Estimation of Rice Plant Height from a Low-Cost UAV- Based Lidar Point Clouds

Phan, Anh Thu Thi; Takahashi, Kazuhiko

doi:10.52939/ijg.v17i2.1765

Cited by 4 publications

(2 citation statements)

References 37 publications

(32 reference statements)

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“…The UAVs discovered that the most significant factors were canopy cover, plant height, and Triangular Greenness Index (TGI), followed by Green Normalized Difference Vegetation (GNDVI) and Normalized Difference Vegetation Index (NDVI), respectively. To increase the accuracy of the plant height estimation, the Light Detection and Ranging (LIDAR) system was also applied [18]. The system is still an expensive technology to date.…”

Section: Discussionmentioning

confidence: 99%

The Application of Unmanned Aerial Vehicles (UAVs) and Extreme Gradient Boosting (XGBoost) to Crop Yield Estimation: A Case Study of Don Tum District, Nakhon Pathom, Thailand

2023

IJG

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Rice (Oryza sativa L.) is a staple food for more than half of the global population. This research, therefore, aims to explore the estimation of crop yields towards the application of unmanned aerial vehicles (UAVs). The research areas are the sample rice fields owned by Sam Ngam Large-Scale Rice Production Community Enterprise in Don Tum District, Nakhon Pathom. The data collected by both RGB and multispectral UAVs was used for estimating the crop yields of Rice Department 41 (RD41), a rice variety, and then analyzed by a geographic information system (GIS). Multiple Linear Regression was applied to factor analysis for the purpose of crop yield estimation based on the factors investigated and obtained by the UAVs. These factors included vegetation indexes (i.e. Normalized Difference Vegetation Index, Green Normalized Difference Vegetation Index, and Triangular Greenness Index), plant height, and canopy coverage. The prediction of the analysis model was proved to be valid (R2 = 0.99; RMSE = 2.506 g.). Extreme Gradient Boosting (XGBoost) was applied to increase the accuracy of the estimation (RMSE = 0.557 g.; MAE = 0.364). The findings of study showed that the utilization of UAVs could contribute to the estimation of crop yield in the research areas.

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Section: Discussionmentioning

confidence: 99%

The Application of Unmanned Aerial Vehicles (UAVs) and Extreme Gradient Boosting (XGBoost) to Crop Yield Estimation: A Case Study of Don Tum District, Nakhon Pathom, Thailand

2023

IJG

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“…Studies have shown that UAV-LiDAR was used to obtain 3D point cloud information of ground objects, which generates a digital elevation model to obtain plant height of crops. This method has been applied to various crops, such as vegetable wheat ( Guo et al, 2019 ), corn ( Zheng et al, 2015 ), rice ( Tilly et al, 2014 ; Phan and Takahashi, 2021 ), soybean ( Luo et al, 2021 ), etc. The above results could better realize the analysis of crop phenotype indicators.…”

Section: Introductionmentioning

confidence: 99%

Identification lodging degree of wheat using point cloud data and convolutional neural network

Yang²,

Zhou³

et al. 2022

Front. Plant Sci.

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Wheat is one of the important food crops, and it is often subjected to different stresses during its growth. Lodging is a common disaster in filling and maturity for wheat, which not only affects the quality of wheat grains, but also causes severe yield reduction. Assessing the degree of wheat lodging is of great significance for yield estimation, wheat harvesting and agricultural insurance claims. In particular, point cloud data extracted from unmanned aerial vehicle (UAV) images have provided technical support for accurately assessing the degree of wheat lodging. However, it is difficult to process point cloud data due to the cluttered distribution, which limits the wide application of point cloud data. Therefore, a classification method of wheat lodging degree based on dimensionality reduction images from point cloud data was proposed. Firstly, 2D images were obtained from the 3D point cloud data of the UAV images of wheat field, which were generated by dimensionality reduction based on Hotelling transform and point cloud interpolation method. Then three convolutional neural network (CNN) models were used to realize the classification of different lodging degrees of wheat, including AlexNet, VGG16, and MobileNetV2. Finally, the self-built wheat lodging dataset was used to evaluate the classification model, aiming to improve the universality and scalability of the lodging discrimination method. The results showed that based on MobileNetV2, the dimensionality reduction image from point cloud obtained by the method proposed in this paper has achieved good results in identifying the lodging degree of wheat. The F1-Score of the classification model was 96.7% for filling, and 94.6% for maturity. In conclusion, the point cloud dimensionality reduction method proposed in this study could meet the accurate identification of wheat lodging degree at the field scale.

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GNSS-IMU-assisted colored ICP for UAV-LiDAR point cloud registration of peach trees

Yuan

Choi

Bolkas

2022

Computers and Electronics in Agriculture

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Estimation of Rice Plant Height from a Low-Cost UAV- Based Lidar Point Clouds

Cited by 4 publications

References 37 publications

The Application of Unmanned Aerial Vehicles (UAVs) and Extreme Gradient Boosting (XGBoost) to Crop Yield Estimation: A Case Study of Don Tum District, Nakhon Pathom, Thailand

The Application of Unmanned Aerial Vehicles (UAVs) and Extreme Gradient Boosting (XGBoost) to Crop Yield Estimation: A Case Study of Don Tum District, Nakhon Pathom, Thailand

Identification lodging degree of wheat using point cloud data and convolutional neural network

GNSS-IMU-assisted colored ICP for UAV-LiDAR point cloud registration of peach trees

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