Growth Monitoring and Yield Estimation of Maize Plant Using Unmanned Aerial Vehicle (UAV) in a Hilly Region

Article


Sapkota, Sujan and Paudyal, Dev Raj. 2023. "Growth Monitoring and Yield Estimation of Maize Plant Using Unmanned Aerial Vehicle (UAV) in a Hilly Region." Sensors. 23 (12), pp. 1-26. https://doi.org/10.3390/s23125432
Article Title

Growth Monitoring and Yield Estimation of Maize Plant Using Unmanned Aerial Vehicle (UAV) in a Hilly Region

ERA Journal ID34304
Article CategoryArticle
AuthorsSapkota, Sujan and Paudyal, Dev Raj
EditorsSankaran, S
Journal TitleSensors
Journal Citation23 (12), pp. 1-26
Article Number5432
Number of Pages26
Year2023
PublisherMDPI AG
Place of PublicationSwitzerland
ISSN1424-8220
1424-8239
Digital Object Identifier (DOI)https://doi.org/10.3390/s23125432
Web Address (URL)https://www.mdpi.com/1424-8220/23/12/5432
Abstract

More than 66% of the Nepalese population has been actively dependent on agriculture for their day-to-day living. Maize is the largest cereal crop in Nepal, both in terms of production and cultivated area in the hilly and mountainous regions of Nepal. The traditional ground-based method for growth monitoring and yield estimation of maize plant is time consuming, especially when measuring large areas, and may not provide a comprehensive view of the entire crop. Estimation of yield can be performed using remote sensing technology such as Unmanned Aerial Vehicles (UAVs), which is a rapid method for large area examination, providing detailed data on plant growth and yield estimation. This research paper aims to explore the capability of UAVs for plant growth monitoring and yield estimation in mountainous terrain. A multi-rotor UAV with a multi-spectral camera was used to obtain canopy spectral information of maize in five different stages of the maize plant life cycle. The images taken from the UAV were processed to obtain the result of the orthomosaic and the Digital Surface Model (DSM). The crop yield was estimated using different parameters such as Plant Height, Vegetation Indices, and biomass. A relationship was established in each sub-plot which was further used to calculate the yield of an individual plot. The estimated yield obtained from the model was validated against the ground-measured yield through statistical tests. A comparison of the Normalized Difference Vegetation Index (NDVI) and the Green–Red Vegetation Index (GRVI) indicators of a Sentinel image was performed. GRVI was found to be the most important parameter and NDVI was found to be the least important parameter for yield determination besides their spatial resolution in a hilly region.

Keywords differential global positioning system (DGPS); precision agriculture; digital surface model (DSM); digital terrain model (DTM); green–red vegetation index; leaf area index (LAI); near infrared (NIR); NDVI; receiver independent exchange format (RINEX)
Article Publishing Charge (APC) Amount Paid2400.0
Article Publishing Charge (APC) FundingOther
Contains Sensitive ContentDoes not contain sensitive content
ANZSRC Field of Research 2020401304. Photogrammetry and remote sensing
Byline AffiliationsNepal Open University, Nepal
School of Surveying and Built Environment
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