Deep Learning-based Segmentation and Classification System for Artichoke Seedling Grading

Cubas Munoz P.B.; Huaman Kemper R.J.; Fiestas Sorogastua E.M.; Prado Gardini S.R. · Proceedings of the 2023 IEEE 30th International Conference on Electronics, Electrical Engineering and Computing, INTERCON 2023 (2023) · DOI: 10.1109/intercon59652.2023.10326060

Resumen

This study introduces a strategy for the automated grading of artichoke seedlings, a critical task in the transplantation process. The proposed strategy leverages two distinct perspectives, vertical and horizontal, to capture images of the seedlings using depth cameras. These images are then processed through segmentation modules to extract specific morphological attributes such as width, height, and leaf area. A feature vector composed of these attributes is evaluated by a Multi-Layer Perceptron (MLP) to classify the seedlings into two categories: 'good' and 'not good'. Two deep learning models, Mask R-CNN and YOLOv8, were employed for detection and instance segmentation tasks. Various backbones were experimented with, and the most effective ones were identified for both models. An MLP was developed for the classification stage, achieving an accuracy of 85.19% with specific training parameters. This research contributes a foundational framework for automated seedling grading, with potential applications and improvements in the field of agricultural automation.

Instituciones: Universidad Privada Antenor Orrego