Dear Patrick Ahmed Akan

By analyzing historical and updated data of the place to be planted to predict agricultural conditions and improve planning for these lands. This is one of the experiments I conducted years ago in the Emirates, where I used machine learning models to predict production rates based on weather data, irrigation, and soil quality.

Key areas of AI-driven improvements include precision farming, predictive analytics, automated machinery and robotics, crop disease detection and pest management, smart irrigation systems, supply chain optimization, genetic selection and automation in vertical and indoor farming. AI-powered sensors gather real-time data on soil conditions, moisture levels, nutrient content, and crop health, allowing farmers to apply water, fertilizers, and pesticides only where needed. AI models can also predict future yields and climate adaptation, enabling farmers to make informed decisions on planting and harvesting schedules. AI-powered machines can perform labor-intensive tasks autonomously and efficiently, reducing labor costs and time. AI-based breeding programs can analyze plant genetics to select crops with the best traits for higher yields, disease resistance and climate resilience. AI-controlled growing conditions can create optimal growing conditions year-round, improving yields even in non-traditional farming environments.