Automatic Generation of Highly Accurate Parameter-optimal Segmentation Models in Precision Agriculture
Year: 2024
Type: conference-paper
Abstract: In this paper, we demonstrate how an existing AutoML tool can be enhanced to generate highly accurate yet edge-friendly segmentation models for precision agriculture. Currently, only a few AutoML tools are capable of producing TinyML models for dense computer vision tasks, such as segmentation and object detection. Furthermore, creating accurate yet parameter-efficient segmentation models is particularly challenging in precision agriculture applications, such as crop and weed segmentation. We show that such models can be achieved by invoking only two AutoML API functions and tuning just four hyperparameters. A video of the tuning process is available at: https://youtu.be/L3MyXkVTzCc.
Authors: Swarnava Dey, Soma Dasgupta
Institution: Tata Consultancy Services (India)
Language: English
Topic: Smart Agriculture and AI
Subfield: Plant Science
Domain: Life Sciences
SDG: Zero hunger
Open Access status: gold
OpenAlex ID: W4411624643