Can Artificial Intelligence help improve agricultural productivity?

  • AI use growing in agriculture
  • AI is applied in some aspects of the farming practices
  • Some examples of AI are offered
When l reflected on the future of agriculture, l could not avoid thinking about the power of technology to solve problems bedeviling this sector. Climate change, population growth, and food security concerns have pushed for innovative technological solutions to farming.
Artificial Intelligence is emerging as part of the solutions towards improved agricultural productivity. In this item, l will look at what AI is, how it is used in agriculture, common AI applications that have been used. I will conclude by prodding some emerging concerns on AI.
AI in agriculture
Individual agricultural activities on the farm take effort, for example planting, maintaining, and harvesting crops need money, energy, labor, and resources. What if we can use technology to replace some of the human activities and guarantee efficiency? That’s where artificial intelligence comes in.
To exemplify, a team of researchers developed an AI that can identify diseases in plants. This team used a technique known as transfer learning to teach the AI to recognize crop diseases and pest damage. In their case, they used TensorFlow, a Google’s open source library to build a library of AI 2,756 images of cassava leaves from plants in Tanzania. The success was that the AI was able to identify a disease with 98% accuracy. Read more here
This is one example, the other examples include the development by Abundant Robotics of an apple-picking robot; the John Deere uses AI and machine learning to care for plants and eliminate weeds. See other examples here
AI applications in agriculture
Agriculture is slowly becoming digital and AI in agriculture is emerging in three major categories, (i) agricultural robotics, (ii) soil and crop monitoring, and (iii) predictive analytics. Farmers are increasingly using sensors and soil sampling to gather data and this data is stored on-farm management systems that allow for better processing and analysis. The availability of this data and other related data is paving a way to deploy AI in agriculture.
We are seeing, as a result, a number of tech companies investing in algorithms that are becoming useful in agriculture. For example, we have image recognition used in potatoes, AgVoice by a Georgia-based startup for using natural language toolkit for field notes, and yield prediction algorithms based on satellite imagery.
The trend with the AI in agriculture has been in new tech start-ups who are implementing these solutions. In some cases, these are then bought by big agro-industry giants and there are still opportunities for AI in the public sector, and also in agriculture.
Will Al replace the knowledge and intuition that farmers have always had?, asks Peter Gredig. The response is probably not – but he acknowledges that AI will complement and challenge how decisions are made and perhaps improve the farming practices.
What do you think about AI in agriculture? Do you see an opportunity in increasing productivity?