SPAGRI-AI: Smart precision agriculture dataset of aerial images at different heights for crop and weed detection using super-resolution
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26220%2F24%3APU150543" target="_blank" >RIV/00216305:26220/24:PU150543 - isvavai.cz</a>
Result on the web
<a href="https://www.sciencedirect.com/science/article/pii/S0308521X2400026X" target="_blank" >https://www.sciencedirect.com/science/article/pii/S0308521X2400026X</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1016/j.agsy.2024.103876" target="_blank" >10.1016/j.agsy.2024.103876</a>
Alternative languages
Result language
angličtina
Original language name
SPAGRI-AI: Smart precision agriculture dataset of aerial images at different heights for crop and weed detection using super-resolution
Original language description
Abstract: CONTEXT Recently, smart agriculture has become an essential part of modern agriculture approaches from tillage, via plant seeding and grow support to their collection. With modern technologies, farmers can use substances like pesticides, herbicides, or fertilizers at precise dosages or to identify places on a field with specific production rates. OBJECTIVE The main objective of this study is to introduce a novel and a unique aerial image dataset of various fields acquired by UAV containing crops/weeds in the early phenophases captured in two different resolutions (2 mm and 7 mm per pixel). Secondly, the best super-resolution technique for high-resolution images, substitution with lower resolution is explored. METHODS For data acquisition, we employed DJI Matrice 600 equipped with a full-frame Sony Alpha A7R IV285 image sensor. Data were captured at flight heights of 26 and 95 m from 4 different fields in Central Europe. In addition, we proposed a methodology focused on the selection of an appropriate super-resolution method to enhance low-resolution aerial images to obtain better accuracy of crop/weed detection. As a baseline crop/weed detector for super-resolution effect evaluation, YOLOv5 architecture was used. Next, we explored the performance of several super-resolution models (U-Net++, ESRGAN, SwinIR), and fine-tuned the best-performed one. RESULTS AND CONCLUSIONS We present the new dataset named SPAGRI-AI: a novel unique dataset of aerial images for super-resolution experiments in smart precision agriculture. The dataset contains 27,638 aerial images (1024 × 1024 px) and additionally, it contains a subset of 2014 labeled images with 45,548 bounding boxes of 12 classes. The main purpose of the SPAGRI-AI is to provide the scientific community with real-world data to test new methods for super-resolution (SR) and crop/weed detection. During the evaluation of selected super-resolution models, the YOLOv5 model trained on high-resolution images resulted in
Czech name
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Czech description
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Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
20202 - Communication engineering and systems
Result continuities
Project
<a href="/en/project/VJ02010019" target="_blank" >VJ02010019: Tools for Handwriting fORensics</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)
Others
Publication year
2024
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
AGRICULTURAL SYSTEMS
ISSN
0308-521X
e-ISSN
1873-2267
Volume of the periodical
216
Issue of the periodical within the volume
April 2024
Country of publishing house
GB - UNITED KINGDOM
Number of pages
11
Pages from-to
1-11
UT code for WoS article
001181010900001
EID of the result in the Scopus database
2-s2.0-85183976148