农业科技播客：Tria Americas 的 Jim Beneke 探讨配方中的自适应人工智能
2025年五月9日 在本期农业科技讲座中 AgriBusiness Global, Jim Beneke, Vice President of Tria Americas discusses how adaptive artificial intelligence and edge computing are driving advancements in agriculture, particularly through autonomous machinery and smart systems that operate with limited connectivity.
* 这是该播客经过编辑的部分文字记录。
ABG: AI and robotics are reshaping many aspects of agriculture. What specific opportunities do you see for crop input manufacturers to leverage adaptive AI and humanoid robotics in product development, application, or delivery?
Jim Beneke: Adaptive AI has many potential applications in agriculture—everything from modeling and simulation to developing unique crop formulas and traits. At Tria, we focus more on edge AI and edge computing, where real-time processing is needed in environments with limited or no connectivity.
This includes robotics, autonomous machinery, and smart assistance tools that manage systems in the field. While robotics is still in its early stages, we expect significant growth as AI capabilities advance. Over time, the integration of smart devices into larger platforms will further accelerate adoption and amplify the benefits of AI in agriculture.
ABG： What are some of the biggest barriers to integrating AI and robotics into farm equipment and vehicles, particularly when it comes to applying crop protection products at scale and under regulatory scrutiny?
约翰： AI certainly offers enormous benefits and holds a lot of promise for agriculture, but it’s still an evolving technology with several key challenges. At Tria, we help OEMs developing these advanced machines and robotics by offering expertise in embedded system design, integration, and software enablement.
One major barrier is cost—these systems typically require a much higher upfront investment due to their advanced capabilities. There’s also the need for supporting infrastructure, particularly around connectivity, to ensure reliable data collection and communication.
From an AI standpoint, accuracy and reliability are critical. AI models are only as effective as the data used to train them, so developing high-quality datasets and training those models takes significant time and expertise. Additionally, these systems rely heavily on sensors, which must function consistently across variable environmental conditions—something that can be difficult to guarantee in the field.
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