AI in Agriculture

Artificial Intelligence (AI) in Agriculture

प्रगत प्रशिक्षणाद्वारे शेतीत तंत्रज्ञानाचा वापर शिका (Advanced Training Program)

Artificial Intelligence (AI) in Agriculture training program covers practical and essential topics:

This syllabus will be changed based on the audience, such as farmers, agribusiness professionals, or agriculture students. Practical demos, field visits, and case studies also included for a hands-on experience.

Training Curriculum (10 Modules)

• Overview of Artificial Intelligence (AI) and its relevance in agriculture
• History and evolution of AI in farming
• Applications of AI in global and Indian agriculture
• Case studies of AI usage in agriculture

• Basic concepts of AI, Machine Learning (ML), and Deep Learning (DL)
• Data collection and management in agriculture
• Algorithms used in AI for agriculture (Regression, Classification, Decision Trees, Neural Networks)
• Role of IoT (Internet of Things) and Big Data in farming

• Introduction to precision agriculture and its importance
• Use of AI in soil management, water resource management, and smart irrigation systems
• Drones, sensors, and remote sensing technology for real-time farm data collection
• Automated irrigation systems using AI

• AI for disease detection, pest control, and nutrient management
• Image processing techniques for crop health monitoring using AI
• Yield prediction models based on AI algorithms
• Role of weather forecasting in crop planning

• Introduction to robotics in agriculture (autonomous tractors, drones, robots)
• AI-driven machinery for planting, weeding, and harvesting
• Integration of AI and robotics for smart farming systems

• AI in farm-to-fork traceability and transparency
• Using AI for forecasting market demand and optimizing supply chains
• Minimizing food wastage through AI-based inventory and logistics management

• AI applications for monitoring animal health and behavior
• Automated feeding, milking, and breeding systems
• Disease detection and prevention in livestock using AI

• Government schemes supporting AI in agriculture
• AI and sustainable agriculture practices
• Ethical concerns, challenges, and opportunities in AI adoption

• Practical exercises in AI-based farm management software
• AI in drone image analysis for crop health
• Real-time data analytics using AI for yield prediction
• Group projects on applying AI in specific agricultural challenges

• Emerging trends in AI and agriculture
• Future technologies in smart farming
• AI’s role in addressing climate change and sustainability in agriculture