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Optimizing Agricultural Production with AI and ML Solutions

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Resolving production bottlenecks with DeepTech. Agriculture is a critical part of the global economy, but it`s also one of the most challenging sectors to operate in. Agriculture is challenged by the growth of the global population at an alarming rate, and with it the demand for food. It`s estimated that we`ll need to produce 70% more food by 2050 to meet the needs of the world`s population. Farmers are under constant pressure to produce more food with fewer resources, and they often don`t have access to the latest technology or information while they face increasing competition from abroad and rising production costs.

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Artificial Intelligence (AI) and Machine Learning (ML) can help farmers overcome these challenges. It can help increase crop yields while reducing production costs at farm level. Our algorithms use real-time data and visualize analytics to improve predictions about which crops will thrive and where pests are likely to appear. We also develop optimized pesticide mixes to be applied only where needed, reducing waste and environmental impact. With our algorithms` ability to identify pests and disease using image processing and recognition framework, farmers/growers can get immediate actionable advice for limiting crop losses. It also connects them with advisors for further assistance.

Traditional methods are no longer enough to handle huge food demand, which is driving farmers and agri-based companies to find newer ways to increase production and reduce input and output waste. As a result, Artificial Intelligence (AI) and Machine Learning (ML) is steadily emerging as part of the agri industry’s tech evolution. There’s no doubt that crop yields and quality are more efficient now than they were centuries, or even decades ago with the help of AI. We’ll take a look at some of the most promising AI/ML use-cases for agriculture:
  • Crop and soil health monitoring
  • Identifying pests and disease incidence
  • Increasing irrigation efficiency
  • Improving farming practices
  • Enabling climate smart practices
  • Reducing input wastage
  • Crop yield forecasting
  • Prediction on early onset of pests
  • Identifying crop water stress
Proprietary AI algorithm for agribusiness
  • High data granularity
  • Reduce crop losses
  • Combat climate change

High performance computing for agriculture
  • Optimizing farming practices
  • Data-driven decision making
  • Yield forecast and estimation
  • Mitigate crop deficiencies