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.
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AI in AGRICULTURE
Individual agricultural activities on the farm takes 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
The Global AI in AGRICULTURE Outlook
AI having applications in various sectors including agriculture has completely transformed the approaches of the agriculture market. AI in Agriculture helps the farmers in examining weather, soil, and field data to improve farming operations and crop productivity. AI in the agriculture market seems to be driven by the Internet of Things (IoT) due to its ability to revolutionize and transform current farming methods to a new level. Although, collecting accurate field data requires high initial investments which may hamper the growth of AI in the agriculture market.
The report provides a detailed overview of the industry including both qualitative and quantitative information. It provides an overview and forecast of the global Artificial Intelligence in Agriculture based on by type, component, and application. It also provides market size and forecast till 2025 for overall Artificial Intelligence in Agriculture with respect to five major regions, namely; North America, Europe, Asia-Pacific (APAC), Middle East and Africa (MEA) and South America (SAM). The market by each region is later sub-segmented by respective countries and segments. The report covers analysis and forecast of 16 counties globally along with current trend and opportunities prevailing in the region.
Besides this, the report analyzes factors affecting market from both demand and supply side and further drivers evaluate market dynamics affecting the market during the forecast period i.e., restraints, opportunities, and future trend. The report also provides exhaustive PEST analysis for all five regions namely; North America, Europe, APAC, MEA and South America after evaluating political, economic, social and technological factors affecting the market in these regions.
The “Global Artificial Intelligence in Agriculture Analysis to 2025” is a specialized and in-depth study of the Artificial Intelligence in Agriculture industry with a focus on the global market trend.
Also, key Artificial Intelligence in Agriculture players influencing the market is profiled in the study along with their SWOT analysis and market strategies. The report also focuses on leading industry players with information such as company profiles, products and services offered, financial information for the last 3 years, and the key development for past five years.
Some of the key players influencing the market are
- Ag Leader Technology
- Trimble Inc.
- Agribotix LLC
- Granular, Inc.
- SAP
- Mavrx Inc.
- PrecisionHawk
- aWhere
- IBM
- Prospera Technologies
