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 m × 10 m linearly correlates with photo-interpreted linear gap percentage with a coefficient of determination (R2)= 0.9; a root mean square error (RMSE) = 5.04; and probability (p)

Plant phenotyping relevance

UAV画像とデジタルモザイク処理によりサトウキビの植栽ギャップという植物群落状態を抽出・地図化し、精度と既存評価法との相関を検証しているため、手法が中心です。

Code and data availability

The article describes UAV imagery acquisition, photo-interpreted reference data, and custom R scripts for sugarcane gap analysis, but no public deposit of the imagery, digitized shapefiles, training data, or author R scripts is mentioned. All URLs cited are generic software/library or climate-data references, not paper

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