独特的地面条件,以最佳地利用土地。物联网为农民提供了获取有关正在发生的事情的更多信息的手段,这样他们就可以主动管理,而不是被动管理。农民希望获得一致的作物质量,避免各种偏差,物联网可以提供帮助。传感器不断监测项目,如叶片质量,颜色,和,根系强度;然后将当前的测量结果与历史数据进行比较,并确定作物的生长情况。有了它,农民可以做到以下所有事情:从本质上讲,业务流动更加一致。一旦成品被分发出去,下一批就可以种植了。这些见解降低了生产风险,增强了农民的权能,使他们不会面临产品短缺和收入中断的问题。另一个重要的智能农业例子是预测性维护。智能物联网传感器的出现使供应商能够收集设备性能信息作为其设备功能。人工智能、机器学习和数据分析通过振动分析、油液分析和热成像等项目来评估资产的典型效率和磨损情况。预测模型的特点是算法可以识别资产何时需要维护或维修。来自美国能源部的数据表明,预测性维护非常具有成本效益。将预测性维护计划落实到位:从本质上讲,农民获得了一种更好的方式,以确保他们的设备以最佳性能运行。传统的牲畜监测方法依赖于个人手动检查动物并寻找疾病或受伤的迹象,这是一种昂贵、高度不可靠且效率低下的方法。物联网牲畜管理解决方案消除了对动物健康状况的猜测。物联网牲畜管理如何运作?通过可穿戴的项圈或标签,电池驱动的传感器可以监测动物的位置、温度、血压和心率。信息以近乎实时的方式无线发送到应用程序。农民通过移动设备获取信息,因此他们可以做以下事情: 此外,农民不再需要亲自检查每只动物的重要器官,以确定疾病是否已经传播。温度追踪有助于确定交配季节的高峰。牲畜监测解决方案还使用跟踪来收集和存储首选放牧地点的历史数据。保持牲畜健康很重要,因为如果它们生病了,它们的发育就会落后于它们的同伴。这样的动物通常追不上兽群中的其他动物,对农民来说,它们的价值就会降低。通过这个智能农业的例子,农民对他们的动物的健康和福祉有了更多的了解。农民需要提高效率。几十年前,农民开始用机器取代手工劳动。现在,物联网为他们提供了这一过程的下一步:农民们在这些能力上变得更知情、更主动。他们发现问题,深入研究问题,排除故障,创造变通办法,更快更有效地工作。农业是一个成熟的、竞争激烈的、体力密集的产业。上述智能农业的例子突出了这一点。新兴的物联网技术简化了土壤管理、预测性维护和自动化等领域的操作。使用智能传感器收集环境和机器指标,使农民能够做出明智的决定,并改善日常工作流程的各个方面。
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