Researchers at the Federal Polytechnic School of Lausanne explored the use of a neural network chatbot to assist in the design of a tomato harvesting robot. The project demonstrates how conversational AI can play a role in early-stage engineering planning, from concept to a practical prototype.
In an initial phase, the team used a chatbot to discuss the core idea of a mechanical harvester and to identify its intended purpose, the key design parameters, and the technical specifications that would guide development. They began with a big-picture conversation about potential future challenges facing humanity and concluded that robotic harvesting could contribute to addressing global food security. The dialogue then moved toward practical questions, such as what characteristics a harvesting robot should possess and which goals should drive its creation.
After agreeing on a basic robot format—specifically a motorized gripper designed to hold ripe tomatoes without bruising—the researchers probed the chatbot for more detailed guidance. They explored the most suitable gripper shape, material options, and even code snippets to control the device. This approach yielded a functioning prototype capable of picking tomatoes with care, minimizing damage to the fruit during collection.
As the scientists reflected on the process, they noted that while Chat-GPT is fundamentally a language model, its text-based code-generation capability provided valuable direction for design. They described the tool as a resonator that stimulated human creativity and helped refine practical engineering decisions. This perception highlights a growing role for AI in assisting engineers by expanding the range of questions they consider and accelerating exploratory thinking.
In summarizing the work, the team acknowledged both the promises and the limits of using chat-based AI in hardware design. They emphasized the importance of combining AI insights with hands-on testing, material science considerations, and robust control systems to ensure reliability in real-world harvesting scenarios. The dialogue with the chatbot allowed the researchers to articulate constraints, test assumptions, and iterate toward a feasible, tomato-safe harvesting mechanism that can operate in diverse agricultural environments.
Further reflections touch on how such tools might evolve. The experience at Lausanne suggests that AI-assisted design could become a standard step in the development of agricultural robotics, helping teams to frame problems clearly, explore broader design spaces, and speed up the transition from concept to working equipment. The study stands as an example of how language-based AI can partner with engineers to unlock new possibilities for sustainable farming and efficient food production. It also invites ongoing evaluation of AI capabilities in the context of real-world constraints, ensuring that automation serves growers and consumers alike. It remains to be seen how these systems will integrate with existing harvesting practices and how they will adapt to different crops, terrains, and climates. The researchers recognize that human expertise remains essential, guiding the interpretation of AI outputs and validating the safety and effectiveness of the resulting hardware. The collaboration between researchers and AI demonstrates a pathway toward more intelligent, adaptable agricultural robots that can help meet growing demand while preserving produce quality. In this evolving landscape, the Lausanne project contributes a pragmatic blueprint for combining conversational AI with hands-on engineering to advance robotics in farming. It also presents a reminder that innovation benefits from interdisciplinary dialogue and careful testing across real-world conditions. A final observation underscores the potential of AI as a creative amplifier—encouraging engineers to ask novel questions, test inventive ideas, and move confidently toward practical solutions that protect the freshness and integrity of harvested tomatoes. The study, while focused on a specific crop, signals broader implications for automated harvesting across various fruits and vegetables.