LiDAR applications in precision agriculture for cultivating crops: A review of recent advances

Rivera, Gilberto; Porras, Raúl; Florencia, Rogelio; Sánchez-Solís, J. Patricia

doi:10.1016/j.compag.2023.107737

Cited by 56 publications

(19 citation statements)

References 95 publications

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“…Recent literature clearly shows that there is lack of specialized software solutions for automatic extraction of plant/plot/canopy features. As showed in systematic review of Rivera et al [ 23 ], most of the published studies applying LiDAR in agriculture reported data processing by some of the generic geoinformatics software with manual point-cloud analysis. In contrast, our software automatically builds CHM and extract plant height for all experimental field-plots.…”

Section: Discussionmentioning

confidence: 99%

Crop growth dynamics: Fast automatic analysis of LiDAR images in field-plot experiments by specialized software ALFA

Fryčák,

Fürst,

Koprna

et al. 2024

PLoS ONE

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Repeated measurements of crop height to observe plant growth dynamics in real field conditions represent a challenging task. Although there are ways to collect data using sensors on UAV systems, proper data processing and analysis are the key to reliable results. As there is need for specialized software solutions for agricultural research and breeding purposes, we present here a fast algorithm ALFA for the processing of UAV LiDAR derived point-clouds to extract the information on crop height at many individual cereal field-plots at multiple time points. Seven scanning flights were performed over 3 blocks of experimental barley field plots between April and June 2021. Resulting point-clouds were processed by the new algorithm ALFA. The software converts point-cloud data into a digital image and extracts the traits of interest–the median crop height at individual field plots. The entire analysis of 144 field plots of dimension 80 x 33 meters measured at 7 time points (approx. 100 million LiDAR points) takes about 3 minutes at a standard PC. The Root Mean Square Deviation of the software-computed crop height from the manual measurement is 5.7 cm. Logistic growth model is fitted to the measured data by means of nonlinear regression. Three different ways of crop-height data visualization are provided by the software to enable further analysis of the variability in growth parameters. We show that the presented software solution is a fast and reliable tool for automatic extraction of plant height from LiDAR images of individual field-plots. We offer this tool freely to the scientific community for non-commercial use.

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Section: Discussionmentioning

confidence: 99%

Crop growth dynamics: Fast automatic analysis of LiDAR images in field-plot experiments by specialized software ALFA

Fryčák,

Fürst,

Koprna

et al. 2024

PLoS ONE

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“…In recent times, LiDAR (Light Detection and Ranging) technology has become a hot topic in remote sensing due to its ability to deliver highly detailed data [1] . There are already studies showing that UAV-based LiDAR can effectively monitor crop changes [2] and provide efficient tracking of biomass and nitrogen uptake [3] .…”

Section: Objectivementioning

confidence: 99%

VineLiDAR: High-resolution UAV-LiDAR vineyard dataset acquired over two years in northern Spain

Vélez,

Ariza-Sentís,

Valente

2023

Data in Brief

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“…As an effective tool and process, plant phenotype is an essential part of modern, intelligent, and precise agricultural production. Various physiological and morphological parameters about plants are acquired by various sensors such as RGB cameras, lidar and multiple and hyperspectral cameras to serve as decision-making basis for real-time and future plant management ( Rivera et al., 2023 ).…”

Section: Plant Phenotypingmentioning

confidence: 99%

Editorial: AI, sensors and robotics in plant phenotyping and precision agriculture, volume II

Qiao

et al. 2023

Front. Plant Sci.

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LiDAR applications in precision agriculture for cultivating crops: A review of recent advances

Cited by 56 publications

References 95 publications

Crop growth dynamics: Fast automatic analysis of LiDAR images in field-plot experiments by specialized software ALFA

Crop growth dynamics: Fast automatic analysis of LiDAR images in field-plot experiments by specialized software ALFA

VineLiDAR: High-resolution UAV-LiDAR vineyard dataset acquired over two years in northern Spain

Editorial: AI, sensors and robotics in plant phenotyping and precision agriculture, volume II

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