Journal
FUTURE INTERNET
Volume 14, Issue 7, Pages -Publisher
MDPI
DOI: 10.3390/fi14070199
Keywords
agriculture; deep learning; IoT; robot; trunk detection
Categories
Funding
- Fundacao La Caixa [PD20-00011]
- PrunusBot project-Autonomous controlled spraying aerial robotic system and fruit production forecast [PDR2020-101-031358]
- EAFRD
- European Union under the Portugal 2020 program
- Fundacao para a Ciencia e a Tecnologia (FCT-MCTES) [UIDB/00151/2020]
- FCT/MCTES
- EU funds [UIDB/EEA/50008/2020]
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The study utilized advanced detector technology to achieve accurate detection of vine trunks in agriculture, aiding agricultural robots in performing various tasks. Increasing the size of the input model and the quantity of training data improved the model's performance.
The concept of the Internet of Things (IoT) in agriculture is associated with the use of high-tech devices such as robots and sensors that are interconnected to assess or monitor conditions on a particular plot of land and then deploy the various factors of production such as seeds, fertilizer, water, etc., accordingly. Vine trunk detection can help create an accurate map of the vineyard that the agricultural robot can rely on to safely navigate and perform a variety of agricultural tasks such as harvesting, pruning, etc. In this work, the state-of-the-art single-shot multibox detector (SSD) with MobileDet Edge TPU and MobileNet Edge TPU models as the backbone was used to detect the tree trunks in the vineyard. Compared to the SSD with MobileNet-V1, MobileNet-V2, and MobileDet as backbone, the SSD with MobileNet Edge TPU was more accurate in inference on the Raspberrypi, with almost the same inference time on the TPU. The SSD with MobileDet Edge TPU achieved the second-best accurate model. Additionally, this work examines the effects of some features, including the size of the input model, the quantity of training data, and the diversity of the training dataset. Increasing the size of the input model and the training dataset increased the performance of the model.
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