The AI-Powered Fourth Agricultural Revolution

Fourth Agricultural revolution

Agriculture, the cultivation of domestic plants and animals, began about 12,000 years ago in the Fertile Crescent. This was the first agricultural revolution, leading to large settlements and complex urban centers.

The second agricultural revolution began in Britain in the 17th century, introducing new irrigation techniques, fertilizers, and transport methods. The third, the Green Revolution in the 1940s, saw huge increases in crop yields due to new fertilizers and pesticides, averting a projected population collapse.

Now, a fourth revolution is underway. Technological advancements, like the Internet of Things (IoT) and artificial intelligence (AI), are creating new efficiencies in agriculture.

Meeting the Needs of a Growing Population

The global population, exceeding 8.1 billion, will increase by 2 billion in the next 30 years. Increasing drought, rising fuel costs, environmental regulations, and invasive pests pose challenges to food production.

Production needs to increase by as much as 110% to feed future generations. Farmers are increasingly reliant on digital technology to manage crops and boost yields. Precision agriculture, leveraging AI and IoT, is the way forward.

The Basics of AI and IoT Technology in Agriculture

AI’s use in farming has accelerated since the 1970s. Big data collection and analysis are creating major efficiencies for growers.

Data on humidity, pests, rainfall, soil moisture, and temperature is collected using various technologies, including IoT sensors, UAVs, UGVs, and satellites. Machine learning and deep learning programs analyze this data.

Mike Flaxman, from HEAVY.AI, explains: “That data would be completely overwhelming if it weren’t for AI being able to organize it.”

AI programs filter out irrelevant data and identify exceptional conditions, enabling farmers to take timely action. The technology allows for natural language queries and visual representations of answers, aiding decision-making.

Robotic technologies, guided by AI, can perform tasks like weed removal, pesticide spraying, seed planting, and harvesting, increasing precision and reducing damage.

Synthesis of Historical and Contemporary Data

AI models leverage historical agricultural data, dating back thousands of years, to make predictions about crucial factors like climate and resource availability.

This data is integrated with information from IoT sensors, UAVs, UGVs, and satellites to generate specific and useful forecasts.

Flaxman notes: “AI is really good at cleaning up dirty data.” AI programs filter out static and generalize the most useful information.

Farm management information systems (FMIS) integrate various information sources and generate usable recommendations for planning, management, harvest, and sale. These recommendations can reduce costs, increase yields, and contribute to sustainable practices.

Key Applications of IoT and AI in Agriculture

  1. Management of Water Resources: AI helps determine when and how to irrigate crops, optimizing water use and reducing runoff.
  2. Detection and Mitigation of Weeds and Pests: AI analyzes aerial images to detect early signs of disease or pest infestation, enabling targeted treatment.
  3. Soil Conditions and Planting: AI assists in analyzing soil nutrient content, composition, and texture, aiding in crop selection, planting depth, and spacing.
  4. Monitoring Growth and Managing Harvests: AI technology monitors plant growth, estimates health and maturity, and predicts yields. It can also assist in harvesting and sorting produce.

Limitations and Challenges

While AI analysis of major crops is advanced, availability for less common crops is uneven.

Valeria Kogan, from Fermata, points out: “The quality of data still remains a huge challenge due to the absence of ground truth.”

The technology may not be available for some plants yet. Flaxman adds: “That’ll be the next major push in agriculture… Nobody grows general crops, right?”

Accessibility and affordability for independent farmers remain challenges. Integrating technologies and data into actionable information requires substantial investment.

Training programs can equip growers, especially in food-stressed regions, to utilize IoT and AI effectively.

AI and IoT technologies are transforming agriculture, offering solutions to meet the demands of a growing population. While challenges remain, the potential for increased efficiency, sustainability, and food security is significant.

As Avvocato notes: “Being able to increase crop production 10, 20 or 30% can have a dramatic impact.”

The fourth agricultural revolution, powered by AI and IoT, is poised to reshape the way we feed the world.

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