1. Season Prediction and Optimization
AI plays an important role in predicting crop yields based on data analysis from weather, soil, temperature, and humidity. AI systems use data from sensors and satellite images to make accurate predictions about planting and harvest times, helping farmers optimize yields.
Case Studies: John Deere’s AI platform uses data from fields and equipment to optimize farming processes and help farmers save money.
2. Automated Farm System
Robots and automated systems are revolutionizing the agricultural industry. Agricultural robots, powered by AI, are capable of autonomously plowing, planting, and harvesting with high precision, helping to reduce dependence on manual labor and increase production efficiency.
Practical example: Naïo Technologies’ robot is capable of self-navigating and removing weeds automatically without human supervision.
3. Water and Fertilizer Management
AI helps effectively manage water and fertilizer resources by analyzing data from soil sensors, satellite images and weather to determine the amount of water and fertilizer needed for crops. This helps save water resources and improve farming efficiency.
Practical application: The CropX AI system provides information about water and fertilizer needs, helping farmers optimize resources and crop yields.
4. AI in Pest Detection
AI has the ability to analyze images from fields to detect pests early, helping farmers handle them promptly. Deep learning algorithms can analyze crop images and identify signs of disease, thereby providing precise treatment solutions.
For example: The Plantix application uses AI to detect diseases and pests through crop images, providing treatment instructions to farmers.
5. AI In The Food Supply Chain
AI does not stop at the farm but also supports the food supply chain, optimizing the transportation and preservation of products. AI systems analyze factors such as temperature, delivery time and environmental conditions to ensure food retains its best quality, while minimizing waste.
Practical application: IBM Food Trust uses AI and blockchain to track the food supply chain, helping ensure safety and transparency during production and distribution.
6. AI In Food Production
In food production, AI supports the optimization of production processes from material planning to product quality inspection. AI helps detect errors in the production process, minimizing human intervention and ensuring products meet high quality standards.
For example: Tomra Sorting company uses AI to classify fruits and vegetables, helping to increase productivity and reduce waste during the production process.
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