Correlating the Plant Height of Wheat with Above-Ground Biomass and Crop Yield Using Drone Imagery and Crop Surface Model, A Case Study from Nepal

Abstract

<jats:p>Food security is one of the burning issues in the 21st century, as a tremendous population growth over recent decades has increased demand for food production systems. However, agricultural production is constrained by the limited availability of arable land resources, whereas a significant part of these is already degraded due to overexploitation. In order to get optimum output from the available land resources, it is of prime importance that crops are monitored, analyzed, and mapped at various stages of growth so that the areas having underdeveloped/unhealthy plants can be treated appropriately as and when required. This type of monitoring can be performed using ultra-high-resolution earth observation data like the images captured through unmanned aerial vehicles (UAVs)/drones. The objective of this research is to estimate and analyze the above-ground biomass (AGB) of the wheat crop using a consumer-grade red-green-blue (RGB) camera mounted on a drone. AGB and yield of wheat were estimated from linear regression models involving plant height obtained from crop surface models (CSMs) derived from the images captured by the drone-mounted camera. This study estimated plant height in an integrated setting of UAV-derived images with a Mid-Western Terai topographic setting (67 to 300 m amsl) of Nepal. Plant height estimated from the drone images had an error of 5% to 11.9% with respect to direct field measurement. While R2 of 0.66 was found for AGB, that of 0.73 and 0.70 were found for spike and grain weights respectively. This statistical quality assurance contributes to crop yield estimation, and hence to develop efficient food security strategies using earth observation and geo-information.</jats:p>

A Review on Drone-Based Data Solutions for Cereal Crops

“…Like crop monitoring, plant height, RGB-based color indices as well as NIR/Red-Edge-based vegetation indices have been used to estimate crop biomass. Canopy height obtained from RGB images is used in many studies [17,133,137]. The combination of vegetation indices and plant height derived from drone RGB imagery has shown a high potential for maize biomass estimation [66].…”

“…Yield estimation of cereals using plant height [137,138], CIs/VIs [18,65,75,81,82,132,138] derived from RGB, multispectral and/or hyperspectral sensors are available in the literature. An image processing method combined with the K-means clustering algorithm with a graph-cut (KCG) algorithm on RGB images collected using a multi-rotor drone is utilized to estimate rice yield [139].…”

“…have developed over the years, and are relatively new for agricultural applications. Several studies have used a linear regression model to correlate biomass and/or crop yield with crop parameters measured using remote sensing techniques [18,66,74,81,137,[143][144][145]. The use of multiple variables (multiple linear regression) and multitemporal data are also reported in the literature [66,79,83,132].…”

“…Henceforth, the drone-based system is scalable with a cooperative farming system. In one of our research, plant height measurements were performed by farmers' cooperative members [137]. Thus, the farmers' cooperatives can appropriately employ the drones and operate it themselves as well, if proper training is prearranged.…”

Portable LiDAR-Based Method for Improvement of Grass Height Measurement Accuracy: Comparison with SfM Methods

“…For example, Cooper et al [ 1 ] carried out digital camera photography from eye height level, on 11 grass plots in South Dakota, USA, and showed that biomass could be estimated from the volume of the grass material reconstructed by SfM. Researchers have also used this method to determine tree height [ 2 , 3 ], wheat height [ 4 ], pasture height [ 5 , 6 , 7 , 8 ], and reconstruct three-dimensional structures of legumes and carrots [ 9 ]. Vegetation classification can also be performed, using aerial images captured by an unmanned aerial vehicle (UAV) [ 10 , 11 ].…”