Developing Site-Specific Prescription Maps for Sugarcane Weed Control Using High-Spatial-Resolution Images and Light Detection and Ranging (LiDAR)

Kerin F. Romero and Muditha K. Heenkenda ()
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Kerin F. Romero: Department of Agricultural Engineering, Instituto Tecnológico de Costa Rica, Cartago 30101, Costa Rica
Muditha K. Heenkenda: Department of Geography and the Environment, Lakehead University, Thunder Bay, ON P7B 5E1, Canada

Land, 2024, vol. 13, issue 11, 1-14

Abstract: Sugarcane is a perennial grass species mainly for sugar production and one of the significant crops in Costa Rica, where ideal growing conditions support its cultivation. Weed control is a critical aspect of sugarcane farming, traditionally managed through preventive or corrective mechanical and chemical methods. However, these methods can be time-consuming and costly. This study aimed to develop site-specific, variable rate prescription maps for weed control using remote sensing. High-spatial-resolution images (5 cm) and Light Detection And Ranging (LiDAR) were acquired using a Micasense Rededge-P camera and a DJI L1 sensor mounted on a drone. Precise locations of weeds were collected for calibration and validation. Normalized Difference Vegetation Index derived from multispectral images separated vegetation coverage and soil. A deep learning (DL) algorithm further classified vegetation coverage into sugarcane and weeds. The DL model performed well without overfitting. The classification accuracy was 87% compared to validation samples. The density and average heights of weed patches were extracted from the canopy height model (LiDAR). They were used to derive site-specific prescription maps for weed control. This efficient and precise alternative to traditional methods could optimize weed control, reduce herbicide usage and provide more profitable yield.

Keywords: sugarcane; variable rate application; deep learning; LiDAR; canopy height model; Micasense RedEdge-P camera (search for similar items in EconPapers)
JEL-codes: Q15 Q2 Q24 Q28 Q5 R14 R52 (search for similar items in EconPapers)
Date: 2024
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