Precision Agriculture: Uses GPS, sensors, and drones to monitor crop and soil conditions, leading to efficient resource management and higher yields while reducing environmental impact.
Internet of Things (IoT): Connected devices gather real-time data for smart decision-making, improving water use, reducing energy consumption, and optimizing farming processes.
Artificial Intelligence (AI): AI predicts weather, detects diseases, and automates farming tasks, enhancing productivity while minimizing resource use and environmental harm.
Robotics and Automation: Autonomous machines perform labor-intensive tasks like planting and harvesting, increasing efficiency and reducing manual labor.
Blockchain Technology: Ensures transparency in the food supply chain, improving traceability, sustainability, and reducing fraud.
Big Data and Analytics: Provides insights through data analysis to improve decision-making, anticipate challenges, and adopt sustainable practices.
Biotechnology (CRISPR): Genetic modification enhances crop resistance to pests and climate challenges, boosting productivity with less resource input.
Mobile Apps: Provide farmers with real-time market data and agronomic advice, improving decision-making and promoting sustainable practices.
3D Printing: Allows farmers to produce custom tools on-demand, reducing costs and the environmental impact of manufacturing.
Agriculture could undergo a revolution because to digital technologies, which can boost yield, improve sustainability and improve farmers' capacity to make decisions. Such as mobile applications, automation, drones and aerial photography, precision agriculture, the Internet of Things (IoT), genomics and biotechnology tools, etc.
Mr. Mouhamad has already provided you with quite a good list of potential technological and digital improvement in agriculture.
There is one that I am missing that I recently hear more and more about and that is the 'Digital Twin'-concept. Think of it as a computerized copy of a farming system, build from different models and fed with several years worth of 'real-life' data.
This 'Twin' can then be used to model potential outcomes of new measurements against for example flooding or drought, without immediately having to change a whole farm management system in the real world.
When coupled with actual field experiments it is a powerful technique that can help demonstrate and calculate the influence of a certain measure, as well as calculate the size of said influence.