Spatial variability in commercial orange groves. Part 1: canopy volume and height

Colaço, André Freitas; Molin, José Paulo; Rosell-Polo, Joan R.; Escolà, Alexandre

doi:10.1007/s11119-018-9612-3

Cited by 14 publications

(6 citation statements)

References 35 publications

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“…The plant height was spatially correlated for longer distances than the plant canopy temperature for both years (see range parameters in Table 3). Maps derived from kriging indicated that the plot could be divided into zones, which was consistent with other researchers using on-the-go sensors to measure plant canopy height and volume [21]. The differences in plant height observed on the maps were verified during ground-truthing of the field, thus justifying accuracy and potential use of the system as a survey tool.…”

Section: Discussionsupporting

confidence: 87%

Spatial Analysis of Soybean Plant Height and Plant Canopy Temperature Measured with On-the-Go Tractor Mounted Sensors

Fletcher¹,

Fisher²

2019

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There is a growing interest in the Open Ag community to use inexpensive sensors controlled by open-source software to measure plant height and plant canopy temperature of agricultural crops. Plant height and plant canopy temperature are key indicators of plant health. This research study reports on an ongoing research initiative to test a compact and inexpensive mobile sensor to measure plant height and plant canopy temperature. The system is controlled by open source software and hardware. The specific objectives for this study were to analyze the relationship between plant height and plant canopy temperature of soybeans (Glycine max L.

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Section: Discussionsupporting

confidence: 87%

Spatial Analysis of Soybean Plant Height and Plant Canopy Temperature Measured with On-the-Go Tractor Mounted Sensors

Fletcher¹,

Fisher²

2019

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“…Alternatives to the field inspections aimed at verifying the proper completion of these tasks have arisen to overcome this problem in the last years. For example, accurate three-dimensional (3D) modeling and characterization of crop canopies have been achieved using different technologies, such as aerial photogrammetry by means of unmanned aerial vehicles (UAVs) acquired images [4,5], and also LIDAR (light detection and ranging) [6,7], ultrasonic sensors [8,9], or depth cameras [10,11] for mapping 3D structure in different crops. In an economic study, the use of photogrammetric techniques applied in UAV-images has been reported as the most efficient method to geometrically and accurately characterize a vineyard when compared to LIDAR and depth-camera sensors on board on-ground vehicles [12].…”

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confidence: 99%

Monitoring Vineyard Canopy Management Operations Using UAV-Acquired Photogrammetric Point Clouds

et al. 2020

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Canopy management operations, such as shoot thinning, leaf removal, and shoot trimming, are among the most relevant agricultural practices in viticulture. However, the supervision of these tasks demands a visual inspection of the whole vineyard, which is time-consuming and laborious. The application of photogrammetric techniques to images acquired with an Unmanned Aerial Vehicle (UAV) has proved to be an efficient way to measure woody crops canopy. Consequently, the objective of this work was to determine whether the use of UAV photogrammetry allows the detection of canopy management operations. A UAV equipped with an RGB digital camera was used to acquire images with high overlap over different canopy management experiments in four vineyards with the aim of characterizing vine dimensions before and after shoot thinning, leaf removal, and shoot trimming operations. The images were processed to generate photogrammetric point clouds of every vine that were analyzed using a fully automated object-based image analysis algorithm. Two approaches were tested in the analysis of the UAV derived data: 1) to determine whether the comparison of the vine dimensions before and after the treatments allowed the detection of the canopy management operations; and 2) to study the vine dimensions after the operations and assess the possibility of detecting these operations using only the data from the flight after them. The first approach successfully detected the canopy management. Regarding the second approach, significant differences in the vine dimensions after the treatments were detected in all the experiments, and the vines under the shoot trimming treatment could be easily and accurately detected based on a fixed threshold.

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“…By comparing the differences in canopy height, coverage and canopy volume, it was proved that reducing farming could improve crop geometric parameters. Colaço et al [29] obtained the canopy height and volume of the orange garden through point cloud data, proving the spatial variability of the canopy geometry. Among them, the coefficient of variation of the canopy volume was 30% to 40%, and the information can be used in agricultural machinery variable assignments.…”

Section: Introductionmentioning

confidence: 98%

Analysis of cotton height spatial variability based on UAV-LiDAR

Liu

Dong

Qiu

2018

International Journal of Precision Agricultural Aviation

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The spatial variance of geometric information of farmland crops is the basis of field management. Therefore, it has significance for variable mechanical operations to accurately obtain the spatial difference of crop height information. In the present study, UAV-LiDAR was used to collect data at the cotton planting base in Korla to estimate the spatial differences in cotton plant height. The crop height was estimated using the average height of a certain number of highest points per m 2 point cloud. First, the plant heights of different spatial locations in the field were collected manually and compared with the system measurement. The results showed that the maximum relative error of sampling was 12.73%, the error value was 3.48 cm, and the height map was visualized. In order to explain the height change of plant height in the direction of crop rows and vertical crop rows, this paper used the coefficient of variation as a measure. The results showed that the plant height variation coefficient in the crop row direction ranged from 0.54-1.04 and the average variation coefficient was 0.73; perpendicular to the crop row direction, the crop height variation coefficient range was 0.06-1.27 and the average variation coefficient was 0.58. The spatial difference information was characterized by the coefficient of variation of the geometrical features of the crop height. This work can provide information for cotton field variable operation machinery and provide reference for the extraction of field crop geometric information.

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Spatial variability in commercial orange groves. Part 1: canopy volume and height

Cited by 14 publications

References 35 publications

Spatial Analysis of Soybean Plant Height and Plant Canopy Temperature Measured with On-the-Go Tractor Mounted Sensors

Spatial Analysis of Soybean Plant Height and Plant Canopy Temperature Measured with On-the-Go Tractor Mounted Sensors

Monitoring Vineyard Canopy Management Operations Using UAV-Acquired Photogrammetric Point Clouds

Analysis of cotton height spatial variability based on UAV-LiDAR

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