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Smart Farming

What is Smart Farming Data?

Smart farming refers to the use of connected technology and artificial intelligence in the field of agriculture. It is also referred to as smart agriculture, agritech, agrotech, or even agro technology. However, the terms agritech and agrotech include the fields of horticulture and aquaculture.

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Why Is It Important to Have a Smart Farming Model?

There are few things in the world as important—or as risky—as growing food to feed people. Therefore, farmers should use the latest technologies and machine learning programs to ensure consistently productive yields.

Smart farming can also help alleviate poverty while improving sustainability efforts. This is especially so when open-source data are made available to farmers in developing countries.

What Internal Data Should I Have for a Good Smart Farming Model?

The line between internal data and external data can become blurred. For instance, while employee, equipment, and seed type data are clearly internal, the farmland itself might not be.

Put another way, the land belongs to the farmer but its soil quality, growing season, or biome might be classified as external data.

What External Data Is Essential for a Good Model?

In addition to climate and weather, essential external data for a smart farming system includes crop and livestock threat monitoring—for example, disease outbreaks.

What External Data May Prove Useful for a Good Model?

Additional external data includes supply chain data and market trends. Supply chain problems, for instance, affect the condition of the food when it reaches market, which affects the farm’s profitability. Meanwhile, market trends could indicate to the farmer which crops or farming techniques (such as organic or pesticide-free farming practices) he or she should consider taking on in the future.

What Are the Main Challenges of this Use Case?

Despite the fact that smart farming technology allows for 24/7 crop and livestock monitoring and predictive analytics, agriculture remains a very risky business. Natural disasters threaten the farm and ranch as well as the supply chain, as do political developments (e.g., embargoes and tariffs). Farmers much purchase and maintain equipment. And larger enterprises have stakeholders to please, who may be more susceptible to market trends than the farmers themselves.

Interesting Case Studies and Blogs to Look Into

Science Direct: Big Data in Smart Farming – A review
Statista: Smart Agriculture

Tangible Examples of Impact

A smart farming startup has launched the third element in its robot system for pesticide-free agriculture, an AI-based system called Wilma.

Wilma, developed by the Small Robot Company, provides ‘per plant intelligence’, using precise information gleaned by Tom, a scouting robot, on the health of the plant. If Wilma identifies the plant as a weed then Dick – the world’s first non-chemical robotic weeder – is dispatched to zap it.

eeNews Europe: AI for pesticide-free smart farming weed removal

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