ChatGPT in the context of precision agriculture data analytics
- URL: http://arxiv.org/abs/2311.06390v1
- Date: Fri, 10 Nov 2023 20:44:30 GMT
- Title: ChatGPT in the context of precision agriculture data analytics
- Authors: Ilyas Potamitis
- Abstract summary: We argue that integrating ChatGPT into the data processing pipeline of automated sensors in precision agriculture has the potential to bring several benefits.
We show three ways of how ChatGPT can interact with the database of the remote server.
We examine the potential and the validity of the response of ChatGPT in analyzing, and interpreting agricultural data.
- Score: 0.19036571490366497
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: 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
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