Combining UAV-based hyperspectral imagery and machine learning algorithms for soil moisture content monitoring

Ge, Xiangyu; Wang, Jingzhe; Ding, Jianli; Cao, Xiaoyi; Zhang, Zipeng; Liu, Jie; Li, Xiaohang

doi:10.7717/peerj.6926

Cited by 123 publications

(47 citation statements)

References 88 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For fine-scale research on surface ice melt on an Arctic glacier, [103] used an ablation stake network as ground truth data for measuring melting variability, while an analysis of slow geomorphological processes [104] used terrestrial laser scan as a reference. For soil water content measurement, the most common method is soil sampling, within defined sampling cells [105] or precisely determined locations [106] of crop fields at a specific depth. Afterward, soil samples are oven-dried in laboratory processing and used as reference data.…”

Section: Field Data Collectionmentioning

confidence: 99%

Current Practices in UAS-based Environmental Monitoring

et al. 2020

View full text Add to dashboard Cite

With the increasing role that unmanned aerial systems (UAS) are playing in data collection for environmental studies, two key challenges relate to harmonizing and providing standardized guidance for data collection, and also establishing protocols that are applicable across a broad range of environments and conditions. In this context, a network of scientists are cooperating within the framework of the Harmonious Project to develop and promote harmonized mapping strategies and disseminate operational guidance to ensure best practice for data collection and interpretation. The culmination of these efforts is summarized in the present manuscript. Through this synthesis study, we identify the many interdependencies of each step in the collection and processing chain, and outline approaches to formalize and ensure a successful workflow and product development. Given the number of environmental conditions, constraints, and variables that could possibly be explored from UAS platforms, it is impractical to provide protocols that can be applied universally under all scenarios. However, it is possible to collate and systematically order the fragmented knowledge on UAS collection and analysis to identify the best practices that can best ensure the streamlined and rigorous development of scientific products.

show abstract

Section: Field Data Collectionmentioning

confidence: 99%

Current Practices in UAS-based Environmental Monitoring

et al. 2020

View full text Add to dashboard Cite

show abstract

“…As the state of the art, the unmanned aerial vehicle (UAV) is now widely used for data collection in agricultural- and ecological-related applications, and it can be quickly deployed for acquiring images when needed compared with traditional satellite remote sensing (SRS) [ 30 , 31 , 32 , 33 , 34 ]. The high temporal and spatial images from UAV platforms are better for analysis as the data are not much influenced by the cloud [ 35 , 36 , 37 ].…”

Section: Introductionmentioning

confidence: 99%

Modified Red Blue Vegetation Index for Chlorophyll Estimation and Yield Prediction of Maize from Visible Images Captured by UAV

Guo

Wang

et al. 2020

Sensors

Self Cite

View full text Add to dashboard Cite

The vegetation index (VI) has been successfully used to monitor the growth and to predict the yield of agricultural crops. In this paper, a long-term observation was conducted for the yield prediction of maize using an unmanned aerial vehicle (UAV) and estimations of chlorophyll contents using SPAD-502. A new vegetation index termed as modified red blue VI (MRBVI) was developed to monitor the growth and to predict the yields of maize by establishing relationships between MRBVI- and SPAD-502-based chlorophyll contents. The coefficients of determination (R2s) were 0.462 and 0.570 in chlorophyll contents’ estimations and yield predictions using MRBVI, and the results were relatively better than the results from the seven other commonly used VI approaches. All VIs during the different growth stages of maize were calculated and compared with the measured values of chlorophyll contents directly, and the relative error (RE) of MRBVI is the lowest at 0.355. Further, machine learning (ML) methods such as the backpropagation neural network model (BP), support vector machine (SVM), random forest (RF), and extreme learning machine (ELM) were adopted for predicting the yields of maize. All VIs calculated for each image captured during important phenological stages of maize were set as independent variables and the corresponding yields of each plot were defined as dependent variables. The ML models used the leave one out method (LOO), where the root mean square errors (RMSEs) were 2.157, 1.099, 1.146, and 1.698 (g/hundred grain weight) for BP, SVM, RF, and ELM. The mean absolute errors (MAEs) were 1.739, 0.886, 0.925, and 1.356 (g/hundred grain weight) for BP, SVM, RF, and ELM, respectively. Thus, the SVM method performed better in predicting the yields of maize than the other ML methods. Therefore, it is strongly suggested that the MRBVI calculated from images acquired at different growth stages integrated with advanced ML methods should be used for agricultural- and ecological-related chlorophyll estimation and yield predictions.

show abstract

“…For instance, the UAV-derived DSM data provide structural and spatial information that increases the capability to separate different species of mangrove in [102]. The effectiveness of the UAV hyperspectral images in mangrove species classification is verified in [27]. Relative height such as airborne LiDAR data plays an important role in classification accuracy and a combination of both spectral and geometric information is the best set of features [69].…”

Section: Statistics Of All 62 Classification Studies Indicate the Relmentioning

confidence: 99%

Meta-analysis of Unmanned Aerial Vehicle (UAV) Imagery for Agro-environmental Monitoring Using Machine Learning and Statistical Models

et al. 2020

View full text Add to dashboard Cite

Unmanned Aerial Vehicle (UAV) imaging systems have recently gained significant attention from researchers and practitioners as a cost-effective means for agro-environmental applications. In particular, machine learning algorithms have been applied to UAV-based remote sensing data for enhancing the UAV capabilities of various applications. This systematic review was performed on studies through a statistical meta-analysis of UAV applications along with machine learning algorithms in agro-environmental monitoring. For this purpose, a total number of 163 peer-reviewed articles published in 13 high-impact remote sensing journals over the past 20 years were reviewed focusing on several features, including study area, application, sensor type, platform type, and spatial resolution. The meta-analysis revealed that 62% and 38% of the studies applied regression and classification models, respectively. Visible sensor technology was the most frequently used sensor with the highest overall accuracy among classification articles. Regarding regression models, linear regression and random forest were the most frequently applied models in UAV remote sensing imagery processing. Finally, the results of this study confirm that applying machine learning approaches on UAV imagery produces fast and reliable results. Agriculture, forestry, and grassland mapping were found as the top three UAV applications in this review, in 42%, 22%, and 8% of the studies, respectively.

show abstract

Combining UAV-based hyperspectral imagery and machine learning algorithms for soil moisture content monitoring

Cited by 123 publications

References 88 publications

Current Practices in UAS-based Environmental Monitoring

Current Practices in UAS-based Environmental Monitoring

Modified Red Blue Vegetation Index for Chlorophyll Estimation and Yield Prediction of Maize from Visible Images Captured by UAV

Meta-analysis of Unmanned Aerial Vehicle (UAV) Imagery for Agro-environmental Monitoring Using Machine Learning and Statistical Models

Contact Info

Product

Resources

About