Evaluating soil moisture content under maize coverage using UAV multimodal data by machine learning algorithms

Han, Wenting; Zhang, Huihui; Niu, Xiaotao; Shao, Guomin

doi:10.1016/j.jhydrol.2023.129086

Cited by 31 publications

(6 citation statements)

References 52 publications

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“…Empirical calibration models were constructed using all data from the in-situ canopy experiments. These data were divided into training set and validation set by random sampling method ( Shao et al, 2022 ; Shao et al, 2023 ; Zhang et al, 2023 ). Taking the four-year data as a whole when dividing these sets can enrich the year-differences of data set, and this method of data processing can improve the adaptability of the model to different years and make the model more robust.…”

Section: Results and Analysismentioning

confidence: 99%

Calibrating ultrasonic sensor measurements of crop canopy heights: a case study of maize and wheat

Zheng,

Hui,

Cai

et al. 2024

Front. Plant Sci.

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Canopy height serves as an important dynamic indicator of crop growth in the decision-making process of field management. Compared with other commonly used canopy height measurement techniques, ultrasonic sensors are inexpensive and can be exposed in fields for long periods of time to obtain easy-to-process data. However, the acoustic wave characteristics and crop canopy structure affect the measurement accuracy. To improve the ultrasonic sensor measurement accuracy, a four-year (2018−2021) field experiment was conducted on maize and wheat, and a measurement platform was developed. A series of single-factor experiments were conducted to investigate the significant factors affecting measurements, including the observation angle (0−60°), observation height (0.5−2.5 m), observation period (8:00−18:00), platform moving speed with respect to the crop (0−2.0 m min−1), planting density (0.2−1 time of standard planting density), and growth stage (maize from three−leaf to harvest period and wheat from regreening to maturity period). The results indicated that both the observation angle and planting density significantly affected the results of ultrasonic measurements (p-value< 0.05), whereas the effects of other factors on measurement accuracy were negligible (p-value > 0.05). Moreover, a double-input factor calibration model was constructed to assess canopy height under different years by utilizing the normalized difference vegetation index and ultrasonic measurements. The model was developed by employing the least-squares method, and ultrasonic measurement accuracy was significantly improved when integrating the measured value of canopy heights and the normalized difference vegetation index (NDVI). The maize measurement accuracy had a root mean squared error (RMSE) ranging from 81.4 mm to 93.6 mm, while the wheat measurement accuracy had an RMSE from 37.1 mm to 47.2 mm. The research results effectively combine stable and low-cost commercial sensors with ground-based agricultural machinery platforms, enabling efficient and non-destructive acquisition of crop height information.

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Section: Results and Analysismentioning

confidence: 99%

Calibrating ultrasonic sensor measurements of crop canopy heights: a case study of maize and wheat

Zheng,

Hui,

Cai

et al. 2024

Front. Plant Sci.

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“…Using multi-modal data can combine this information to provide more comprehensive data, which in turn can improve the accuracy and precision of prediction results. In fact, prediction results using multi-modal data are more accurate than single data sources [41,[51][52][53]. According to the experimental data, the results of using multimodal data for estimation showed that compared to the estimation results of RGB data and point cloud data, the MAE was reduced by 0.024 and 0.016, the MSE was reduced by 0.008 and 0.005, and the R 2 was improved by 0.105 and 0.055, respectively.…”

Section: The Role Of Multi-modal Data In Estimation Results Improvementmentioning

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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“…The data collected by UAVs have a high spatio-temporal resolution and can be effectively used to infer soil moisture conditions and crop growth indicators. Several studies in this area have explored the near-infrared and visible bands [14]. Also, the surface soil moisture was proven to be significantly correlated with the brightness of UAV visible images [15].…”

Section: The Physical Layermentioning

confidence: 99%

Machine Learning for Smart Irrigation in Agriculture: How Far along Are We?

Del-Coco,

Leo,

Carcagnì

2024

Information

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The management of water resources is becoming increasingly important in several contexts, including agriculture. Recently, innovative agricultural practices, advanced sensors, and Internet of Things (IoT) devices have made it possible to improve the efficiency of water use. However, it is the application of control strategies based on advanced machine learning techniques that enables the adoption of smart irrigation scheduling and the immediate economic, social, and environmental benefits. This challenging research area has attracted the attention of many researchers worldwide, who have proposed several technological and methodological solutions. Unfortunately, the results of these scientific efforts have not yet been categorized in a thematic survey, making it difficult to understand how far we are from optimal water management based on machine learning. This paper fills this gap by focusing on smart irrigation systems with an emphasis on machine learning. More specifically, the generic structure of a smart agriculture system is presented, and existing machine learning strategies and available datasets are discussed. Furthermore, several open issues are identified, especially in the processing of long-term data, also due to the lack of corresponding annotated datasets. Finally, some interesting future research directions to be pursued in order to build scalable, domain-independent approaches are proposed.

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Evaluating soil moisture content under maize coverage using UAV multimodal data by machine learning algorithms

Cited by 31 publications

References 52 publications

Calibrating ultrasonic sensor measurements of crop canopy heights: a case study of maize and wheat

Calibrating ultrasonic sensor measurements of crop canopy heights: a case study of maize and wheat

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

Machine Learning for Smart Irrigation in Agriculture: How Far along Are We?

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