In-Field Automatic Identification of Pomegranates Using a Farmer Robot.

ABSTRACT: Ground vehicles equipped with vision-based perception systems can provide a rich source of information for precision agriculture tasks in orchards, including fruit detection and counting, phenotyping, plant growth and health monitoring. This paper presents a semi-supervised deep learning framework for automatic pomegranate detection using a farmer robot equipped with a consumer-grade camera. In contrast to standard deep-learning methods that require time-consuming and labor-intensive image labeling, the proposed system relies on a novel multi-stage transfer learning approach, whereby a pre-trained network is fine-tuned for the target task using images of fruits in controlled conditions, and then it is progressively extended to more complex scenarios towards accurate and efficient segmentat

SUBMITTER: Devanna RP 

PROVIDER: S-EPMC9370860 | biostudies-literature | 2022 Aug

REPOSITORIES: biostudies-literature