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:10.7287/peerj.preprints.27630

Cited by 9 publications

(9 citation statements)

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“…In this study, four individual machine learning algorithms were used to construct winter wheat yield estimation models based on a subset of spectral indices obtained after feature selection. The RF model had the highest accuracy and performed best when trained using the training set data, but the RF model was not the best performer in the validation set of the model, probably due to the overfitting phenomenon of the RF model in the training set [94]. In the model training set, the LRR models all performed the worst, but in the model validation set the GP models performed the worst at the flowering stage and the LRR models performed the worst at the grain-filling stage.…”

Section: Discussionmentioning

confidence: 99%

UAV-Based Hyperspectral and Ensemble Machine Learning for Predicting Yield in Winter Wheat

et al. 2022

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Winter wheat is a widely-grown cereal crop worldwide. Using growth-stage information to estimate winter wheat yields in a timely manner is essential for accurate crop management and rapid decision-making in sustainable agriculture, and to increase productivity while reducing environmental impact. UAV remote sensing is widely used in precision agriculture due to its flexibility and increased spatial and spectral resolution. Hyperspectral data are used to model crop traits because of their ability to provide continuous rich spectral information and higher spectral fidelity. In this study, hyperspectral image data of the winter wheat crop canopy at the flowering and grain-filling stages was acquired by a low-altitude unmanned aerial vehicle (UAV), and machine learning was used to predict winter wheat yields. Specifically, a large number of spectral indices were extracted from the spectral data, and three feature selection methods, recursive feature elimination (RFE), Boruta feature selection, and the Pearson correlation coefficient (PCC), were used to filter high spectral indices in order to reduce the dimensionality of the data. Four major basic learner models, (1) support vector machine (SVM), (2) Gaussian process (GP), (3) linear ridge regression (LRR), and (4) random forest (RF), were also constructed, and an ensemble machine learning model was developed by combining the four base learner models. The results showed that the SVM yield prediction model, constructed on the basis of the preferred features, performed the best among the base learner models, with an R2 between 0.62 and 0.73. The accuracy of the proposed ensemble learner model was higher than that of each base learner model; moreover, the R2 (0.78) for the yield prediction model based on Boruta’s preferred characteristics was the highest at the grain-filling stage.

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Section: Discussionmentioning

confidence: 99%

UAV-Based Hyperspectral and Ensemble Machine Learning for Predicting Yield in Winter Wheat

et al. 2022

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“…Random forest is a popular ensemble machine learning algorithm used for classification and regression [ 109 ] especially in situations where the datasets have high-dimensionality [ 110 ]. Random Forest attempts to fix one of the fundamental decision tree problems: overfitting.…”

Section: Current Soybean Cyst Nematode Detection Approachesmentioning

confidence: 99%

Soybean cyst nematode detection and management: a review

et al. 2022

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Soybeans play a key role in global food security. U.S. soybean yields, which comprise $$32\%$$ 32 % of the total soybeans planted in the world, continue to experience unprecedented grain loss due to the soybean cyst nematode (SCN) plant pathogen. SCN remains one of the primary disruptive pests despite the existence of advanced management techniques such as crop rotation and SCN-resistant varieties. SCN detection is a key step in managing this disease; however, early detection is challenging because soybeans do not show any above ground symptoms unless they are significantly damaged. Direct soil sampling remains the most common method for SCN detection, however, this method has several problems. For example, the threshold damage methods—adopted by most of the laboratories to make recommendations—is not reliable as it does not consider soil pH, N, P, and K values and relies solely on the egg count instead of assessment of the root infection. To overcome the challenges of manual soil sampling methods, deep learning and hyperspectral imaging are important current topics in precision agriculture for plant disease detection and have been proposed as cost-effective and efficient detection methods that can work at scale. We have reviewed more than 150 research papers focusing on soybean cyst nematodes with an emphasis on deep learning techniques for detection and management. First: we describe soybean vegetation and reproduction stages, SCN life cycles, and factors influencing this disease. Second: we highlight the impact of SCN on soybean yield loss and the challenges associated with its detection. Third: we describe direct sampling methods in which the soil samples are procured and analyzed to evaluate SCN egg counts. Fourth: we highlight the advantages and limitations of these direct methods, then review computer vision- and remote sensing-based detection methods: data collection using ground, aerial, and satellite approaches followed by a review of machine learning methods for image analysis-based soybean cyst nematode detection. We highlight the evaluation approaches and the advantages of overall detection workflow in high-performance and big data environments. Lastly, we discuss various management approaches, such as crop rotation, fertilization, SCN resistant varieties such as PI 88788, and SCN’s increasing resistance to these strategies. We review machine learning approaches for soybean crop yield forecasting as well as the influence of pesticides, herbicides, and fertilizers on SCN infestation reduction. We provide recommendations for soybean research using deep learning and hyperspectral imaging to accommodate the lack of the ground truth data and training and testing methodologies, such as data augmentation and transfer learning, to achieve a high level of detection accuracy while keeping costs as low as possible.

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“…Soil moisture content is a key constraint that affects biophysical and biogeochemical processes and precipitation, heat transport, carbon uptake, and climate change patterns over the catchment, continental, and global scales (Badía et al., 2017; Kumar et al., 2018). Information on soil moisture plays a pivotal role in tackling existing and foreseeable global food and water scarcity issues through decision support tools and solutions, including flood runoff predictions, water‐efficient irrigation, and aquifer storage and recovery (Ge et al., 2019). For example, UAVs have been used in smart water management platforms (SWAMP) for precision irrigation (Kamienski et al., 2019).…”

Section: Surface Hydrology and Water Managementmentioning

confidence: 99%

Unmanned Aerial Vehicles in Hydrology and Water Management: Applications, Challenges, and Perspectives

Acharya

Bhandari

Bandini

et al. 2021

Water Resources Research

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Global water withdrawal and usage have risen nearly sixfold since 1900. Approximately 69% of water withdrawals are used in agriculture, 19% in industries, and 12% for municipal purposes (Food and Agriculture Organization of the United Nations, 2020). Anthropogenic disturbances and climate change affect the water cycle, water supply and demand, and impose stress on the global water systems (Haddeland et al., 2014). As such, hydrologists, geologists, agronomists, soil scientists, and engineers need reliable tools to monitor and manage surface and groundwater resources, particularly in locations where traditional monitoring is inadequate.Traditional water monitoring and measurement techniques are time-consuming, costly, and typify an inadequate and disproportionate spatial and temporal range. Over the past few decades, airplanes and satellites have been the chief source of remotely sensed water resource data. However, the usefulness of these platforms is mainly restricted to the largest spatial scales. The low frequency of temporal sampling is dictated by logistical costs (aerial imagery) or tasking schedules (satellites; Becker et al., 2009;Xue & Su, 2017). The dearth of finer resolution data and temporal inflexibility of these platforms precludes the possibility of on-demand data acquisition that is often necessary to capture the highly transient processes operating at fine spatial scales necessary for the planning and management of water resources (e.g., freshwater abstraction) and the assessment of hazards (e.g., flood extents). However, with recent advances in sensors, cameras,

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

Abstract: X. 2019. Combining UAV-based hyperspectral imagery and machine learning algorithms for soil moisture content monitoring. PeerJ 7:e6926 https://doi.

Cited by 9 publications

References 0 publications

UAV-Based Hyperspectral and Ensemble Machine Learning for Predicting Yield in Winter Wheat

UAV-Based Hyperspectral and Ensemble Machine Learning for Predicting Yield in Winter Wheat

Soybean cyst nematode detection and management: a review

Unmanned Aerial Vehicles in Hydrology and Water Management: Applications, Challenges, and Perspectives

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