The use of agricultural drones is expected to grow significantly due to the increasing demand for food and the challenges faced by farmers in traditional farming methods. These drones are equipped with advanced sensors, GPS, and AI-powered analytics that capture high-resolution images, analyze soil health, and detect plant stress. The benefits of using agricultural drones include automated takeoff and obstacle avoidance, which saves time, lowers labor costs, and enhances yield predictions. Drones also enable precision agriculture, which helps optimize resource use, minimize waste, and increase sustainability. Companies like DJI, Parrot, AgEagle Aerial Systems, and XAG are leading the agricultural drone revolution with advanced models tailored for agriculture. The market is expected to reach $8.03 billion by 2029, driven by the need for smarter, more efficient farming solutions. Autonomous agriculture beyond traditional drones is also transforming how we monitor crops and spray, with ground-based robots taking over complex tasks like planting, weeding, and harvesting. Developing agricultural robots requires a comprehensive approach to data and technology, including collecting high-quality data, AI and ML platforms, robotics middleware and frameworks, hardware considerations, and software integration requirements. Despite the advantages of deploying agricultural robots, several challenges stand in the way of their widespread adoption and effective operation, such as environmental factors, regulatory constraints, high initial costs, data quality issues, and maintenance concerns. Platforms like Encord are designed to address these critical data challenges by providing a comprehensive, data-centric environment tailored to streamline the AI development lifecycle for CV applications in demanding fields like agricultural drones and robotics.