Estimation of Vegetable Crop Parameter by Multi-temporal UAV-Borne Images

Abstract

3D point cloud analysis of imagery collected by unmanned aerial vehicles (UAV) has been shown to be a valuable tool for estimation of crop phenotypic traits, such as plant height, in several species. Spatial information about these phenotypic traits can be used to derive information about other important crop characteristics, like fresh biomass yield, which could not be derived directly from the point clouds. Previous approaches have often only considered single date measurements using a single point cloud derived metric for the respective trait. Furthermore, most of the studies focused on plant species with a homogenous canopy surface. The aim of this study was to assess the applicability of UAV imagery for capturing crop height information of three vegetables (crops eggplant, tomato, and cabbage) with a complex vegetation canopy surface during a complete crop growth cycle to infer biomass. Additionally, the effect of crop development stage on the relationship between estimated crop height and field measured crop height was examined. Our study was conducted in an experimental layout at the University of Agricultural Science in Bengaluru, India. For all the crops, the crop height and the biomass was measured at five dates during one crop growth cycle between February and May 2017 (average crop height was 42.5, 35.5, and 16.0 cm for eggplant, tomato, and cabbage). Using a structure from motion approach, a 3D point cloud was created for each crop and sampling date. In total, 14 crop height metrics were extracted from the point clouds. Machine learning methods were used to create prediction models for vegetable crop height. The study demonstrates that the monitoring of crop height using an UAV during an entire growing period results in detailed and precise estimates of crop height and biomass for all three crops (R 2 ranging from 0.87 to 0.97, bias ranging from −0.66 to 0.45 cm). The effect of crop development stage on the predicted crop height was found to be substantial (e.g., median deviation increased from 1% to 20% for eggplant) influencing the strength and consistency of the relationship between point cloud metrics and crop height estimates and, thus, should be further investigated. Altogether the results of the study demonstrate that point cloud generated from UAV-based RGB imagery can be used to effectively measure vegetable crop biomass in larger areas (relative error = 17.6%, 19.7%, and 15.2% for eggplant, tomato, and cabbage, respectively) with a similar accuracy as biomass prediction models based on measured crop height (relative error = 21.6, 18.8, and 15.2 for eggplant, tomato, and cabbage).

Multi-Temporal Monsoon Crop Biomass Estimation Using Hyperspectral Imaging

“…The total rainfall and mean temperature data of the monsoon cropping season varied from 2016 to 2018 (Table 1) [25]. [11]).…”

“…Timely fertiliser application with water supply is essential for a successful crop. Spectral data from Remote Sensing (RS) have been studied for many years for an adequate assessment of nutrient and water variability for yield optimisation [4][5][6] RS can be an effective tool in monitoring crop production [7-9] and estimating yield [10,11]. Early estimation of yield may allow better planning and forecasting the market prices and support food security based on the regional, national and global demand and supply.…”

Biomass Prediction of Heterogeneous Temperate Grasslands Using an SfM Approach Based on UAV Imaging

“…Forsmoo et al [14] calculated in a grassland sward, that for a plot of 8 m 2 a CH assessment with a ruler needed 550 single height measurements to receive the same accuracy as a SfM-based CH assessment based on UAV RGB imaging. Therefore, the high spatial resolution makes UAV RGB imagery in combination with an SfM approach an interesting tool for yield estimation in practical grassland farming.SfM derived height measurement based on UAV RGB imaging was successfully used in forestry [15], and, to a lower degree also in agricultural crops, such as wheat [6,16], barley [17], maize [18,19] and vegetable crops [20]. All these studies found strong relationships between biomass and RGB imaging in homogeneous crops.…”

“…SfM derived height measurement based on UAV RGB imaging was successfully used in forestry [15], and, to a lower degree also in agricultural crops, such as wheat [6,16], barley [17], maize [18,19] and vegetable crops [20]. All these studies found strong relationships between biomass and RGB imaging in homogeneous crops.…”

Intra-Season Crop Height Variability at Commercial Farm Scales Using a Fixed-Wing UAV

“…Bendig et al [60] estimated barley biomass using dynamic crop height data retrieved from a UAV, while Schirrman et al [61] evaluated the relationship among multiple biophysical parameters in wheat (i.e., multi-temporal crop height, LAI, nitrogen status and biomass). More recently, Moeckel et al [62] used the UAV-SfM approach to monitor vegetable crops (i.e., eggplants, tomatoes and cabbage). Despite the successful application of RGB imagery in UAV-based SfM approaches, all of the cited multi-temporal studies have used rotary UAVs, which have tended to limit the crop monitoring to relatively small field scales (i.e., they did not exceed 11 ha [61]).…”