Bringing Semantics to the Vineyard: An Approach on Deep Learning-Based Vine Trunk Detection

• Type: Article in International Scientific Journal
• Date: 2021

Authors

André Silva Aguiar (Author) Other; Nuno Namora Monteiro (Author) Other; Filipe Neves Santos (Author) FEUP; Eduardo J. Solteiro Pires (Author) Other; Daniel Silva (Author) Other; Armando Jorge Sousa (Author) FEUP; José Boaventura Cunha (Author) Other

Journal

Title: Agriculture (Switzerland)

Vol. 11 No. 2

Pages: 1-20

Publisher: MDPI

Indexing

ISI Web of Knowledge - 0 Citations

Scopus - 0 Citations

Other information

Authenticus ID: P-00T-EPG

DOI: 10.3390/agriculture11020131

Abstract (EN): The development of robotic solutions in unstructured environments brings several challenges, mainly in developing safe and reliable navigation solutions. Agricultural environments are particularly unstructured and, therefore, challenging to the implementation of robotics. An example of this is the mountain vineyards, built-in steep slope hills, which are characterized by satellite signal blockage, terrain irregularities, harsh ground inclinations, and others. All of these factors impose the implementation of precise and reliable navigation algorithms, so that robots can operate safely. This work proposes the detection of semantic natural landmarks that are to be used in Simultaneous Localization and Mapping algorithms. Thus, Deep Learning models were trained and deployed to detect vine trunks. As significant contributions, we made available a novel vine trunk dataset, called VineSet, which was constituted by more than 9000 images and respective annotations for each trunk. VineSet was used to train state-of-the-art Single Shot Multibox Detector models. Additionally, we deployed these models in an Edge-AI fashion and achieve high frame rate execution. Finally, an assisted annotation tool was proposed to make the process of dataset building easier and improve models incrementally. The experiments show that our trained models can detect trunks with an Average Precision up to 84.16% and our assisted annotation tool facilitates the annotation process, even in other areas of agriculture, such as orchards and forests. Additional experiments were performed, where the impact of the amount of training data and the comparison between using Transfer Learning and training from scratch were evaluated. In these cases, some theoretical assumptions were verified.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 20