Measurement and evaluation method of farmland microtopography feature information based on 3D LiDAR and inertial measurement unit

Liu, Guoyang; Xia, Junfang; Zheng, Kan; Cheng, Jian; Wang, Kaixuan; Liu, Zhengyuan; Wei, Youshuai; Xie, Dingyang

doi:10.1016/j.still.2023.105921

Cited by 4 publications

(4 citation statements)

References 42 publications

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“…The WF analysis showed that the LiDAR diffused backscatter from soybean and the underlying ground was challenging to differentiate between the ground and plant (see Figure 10f). To deal with diffused backscatter, bare-ground WFs (see Figure 10c) can be used as a valid reference to measure crop heights when the microtopography of the underlying crop surface is not important [93,94]. Our investigation showed that the CHM analysis has proven useful in deciphering the multi-temporal and spatiotemporal changes in crop height (Figure 6), however, an in-depth 3D crop vertical stratification is limited using the traditional CHM approaches.…”

Section: Discussionmentioning

confidence: 87%

“…Ground points are then used for derivative products, e.g., DEM/DTM, DSM, and CHM. Inaccurate ground point classification highly likely contributes towards the under-/over-estimation of CHM heights when some of the crop points are classified as ground points or ground points are classified as crop points [25,47,49,93]. Furthermore, a wide variety of ground point classification algorithms have been developed; therefore, the choice and algorithm parameter optimization is a challenging task for ULS high-density point cloud classification [25].…”

Section: Discussionmentioning

confidence: 99%

“…This trend is true for the rest of the crop types (Figure 10a-f) including the ground returns from the ground surface (Figure 10c). Higher ground returns from underlying crops are useful to generate high-accuracy DEM models [93]. In the complex crop environment, DEM accuracy becomes important to generate accurate CHM (Figure 6).…”

Section: Discussionmentioning

confidence: 99%

See 2 more Smart Citations

UAS Quality Control and Crop Three-Dimensional Characterization Framework Using Multi-Temporal LiDAR Data

Fareed,

Das,

Flores

et al. 2024

Remote Sensing

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Information on a crop’s three-dimensional (3D) structure is important for plant phenotyping and precision agriculture (PA). Currently, light detection and ranging (LiDAR) has been proven to be the most effective tool for crop 3D characterization in constrained, e.g., indoor environments, using terrestrial laser scanners (TLSs). In recent years, affordable laser scanners onboard unmanned aerial systems (UASs) have been available for commercial applications. UAS laser scanners (ULSs) have recently been introduced, and their operational procedures are not well investigated particularly in an agricultural context for multi-temporal point clouds. To acquire seamless quality point clouds, ULS operational parameter assessment, e.g., flight altitude, pulse repetition rate (PRR), and the number of return laser echoes, becomes a non-trivial concern. This article therefore aims to investigate DJI Zenmuse L1 operational practices in an agricultural context using traditional point density, and multi-temporal canopy height modeling (CHM) techniques, in comparison with more advanced simulated full waveform (WF) analysis. Several pre-designed ULS flights were conducted over an experimental research site in Fargo, North Dakota, USA, on three dates. The flight altitudes varied from 50 m to 60 m above ground level (AGL) along with scanning modes, e.g., repetitive/non-repetitive, frequency modes 160/250 kHz, return echo modes (1n), (2n), and (3n), were assessed over diverse crop environments, e.g., dry corn, green corn, sunflower, soybean, and sugar beet, near to harvest yet with changing phenological stages. Our results showed that the return echo mode (2n) captures the canopy height better than the (1n) and (3n) modes, whereas (1n) provides the highest canopy penetration at 250 kHz compared with 160 kHz. Overall, the multi-temporal CHM heights were well correlated with the in situ height measurements with an R2 (0.99–1.00) and root mean square error (RMSE) of (0.04–0.09) m. Among all the crops, the multi-temporal CHM of the soybeans showed the lowest height correlation with the R2 (0.59–0.75) and RMSE (0.05–0.07) m. We showed that the weaker height correlation for the soybeans occurred due to the selective height underestimation of short crops influenced by crop phonologies. The results explained that the return echo mode, PRR, flight altitude, and multi-temporal CHM analysis were unable to completely decipher the ULS operational practices and phenological impact on acquired point clouds. For the first time in an agricultural context, we investigated and showed that crop phenology has a meaningful impact on acquired multi-temporal ULS point clouds compared with ULS operational practices revealed by WF analyses. Nonetheless, the present study established a state-of-the-art benchmark framework for ULS operational parameter optimization and 3D crop characterization using ULS multi-temporal simulated WF datasets.

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Section: Discussionmentioning

confidence: 87%

Section: Discussionmentioning

confidence: 99%

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UAS Quality Control and Crop Three-Dimensional Characterization Framework Using Multi-Temporal LiDAR Data

Fareed,

Das,

Flores

et al. 2024

Remote Sensing

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“…Laser technology is widely applied in agriculture due to its advantages, fast propagation speed, good concentration, and high energy density, such as for crop growth monitoring [43], farmland measurement [44], and the navigation and autopilot of agricultural vehicles [45]. Laser technology has also been applied to weed-killing treatments, whereby plants are precisely irradiated by high-energy laser beams to destroy plant cells and achieve complete weed eradication.…”

Section: Introductionmentioning

confidence: 99%

YOLOv7-Based Intelligent Weed Detection and Laser Weeding System Research: Targeting Veronica didyma in Winter Rapeseed Fields

Qin,

Xu,

Wang

et al. 2024

Agriculture

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In recent years, rapeseed oil has received considerable attention in the agricultural sector, experiencing appreciable growth. However, weed-related challenges are hindering the expansion of rapeseed production. This paper outlines the development of an intelligent weed detection and laser weeding system—a non-chemical and precision agricultural protection method of weeding Veronica didyma in winter rapeseed fields in the Yangtze River Basin. A total of 234 Veronica didyma images were obtained to compile a database for a deep-learning model, and YOLOv7 was used as the detection model for training. The effectiveness of the model was demonstrated, with a final accuracy of 94.94%, a recall of 95.65%, and a mAP@0.5 of 0.972 obtained. Subsequently, parallel-axis binocular cameras were selected as the image acquisition platform, with binocular calibration and semi-global block matching used to locate Veronica didyma within a cultivation box, yielding a minimum confidence and camera height values of 70% and 30 cm, respectively. The intelligent weed detection and laser weeding system was then built, and the experimental results indicated that laser weeding was practicable with a 100 W power and an 80 mm/s scanning speed, resulting in visibly lost activity in Veronica didyma and no resprouting within 15 days of weeding. The successful execution of Veronica didyma detection and laser weeding provides a new reference for the precision agricultural protection of rapeseed in winter and holds promise for its practical application in agricultural settings.

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Research on an intelligent vibration detachment system for rotary tiller based on soil surface roughness dynamic characteristics

Liu,

Zheng,

Xia

et al. 2024

Computers and Electronics in Agriculture

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Measurement and evaluation method of farmland microtopography feature information based on 3D LiDAR and inertial measurement unit

Cited by 4 publications

References 42 publications

UAS Quality Control and Crop Three-Dimensional Characterization Framework Using Multi-Temporal LiDAR Data

UAS Quality Control and Crop Three-Dimensional Characterization Framework Using Multi-Temporal LiDAR Data

YOLOv7-Based Intelligent Weed Detection and Laser Weeding System Research: Targeting Veronica didyma in Winter Rapeseed Fields

Research on an intelligent vibration detachment system for rotary tiller based on soil surface roughness dynamic characteristics

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