Smart Farming, The Time Is Now
IoT and IIoT devices are becoming increasingly common as the world continues to digitise and advancements are made in artificial intelligence (AI). Using AI software and AI-enabled hardware, more industries are able to collect and analyse vital information.
Generally speaking, AI represents a class of technologies that enable machines to act with higher levels of intelligence in order to mimic human capabilities, such as sensing, comprehension, and action, as well as the ability to learn from experience and adapt over time.
One of the industries taking advantage of these technological steps forward is agriculture. Owing to the limited resources available, high pressure for greater yield, it makes sense that farmers are exploring how to leverage technology.
In this article we’ll cover:
- Smart AI agriculture insights into an evolving space
- How AI is currently being used
- AI technology looming around the corner
Smart AI Agriculture Insights
While there are moonshot ambitions aplenty around the use of AI in the agriculture sector, it’s thought that AI will continue to use problem-solving tools and strategies that focus on increasing productivity, increasing yield, and reducing labour costs.
To this end, farmers are looking at AI tech that can help them more quickly understand the state of their crops and automate time-consuming manual tasks. Even in South Africa, 67% of organisations have pointed at machine learning as the AI technology most useful to them (followed by smart robotics and biometrics), although it’s worth noting that due to South Africa’s high unemployment rates and low understanding of AI, the excitement among employees around the possibilities of AI is generally replaced by the fear of job losses.
The Current Use Of AI In Agriculture
On a global scale, the top ways that AI is currently being used is in the automation of tasks and data analysis for predictive means. Automation and prediction includes a broad range of applications, from increasing employee productivity to predicting customer churn or consumer conversion rates and proactively managing machinery downtime.
The top examples of AI in agriculture:
- Using drones and computer vision for speedy field condition assessments in order to achieve early detection of pests, diseases and weeds and prioritise integrated pest management.
- Weather forecasting to understand temperatures and rainfall in order to make strategic decisions and increase yield.
- Through intelligent automation, data analytics, sensor technologies, IoT, machine learning, and cognitive computing, there can be greater efficiency and effectiveness, as well as precision agriculture.
- Applying text analytics and natural language processing to key terms or research can lead to faster identification of research data.
- AI provides farmers with the forecasting and predictive analytics to reduce errors and minimise the risk of crop failures – essentially, farmers can better manage their risk.
- AI can help farmers identify what produce will be most profitable by analysing market demand.
- Machine learning, including deep learning, can teach systems to detect outliers in data or match data against known patterns with unprecedented precision and recall, resulting in faster innovation and greater efficiency.
AI From A South African Context
Looking at South Africa, producers are currently applying AI to everything, from regenerative agriculture that relies on smart technologies to improve efficiency, to gene technology in order to increase yields.
Not only this, but certain technologies can actually improve the quality and quantity of what’s produced. A great example is seen in the use of robotics to hugely reduce packing time and to identify low quality produce.
Another exciting use is through climate monitoring technology. South African vineyards, for instance, can place almost imperceptible monitors onto the leaves to check the temperatures and chemicals, and detect changes or problems early in the growing process. This kind of data allows producers to take corrective action, and for those whose crops require early intel, like grapes or produce for export, this can be vital to the decision-making process around what to do next.
The Future Of AI
There’s little doubt about the fact that farming is becoming more sophisticated through the help of AI. Farmers no longer merely talk about where to get the best fertilizer (although we’re sure this is a valid conversation). Instead, they are becoming well-versed in discussions around the use of drones and satellites.
As the adoption of AI grows, farmers will benefit from increased access to a growing pool of knowledge about emerging practices and use cases, both industry-wide and worldwide.
We can already see the effect through the automation of manual tasks and optimised efficiencies around crop management and business administration. But in the future, farmers will divest authority for tilling, planting, fertilising, monitoring and harvesting – delegating it all to AI and using algorithms to control drip-irrigation systems, self-driving tractors and combine harvesters, clever enough to respond to the weather and the exact needs of the crop.
Whatever comes next, what’s clear is that with each new AI advancement there are new opportunities for farmers to streamline their processes, increase efficiency, optimise their supply chains and increase profit margins – all while decreasing waste and conserving resources.
A Smart Partner To Support Smart Farming
Keeping on top of emerging technologies and figuring out how to implement the AI that fits in with your strategy, as well as the connectivity required to support your farm can be challenging.
Let Huge Connect simplify the complexity and work with you to choose the right connectivity solution to better prepare you for a digital future. Huge Connect making sure you stay connected.
Let’s Connect.
