Enhancing assessment of corn growth performance using unmanned aerial vehicles (UAVs) and deep learning

Xiao, Juan and Suab, Stanley Anak and Chen, Xinyu and Singh, Chander Kumar and Singh, Dharmendra and Aggarwal, Ashwani Kumar and Korom, Alexius and Widyatmanti, Wirastuti and Mollah, Tanjinul Hoque and Minh, Huynh Vuong Thu and Khedher, Khaled Mohamed and Avtar, Ram (2023) Enhancing assessment of corn growth performance using unmanned aerial vehicles (UAVs) and deep learning. Measurement: Journal of the International Measurement Confederation, 214. ISSN 02632241

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Abstract

The advancement of unmanned aerial vehicles (UAVs) offers precise and accurate spectral and spatial information about crops and plays a pivotal role in precision agriculture. This study used UAVs, geographic information systems (GIS), and deep learning technology to monitor corn growth performance across different management practices. Two experimental corn fields were divided into four plots to evaluate the effects of varying corn management practices (i.e., seeding schedule, planting depth, and fertilization method) on corn growth performance. RGB and MicaSense multispectral cameras were mounted on UAVs to collect corn field images. YOLOv5 was investigated for counting corn plants. Plant height, Normalized Difference Vegetation Index (NDVI), Normalized Difference Red-Edge Index (NDRE), plant density, and plant volume were mapped based on UAV images. Additionally, the Otsu thresholding method was evaluated as an automatic method for separating plant height, NDVI, and NDRE values from the background. YOLOv5 and Otsu thresholding were efficient and accurate for automatically counting corn plants and extracting corn plant heights as well as VIs, respectively. The emergence rates of corn seeds were 40, 33, 41, and 62 in plots A, B, C, and D, respectively. Variations in corn field management practices significantly affected the emergence rate, with fertilizer application close to seeds emerging as the optimal practice for achieving higher emergence rates across experimental plots. This study used deep learning and UAV to provide precise information and valuable insights into corn field practices, which can help farmers optimize corn cultivation. The techniques applied in this study could be extrapolated to improve cultivation processes for other crops. © 2023 Elsevier Ltd

Item Type: Article
Additional Information: Cited by: 27
Uncontrolled Keywords: Antennas; Cultivation; Deep learning; Information management; Seed; Unmanned aerial vehicles (UAV); Vehicle performance; Aerial vehicle; Corn; Corn fields; Corn growth; Growth performance; Management practises; Multispectral sensors; RGB sensor; Unmanned aerial vehicle; YOLOv5; Crops
Subjects: G Geography. Anthropology. Recreation > GA Mathematical geography. Cartography
Divisions: Faculty of Geography > Departemen Sains Informasi Geografi
Depositing User: Sri Purwaningsih Purwaningsih
Date Deposited: 05 Sep 2024 07:51
Last Modified: 05 Sep 2024 07:51
URI: https://ir.lib.ugm.ac.id/id/eprint/6462

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