Mariana Ortega
Hello, everyone! I'm Mariana Ortega, a passionate advocate and innovator in the realm of agricultural automation powered by artificial intelligence. With a deep - rooted belief in the transformative power of AI to reshape the future of farming, I've dedicated my career to exploring, developing, and implementing AI - driven solutions that optimize agricultural processes, enhance productivity, and ensure sustainable food production.
My journey in this field began during my academic pursuits at [University Name], where I earned degrees in Agricultural Engineering and Computer Science. This unique combination of knowledge laid the foundation for my understanding of both the practical aspects of farming and the technological advancements that could revolutionize it. While studying, I was constantly struck by the inefficiencies and challenges faced by traditional agriculture, from labor - intensive practices to unpredictable environmental factors. I saw AI as the key to unlocking a new era of precision and automation in farming.
Currently, I'm focused on further integrating AI with emerging technologies like blockchain and the Internet of Things (IoT) in agriculture. By combining AI - driven data analytics with blockchain's secure and transparent data - recording capabilities, we can create a more reliable and traceable food supply chain. Meanwhile, IoT devices can provide an even larger volume of real - time data, enabling more accurate decision - making in agricultural automation.
I'm also deeply committed to sharing my knowledge and experiences. I frequently participate in international conferences and workshops, where I present my research and engage in discussions about the future of AI in agriculture. I believe that collaboration and knowledge exchange are crucial for the widespread adoption of these technologies, especially in developing countries where small - scale farmers could greatly benefit from AI - powered automation.
In a world where the demand for food is constantly increasing, and the challenges of climate change and resource scarcity loom large, I'm excited to be at the forefront of using AI to transform agriculture. I'm confident that through continuous innovation and collaboration, we can build a more efficient, sustainable, and resilient agricultural industry for generations to come.
Early agricultural production relied on farmers' experience for sowing, irrigation and fertilization, resulting in prominent problems of resource waste and inefficiency. The introduction of AI technology has completely changed this situation. The combination of computer vision and deep learning algorithms gives agricultural machinery "smart eyes". For example, the self-driving tractor launched by John Deere is equipped with a multispectral camera and AI recognition system, which can identify crops and weeds in real time, and accurately control the weeding machinery to spray pesticides only on weeds, reducing the use of pesticides by 70% compared with traditional operations.
In the field of precision irrigation, Israel's intelligent drip irrigation system realizes dynamic calculation of crop water requirements through the collaboration of soil moisture sensors, weather stations and AI models. The system collects soil moisture data every hour, combines crop growth stages and weather forecasts, controls irrigation errors within ±5%, and improves water-saving efficiency by 40%. China's Beidahuang Group uses AI drones for direct seeding of rice. Through route planning algorithms and seeding control systems, the uniformity of seeding has been increased to 98%, and the operating efficiency is 50 times that of manual work.
Automation breakthroughs in pest control are also significant. The AI pest monitoring system developed by the RIKEN Institute of Physical and Chemical Research in Japan can identify 0.5 square centimeters of disease spots at an altitude of 200 meters through a hyperspectral camera carried by a drone. It can predict the spread of diseases by combining historical data and issue a prevention and control warning 72 hours in advance, reducing the use of pesticides by 35%.
In the quality inspection link, AI spectral analysis technology realizes rapid and non-destructive testing. The AI Raman spectrometer of Bruker of Germany can complete the detection of pesticide residues and heavy metal content in grains within 30 seconds, covering more than 200 types of harmful substances, with an accuracy rate comparable to laboratory testing. At the same time, the traceability system built by combining AI and blockchain realizes the full-process data tracking of agricultural products from the field to the table. Consumers can obtain more than 200 pieces of information such as planting, processing, and transportation by scanning the code, enhancing food safety.
In the future, AI agricultural automation will move towards a more advanced stage. The popularization of edge computing and 5G technology will enable real-time data processing and millisecond-level response of equipment; the application of large model technology can build a more accurate agricultural production prediction model; the human-machine collaborative system will be further optimized, such as combining AI-assisted decision-making with human expert experience to improve the ability to respond in complex scenarios. It is estimated that by 2030, the global AI agricultural market size will exceed US$50 billion, and the penetration rate of automated equipment will increase to 65%, injecting strong impetus into the sustainable development of agriculture.
From the field to the table, AI-driven agricultural automation has profoundly changed the production logic of traditional agriculture. This technological revolution has not only improved agricultural production efficiency and product quality, but also provided innovative solutions for global food security and sustainable resource utilization. With the continuous iteration and deepening of technology applications, AI will accelerate agriculture towards a smarter, more efficient and greener direction.