The agricultural industry is poised for a data driven revolution. Technological advancements and the rise of big data have transformed the way we farm and produce food.

Agriculture is becoming increasingly reliant on technology and innovation in order to maintain production capacity. With depleting natural capital and increasing food demands, farmers will be turning to smart farming internet of things (IoT) solutions to lower cost, mitigate risk and maintain their production capacity.

Smart Farming IoT Solutions

Advancements in data based technologies and proliferation of telecommunications infrastructure will provide the framework for farmers to incorporate precision agriculture techniques and smart farming IoT solutions. Data analytics enhance famer database insights, allowing for increase traceability and management throughout agribusiness supply chains.

‘Smart farming’ and precision agriculture begins with on farm data collection. This is enabled through IoT connected devices, which enable large amounts of real time data collection.

Some of the main advantages provided by data analytics include:

  • Improved monitoring
  • Improved farm management
  • Enhanced traceability
  • Risk mitigation via forecasting

Farm machinery such as tractors as well as probes and drones are fitted with sensors. This can allow for a wide range of variables and systems to be monitored.

Smart Farming and Data Analytics

When collected data is transferred into cloud based systems, predictive models and other data sets can be applied, providing solutions and recommendations to farmers through digital interfaces.

Data analytics allow for identification of specific patterns in various aspects of farms production systems, providing increased prediction and visibility of risk.

Proliferation of machine learning and (ML) and artificial intelligence (AI) techniques serve to enhance smart farming IoT solutions, improving yields and profitability via consolidation of farm, weather and agronomic data.

The value of maintaining a farmer database will increase in consideration of agricultural block chain solutions, designed to improve supply chain traceability, management and performance within the industry.

Smart Farming Applications

When talking about smart farming and data driven solutions in agriculture, it can be overwhelming and confusing when trying to decipher where such technology can be applied to yield relevant insights. The following outlines some key areas where IoT agriculture solutions can be applied:

Plant & Soil IoT Applications

Data analytics agriculture  and smart farming enhances the ability of farmer’s to monitor crop and soil health. This is crucial for mitigating risk and maintaining agro-ecosystem health on farm, in order to continue production into the future and maintain profitability.

IoT uses for plant and soil health include:

  • Moisture sensing 
  • Soil nutrient monitoring
  • Field mapping and tailored input application maps 
  • Tailored fertiliser plans based on soil health and composition 
  • Precision chemical application 
  • Determining optimum times for planting and harvesting
  • Growth monitoring 
  • Soil temperature and Air temperature

Livestock IoT Applications

There are also a range of IoT application for livestock production, including

  • Monitoring livestock movements
  • Monitoring livestock health attributes 
  • Virtual fencing

Iot Agriculture Australia- Understanding the Benefits of Smart Farming

In the context of Australian agriculture, such applications will be crucial for hedging against the risk induced by weather volatility. IoT applications in agriculture analytics don’t come without limitations. The need for internet connectivity and telecommunications infrastructure in order to proliferate smart farming also present major barriers when considering connectivity issues faced by rural Australians.

Nonetheless, the benefits offered by smart farming exemplifies the need for farmer market research in order to enhance the R&D process for such technologies and drive proliferation. Utilisation of farmer database research will provide rich insight into existing perceptions of smart farming from the producer themselves, alongside the perceived advantages and disadvantages of such technologies in the context of on farm production.

When looking to the future, service providers of agricultural analytics and smart farming solutions will look to building AI capabilities from their individual farmer database. Longitudinal performance monitoring of enterprises will enhance ML for predicting performance and supplying producers with direction.

 

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