Estimating Effective Leaf Area Index of Winter Wheat Based on UAV Point Cloud Data

Yang, Jie; Xing, Minfeng; Tan, Qiyun; Shang, Jiali; Song, Yang; Ni, Xiliang; Wang, Jinfei; Xu, Min

doi:10.3390/drones7050299

Cited by 17 publications

(4 citation statements)

References 47 publications

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“…Most importantly, when combined with high-definition digital cameras, multispectral cameras, and hyperspectral cameras, low-altitude UAV remote sensing can acquire high-resolution texture remote sensing images that exceed those collected using traditional aerial and space-borne remote sensing techniques. As a result, UAV remote sensing technology has been effectively used in monitoring studies of crop phenotypic parameters such as nitrogen content [11], leaf chlorophyll content [9,12,13], above-ground biomass [6][7][8], the leaf area index [14,15], irrigated areas [16], and more. Presently, existing research on the extraction of crop maturity information from remote sensing data primarily falls into three categories: (1) time series vegetation index analysis [17,18], (2) crop model and data assimilation [19,20], and (3) machine learning methods [12,21,22].…”

Section: Introductionmentioning

confidence: 99%

Mapping Soybean Maturity and Biochemical Traits Using UAV-Based Hyperspectral Images

Wang,

Gao,

et al. 2023

Remote Sensing

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Soybeans are rich in high-quality protein and raw materials for producing hundreds of chemical products. Consequently, soybean cultivation has gained widespread prevalence across diverse geographic regions. Soybean breeding necessitates the development of early-, standard-, and late-maturing cultivars to accommodate cultivation at various latitudes, thereby optimizing the utilization of solar radiation. In the practical process of determining the maturity of soybean breeding materials within the breeding field, the ripeness is assessed based on three critical criteria: pod moisture content, leaf color, and the degree of leaf shedding. These parameters reflect the crown structure, physicochemical parameters, and reproductive organ changes in soybeans during the maturation process. Therefore, methods for analyzing soybean maturity at the breeding plot scale should match the standards of agricultural experts to the maximum possible extent. This study presents a hyperspectral remote sensing approach for monitoring soybean maturity. We collected five periods of unmanned aerial vehicle (UAV)-based soybean canopy hyperspectral digital orthophoto maps (DOMs) and ground-level measurements of leaf chlorophyll content (LCC), flavonoids (Flav), and the nitrogen balance index (NBI) from a breeding farm. This study explores the following aspects: (1) the correlations between soybean LCC, NBI, Flav, and maturity; (2) the estimation of soybean LCC, NBI, and Flav using Gaussian process regression (GPR), partial least squares regression (PLSR), and random forest (RF) regression techniques; and (3) the application of threshold-based methods in conjunction with normalized difference vegetation index (NDVI)+LCC and NDVI+NBI for soybean maturity monitoring. The results of this study indicate the following: (1) Soybean LCC, NBI, and Flav are associated with maturity. LCC increases during the beginning bloom period (P1) to the beginning seed period (P3) and sharply decreases during the beginning maturity period (P4) stage. Flav continues to increase from P1 to P4. NBI remains relatively consistent from P1 to P3 and then drops rapidly during the P4 stage. (2) The GPR, PLSR, and RF methodologies yield comparable accuracy in estimating soybean LCC (coefficient of determination (R2): 0.737–0.832, root mean square error (RMSE): 3.35–4.202 Dualex readings), Flav (R2: 0.321–0.461, RMSE: 0.13–0.145 Dualex readings), and NBI (R2: 0.758–0.797, RMSE: 2.922–3.229 Dualex readings). (3) The combination of the threshold method with NDVI < 0.55 and NBI < 8.2 achieves the highest classification accuracy (accuracy = 0.934). Further experiments should explore the relationships between crop NDVI, the Chlorophyll Index, LCC, Flav, and NBI and crop maturity for different crops and ecological areas.

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Section: Introductionmentioning

confidence: 99%

Mapping Soybean Maturity and Biochemical Traits Using UAV-Based Hyperspectral Images

Wang,

Gao,

et al. 2023

Remote Sensing

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“…[16,17]. In recent years, with the rapid development of the UAV industry, remote sensing of the UAV clothing industry has played an important role in the application of crop disease and pest stress monitoring on account of its characteristics of high spatial resolution of image, high timeliness of data acquisition and low cost [15,18]. Therefore, UAV hyperspectral photogrammetry is an effective method for rapid and accurate monitoring of small and medium-sized crop pests and diseases.…”

Section: Introductionmentioning

confidence: 99%

Rice False Smut Monitoring Based on Band Selection of UAV Hyperspectral Data

et al. 2023

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Rice false smut (RFS) is a late-onset fungal disease that primarily affects rice panicle in recent years. Severe RFS can decrease the yield by 20–30% and severely affect rice quality. This research used hyperspectral remote sensing data from unmanned aerial vehicles (UAV). On the basis of genetic algorithm combined with partial least squares to select the feature bands, this paper creates a new method to use the Pearson correlation coefficient method and Instability Index between Classes (ISIC) method to further select characteristic bands, which further eliminated 27.78% of the feature bands when the model monitoring accuracy was improved overall. The prediction accuracy of the Gradient Boosting Decision Tree model and Random Forest model was the best, which were 85.62% and 84.10%, respectively, and the monitoring accuracy was improved by 2.22% and 2.4% compared with that before optimization. Then, based on the UAV hyperspectral data and the combination of characteristic bands selected by the three band optimization methods, the sensitive band ranges of rice false smut monitoring were determined, which were 698–800 nm and 974–997 nm. This paper provides an effective method of selecting characteristic bands of hyperspectral data and a method of monitoring crop diseases’ using unmanned aerial vehicles.

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“…Point cloud data can provide very high measurement accuracy, which can avoid human error and measurement errors and improve the reliability and accuracy of data compared to traditional measurement methods [27,33,34]. Yang et al [35] proposed a 3-D point cloud method using UAV to automatically calculate crop-effective LAI (LAIe). The method accurately estimated LAIe by projecting 3-D point cloud data of winter wheat onto the hemisphere and calculating the gap fraction using both single-angle inversion and multi-angle inversion methods.…”

Section: Introductionmentioning

confidence: 99%

Inversion of Leaf Area Index in Citrus Trees Based on Multi-Modal Data Fusion from UAV Platform

Xiao

et al. 2023

Remote Sensing

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The leaf area index (LAI) is an important growth indicator used to assess the health status and growth of citrus trees. Although LAI estimation based on unmanned aerial vehicle (UAV) platforms has been widely used for field crops, mainly focusing on food crops, less research has been reported on the application to fruit trees, especially citrus trees. In addition, most studies have used single-modal data for modeling, but some studies have shown that multi-modal data can be effective in improving experimental results. This study utilizes data collected from a UAV platform, including RGB images and point cloud data, to construct single-modal regression models named VoVNet (using RGB data) and PCNet (using point cloud data), as well as a multi-modal regression model called VPNet (using both RGB data and point cloud data). The LAI of citrus trees was estimated using deep neural networks, and the results of two experimental hyperparameters (loss function and learning rate) were compared under different parameters. The results of the study showed that VoVNet had Mean Squared Error (MSE), Mean Absolute Error (MAE), and R-Squared (R2) of 0.129, 0.028, and 0.647, respectively. In comparison, PCNet decreased by 0.051 and 0.014 to 0.078 and 0.014 for MAE and MSE, respectively, while R2 increased by 0.168 to 0.815. VPNet decreased by 0% and 42.9% relative to PCNet in terms of MAE and MSE to 0.078 and 0.008, respectively, while R2 increased by 5.6% to 0.861. In addition, the use of loss function L1 gave better results than L2, while a lower learning rate gave better results. It is concluded that the fusion of RGB data and point cloud data collected by the UAV platform for LAI estimation is capable of monitoring citrus trees’ growth process, which can help farmers to track the growth condition of citrus trees and improve the efficiency and quality of orchard management.

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Estimating Effective Leaf Area Index of Winter Wheat Based on UAV Point Cloud Data

Cited by 17 publications

References 47 publications

Mapping Soybean Maturity and Biochemical Traits Using UAV-Based Hyperspectral Images

Mapping Soybean Maturity and Biochemical Traits Using UAV-Based Hyperspectral Images

Rice False Smut Monitoring Based on Band Selection of UAV Hyperspectral Data

Inversion of Leaf Area Index in Citrus Trees Based on Multi-Modal Data Fusion from UAV Platform

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