Estimation of Maize Biomass Using Unmanned Aerial Vehicles

Abstract

Unmanned aerial vehicles (UAVs) are a promising tool for technology development and transfer and for the economic success of the agricultural sector. The objective of this study is to assess the validity of biomass estimation in a commercial maize plantation using aerial images obtained by a UAV. The proposed methodology involved analyzing images acquired in scheduled flights, processing orthophoto (georeferenced image) data, evaluating digital terrain elevation models, and assessing the quality of dense point clouds. Data were collected using two cameras, one with a 16-megapixel flat lens and the other with a 12-megapixel fish-eye lens coupled to a UAV, at two flight altitudes (30 and 60 meters) over hybrid maize (AG1051) crop irrigated by center pivot in the municipality of Limoeiro do Norte, Ceará, Brazil. Crop biomass was estimated in 1 m 2 plots sampled randomly, and data were validated by interpreting aerial images of target areas. The measurements of biomass using UAV-based aerial images were promising. The estimated values were more accurate using the fish-eye lens at 30 m altitude, corresponding to 2.97 kg m -2 , which is very close to the values measured in the field (2.92 kg m -2 ).

Improved estimation of herbaceous crop aboveground biomass using UAV-derived crop height combined with vegetation indices

“…At the same time, advances in remote sensing led to the estimation of other variables more linked to crop structural properties such as crop height (Jimenez-Berni et al, 2018) and canopy volume (Calou et al, 2019), thanks to the development and the diffusion of new sensors such as LiDAR, multispectral imaging sensors for photogrammetry mounted on UAV, 3d reconstruction and ultrasonic sonars. Specifically, crop height is well known to be related to crop biomass within crop species (Madec et al, 2017) and final yield (Bendig et al, 2015).…”

Applications of UAS in Crop Biomass Monitoring: A Review

“…Although RGB sensors have low accuracy compared with other sensors because they can collect spectral data from only three bands, the low-cost characteristic of RGB sensors is not possessed by others. Due to the need for low cost during the large-scale use of UAS in the monitoring of crop biomass, RGB sensors have received increasing attention because of their low-cost characteristics (Calou et al, 2019;Lu et al, 2019;Yue et al, 2019). The combination of RGB data with better biomass indices and advanced algorithms can obtain high accuracy at a low cost (Acorsi et al, 2019;Lu et al, 2019;Yue et al, 2019).…”

“…Second, fisheye lenses may have an advantage over flat lenses. Calou et al (2019) coupled a 16megapixel plane lens with a 12-megapixel fisheye lens on a UAV for data collection, and the results showed that the fisheye lens estimation was the most accurate at an altitude of 30 m. Finally, at present, some applications using spectral data are processed without accurate or rough calibration. Guo et al (2019) proposed a general calibration equation that is suitable for images under clear sky conditions and even under a small amount of clouds.…”

Utilizing Spectral, Structural and Textural Features for Estimating Oat Above-Ground Biomass Using UAV-Based Multispectral Data and Machine Learning

“…Canopy spectral, structural, textural, and thermal features extracted from UAV-based imagery can be used to estimate plant biomass. Spectral features, such as Vegetation Indices (VIs), have been commonly employed to estimate biomass in various crops, including winter wheat [ 20 ], barley [ 31 ], rice [ 32 ], and maize [ 33 ]. However, spectral features can present limitations [ 34 ].…”