SPAGRI-AI: Smart precision agriculture dataset of aerial images at different heights for crop and weed detection using super-resolution

Kód výsledku v 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>

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

SPAGRI-AI: Smart precision agriculture dataset of aerial images at different heights for crop and weed detection using super-resolution

Popis výsledku v původním jazyce

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

Název v anglickém jazyce

SPAGRI-AI: Smart precision agriculture dataset of aerial images at different heights for crop and weed detection using super-resolution

Popis výsledku anglicky

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

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

20202 - Communication engineering and systems

Projekt

<a href="/cs/project/VJ02010019" target="_blank" >VJ02010019: Nástroje forenzní expertizy ručně psaného písma</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2024

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

AGRICULTURAL SYSTEMS

ISSN

0308-521X

e-ISSN

1873-2267

Svazek periodika

216

Číslo periodika v rámci svazku

April 2024

Stát vydavatele periodika

GB - Spojené království Velké Británie a Severního Irska

Počet stran výsledku

11

Strana od-do

1-11

Kód UT WoS článku

001181010900001

EID výsledku v databázi Scopus

2-s2.0-85183976148