In this study we argue that integrating ChatGPT into the data processing
pipeline of automated sensors in precision agriculture has the potential to
bring several benefits and enhance various aspects of modern farming practices.
Policy makers often face a barrier when they need to get informed about the
situation in vast agricultural fields to reach to decisions. They depend on the
close collaboration between agricultural experts in the field, data analysts,
and technology providers to create interdisciplinary teams that cannot always
be secured on demand or establish effective communication across these diverse
domains to respond in real-time. In this work we argue that the speech
recognition input modality of ChatGPT provides a more intuitive and natural way
for policy makers to interact with the database of the server of an
agricultural data processing system to which a large, dispersed network of
automated insect traps and sensors probes reports. The large language models
map the speech input to text, allowing the user to form its own version of
unconstrained verbal query, raising the barrier of having to learn and adapt
oneself to a specific data analytics software. The output of the language model
can interact through Python code and Pandas with the entire database, visualize
the results and use speech synthesis to engage the user in an iterative and
refining discussion related to the data. We show three ways of how ChatGPT can
interact with the database of the remote server to which a dispersed network of
different modalities (optical counters, vibration recordings, pictures, and
video), report. We examine the potential and the validity of the response of
ChatGPT in analyzing, and interpreting agricultural data, providing real time
insights and recommendations to stakeholders

本研究主张将 ChatGPT 集成到精确农业自动传感器的数据处理管道中，以增强现代农业实践的各个方面，并为政策制定者提供更直观、自然的方式与农业数据处理系统进行交互，从而实时分析和解释农业数据并向利益相关者提供建议。