Mapping Crop Planting Quality in Sugarcane from UAV Imagery: A Pilot Study in Nicaragua

Abstract

Sugarcane is an important economic resource for many tropical countries and optimizing plantations is a serious concern with economic and environmental benefits. One of the best ways to optimize the use of resources in those plantations is to minimize the occurrence of gaps. Typically, gaps open in the crop canopy because of damaged rhizomes, unsuccessful sprouting or death young stalks. In order to avoid severe yield decrease, farmers need to fill the gaps with new plants. Mapping gap density is therefore critical to evaluate crop planting quality and guide replanting. Current field practices of linear gap evaluation are very labor intensive and cannot be performed with sufficient intensity as to provide detailed spatial information for mapping, which makes replanting difficult to perform. Others have used sensors carried by land vehicles to detect gaps, but these are complex and require circulating over the entire area. We present a method based on processing digital mosaics of conventional images acquired from a small Unmanned Aerial Vehicle (UAV) that produced a map of gaps at 23.5 cm resolution in a study area of 8.7 ha with 92.9% overall accuracy. Linear Gap percentage estimated from this map for a grid with cells of 10 mˆ10 m linearly correlates with photo-interpreted linear gap percentage with a coefficient of determination (R 2 )= 0.9; a root mean square error (RMSE) = 5.04; and probability (p) << 0.01. Crop Planting Quality levels calculated from image-derived gaps agree with those calculated from a photo-interpreted version of currently used field methods (Spearman coefficient = 0.92). These results clearly demonstrate the effectiveness of processing mosaics of Unmanned Aerial System (UAS) images for mapping gap density and, together with previous studies using satellite and hand-held spectroradiometry, suggests the extension towards multi-spectral imagery to add insight on plant condition.

Evaluation of RGB, Color-Infrared and Multispectral Images Acquired from Unmanned Aerial Systems for the Estimation of Nitrogen Accumulation in Rice

“…UAS have proven to be an ideal technique in precision agriculture for crop growth mapping [60,61] and monitoring [13,14,33]. Simultaneously, many aspects of sensor selection should be taken into consideration, such as band configuration and price.…”

Assessing Safety and Suitability of Old Trails for Hiking Using Ground and Drone Surveys

“…When compared to our proposed method, the use of helicopter, both film and digital camera with GPS unit, could be replaced by a drone. The usefulness of drones being implemented in different fields of study is promising, especially in aspects such as agriculture and forestry [31][32][33]. A drone-based technique for topographic analysis provides better accuracy when compared to satellite-based and aircraft-based remote sensing techniques [34][35][36].…”

Application of unmanned aerial vehicles in earth resources monitoring: focus on evaluating potentials for forest monitoring in Ethiopia

“…For example, small UAS were used to detect symptoms of disease and pest attacks on oil-bearing trees in Greece (Psirofonia, Samaritakis, Eliopoulos, & Potamitis, 2017). Planting patterns in sugarcane were supervised using UAS imagery in Nicaragua, Central America (Luna & Lobo, 2016). Promising results were obtained indicating huge potential of the technology in precision agriculture in the future (Felderhof, Gillieson, Zadro, & Van Boven, 2008).…”