scholarly journals Improving Unmanned Aerial Vehicle Remote Sensing-Based Rice Nitrogen Nutrition Index Prediction with Machine Learning

2020 ◽  
Vol 12 (2) ◽  
pp. 215 ◽  
Author(s):  
Hainie Zha ◽  
Yuxin Miao ◽  
Tiantian Wang ◽  
Yue Li ◽  
Jing Zhang ◽  
...  
Keyword(s):  
Machine Learning ◽  
Remote Sensing ◽  
Unmanned Aerial Vehicle ◽  
Spectral Reflectance ◽  
Remote Sensing Data ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
Aerial Vehicle ◽  
N Management ◽  
N Status

Optimizing nitrogen (N) management in rice is crucial for China’s food security and sustainable agricultural development. Nondestructive crop growth monitoring based on remote sensing technologies can accurately assess crop N status, which may be used to guide the in-season site-specific N recommendations. The fixed-wing unmanned aerial vehicle (UAV)-based remote sensing is a low-cost, easy-to-operate technology for collecting spectral reflectance imagery, an important data source for precision N management. The relationships between many vegetation indices (VIs) derived from spectral reflectance data and crop parameters are known to be nonlinear. As a result, nonlinear machine learning methods have the potential to improve the estimation accuracy. The objective of this study was to evaluate five different approaches for estimating rice (Oryza sativa L.) aboveground biomass (AGB), plant N uptake (PNU), and N nutrition index (NNI) at stem elongation (SE) and heading (HD) stages in Northeast China: (1) single VI (SVI); (2) stepwise multiple linear regression (SMLR); (3) random forest (RF); (4) support vector machine (SVM); and (5) artificial neural networks (ANN) regression. The results indicated that machine learning methods improved the NNI estimation compared to VI-SLR and SMLR methods. The RF algorithm performed the best for estimating NNI (R2 = 0.94 (SE) and 0.96 (HD) for calibration and 0.61 (SE) and 0.79 (HD) for validation). The root mean square errors (RMSEs) were 0.09, and the relative errors were <10% in all the models. It is concluded that the RF machine learning regression can significantly improve the estimation of rice N status using UAV remote sensing. The application machine learning methods offers a new opportunity to better use remote sensing data for monitoring crop growth conditions and guiding precision crop management. More studies are needed to further improve these machine learning-based models by combining both remote sensing data and other related soil, weather, and management information for applications in precision N and crop management.

scholarly journals Spatial Prediction of Agrochemical Properties on the Scale of a Single Field Using Machine Learning Methods Based on Remote Sensing Data

Agronomy ◽  
2021 ◽  
Vol 11 (11) ◽  
pp. 2266
Author(s):  
Ilnas Sahabiev ◽  
Elena Smirnova ◽  
Kamil Giniyatullin
Keyword(s):  
Machine Learning ◽  
Remote Sensing ◽  
Remote Sensing Data ◽  
Spatial Prediction ◽  
Landsat 8 ◽  
Landsat 8 Oli ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
Agrochemical Properties ◽  
Sentinel 2

Creating accurate digital maps of the agrochemical properties of soils on a field scale with a limited data set is a problem that slows down the introduction of precision farming. The use of machine learning methods based on the use of direct and indirect predictors of spatial changes in the agrochemical properties of soils is promising. Spectral indicators of open soil based on remote sensing data, as well as soil properties, were used to create digital maps of available forms of nitrogen, phosphorus, and potassium. It was shown that machine learning methods based on support vectors (SVMr) and random forest (RF) using spectral reflectance data are similarly accurate at spatial prediction. An acceptable prediction was obtained for available nitrogen and available potassium; the variability of available phosphorus was modeled less accurately. The coefficient of determination (R2) of the best model for nitrogen is R2SVMr = 0.90 (Landsat 8 OLI) and R2SVMr = 0.79 (Sentinel 2), for potassium—R2SVMr = 0.82 (Landsat 8 OLI) and R2SVMr = 0.77 (Sentinel 2), for phosphorus—R2SVMr = 0.68 (Landsat 8 OLI), R2SVMr = 0.64 (Sentinel 2). The models based on remote sensing data were refined when soil organic matter (SOC) and fractions of texture (Silt, Clay) were included as predictors. The SVMr models were the most accurate. For Landsat 8 OLI, the SVMr model has a R2 value: nitrogen—R2 = 0.95, potassium—R2 = 0.89 and phosphorus—R2 = 0.65. Based on Sentinel 2, nitrogen—R2 = 0.92, potassium—R2 = 0.88, phosphorus—R2 = 0.72. The spatial prediction of nitrogen content is influenced by SOC, potassium—by SOC and texture, phosphorus—by texture. The validation of the final models was carried out on an independent sample on soils from a chernozem zone. For nitrogen based on Landsat 8 OLI R2 = 0.88, for potassium R2 = 0.65, and for phosphorus R2 = 0.31. Based on Sentinel 2, for nitrogen R2 = 0.85, for potassium R2 = 0.62, and for phosphorus R2 = 0.71. The inclusion of SOC and texture in remote sensing-based machine learning models makes it possible to improve the spatial prediction of nitrogen, phosphorus and potassium availability of soils in chernozem zones and can potentially be widely used to create digital agrochemical maps on the scale of a single field.

scholarly journals Statistical Machine Learning Methods and Remote Sensing for Sustainable Development Goals: A Review

2018 ◽  
Vol 10 (9) ◽  
pp. 1365 ◽  
Author(s):  
Jacinta Holloway ◽  
Kerrie Mengersen
Keyword(s):  
Machine Learning ◽  
Remote Sensing ◽  
Sustainable Development ◽  
Sustainable Development Goals ◽  
Remote Sensing Data ◽  
Statistical Machine Learning ◽  
Learning Methods ◽  
Sensing Data ◽  
Machine Learning Methods ◽  
Development Goals

Interest in statistical analysis of remote sensing data to produce measurements of environment, agriculture, and sustainable development is established and continues to increase, and this is leading to a growing interaction between the earth science and statistical domains. With this in mind, we reviewed the literature on statistical machine learning methods commonly applied to remote sensing data. We focus particularly on applications related to the United Nations World Bank Sustainable Development Goals, including agriculture (food security), forests (life on land), and water (water quality). We provide a review of useful statistical machine learning methods, how they work in a remote sensing context, and examples of their application to these types of data in the literature. Rather than prescribing particular methods for specific applications, we provide guidance, examples, and case studies from the literature for the remote sensing practitioner and applied statistician. In the supplementary material, we also describe the necessary steps pre and post analysis for remote sensing data; the pre-processing and evaluation steps.

scholarly journals In-Season Diagnosis of Winter Wheat Nitrogen Status in Smallholder Farmer Fields Across a Village Using Unmanned Aerial Vehicle-Based Remote Sensing

Agronomy ◽  
2019 ◽  
Vol 9 (10) ◽  
pp. 619 ◽  
Author(s):  
Zhichao Chen ◽  
Yuxin Miao ◽  
Junjun Lu ◽  
Lan Zhou ◽  
Yue Li ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Winter Wheat ◽  
Unmanned Aerial Vehicle ◽  
Farming Systems ◽  
Soil Conditions ◽  
Small Scale ◽  
Smallholder Farmer ◽  
Aerial Vehicle ◽  
N Management ◽  
N Status

Improving nitrogen (N) management of small-scale farming systems in developing countries is crucially important for food security and sustainable development of world agriculture, but it is also very challenging. The N Nutrition Index (NNI) is a reliable indicator for crop N status, and there is an urgent need to develop an effective method to non-destructively estimate crop NNI in different smallholder farmer fields to guide in-season N management. The eBee fixed-wing unmanned aerial vehicle (UAV)-based remote sensing system, a ready-to-deploy aircraft with a Parrot Sequoia+ multispectral camera onboard, has been used for applications in precision agriculture. The objectives of this study were to (i) determine the potential of using fixed-wing UAV-based multispectral remote sensing for non-destructive estimation of winter wheat NNI in different smallholder farmer fields across the study village in the North China Plain (NCP) and (ii) develop a practical strategy for village-scale winter wheat N status diagnosis in small scale farming systems. Four plot experiments were conducted within farmer fields in 2016 and 2017 in a village of Laoling County, Shandong Province in the NCP for evaluation of a published critical N dilution curve and for serving as reference plots. UAV remote sensing images were collected from all the fields across the village in 2017 and 2018. About 150 plant samples were collected from farmer fields and plot experiments each year for ground truthing. Two indirect and two direct approaches were evaluated for estimating NNI using vegetation indices (VIs). To facilitate practical applications, the performance of three commonly used normalized difference VIs were compared with the top performing VIs selected from 59 tested indices. The most practical and stable method was using VIs to calculate N sufficiency index (NSI) and then to estimate NNI non-destructively (R2 = 0.53–0.56). Using NSI thresholds to diagnose N status directly was quite stable, with a 57–59% diagnostic accuracy rate. This strategy is practical and least affected by the choice of VIs across fields, varieties, and years. This study demonstrates that fixed-wing UAV–based remote sensing is a promising technology for in-season diagnosis of winter wheat N status in smallholder farmer fields at village scale. The considerable variability in local soil conditions and crop management practices influenced the overall accuracy of N diagnosis, so more studies are needed to further validate and optimize the reported strategy and consecutively develop practical UAV remote sensing–based in-season N recommendation methods.

scholarly journals Forecasting Rainfed Agricultural Production in Arid and Semi-Arid Lands Using Learning Machine Methods: A Case Study

2021 ◽  
Vol 13 (9) ◽  
pp. 4607
Author(s):  
Shahram Rezapour ◽  
Erfan Jooyandeh ◽  
Mohsen Ramezanzade ◽  
Ali Mostafaeipour ◽  
Mehdi Jahangiri ◽  
...  
Keyword(s):  
Machine Learning ◽  
Remote Sensing ◽  
Remote Sensing Data ◽  
Weather Data ◽  
Support Vector ◽  
Learning Methods ◽  
K Nearest Neighbors ◽  
Sensing Data ◽  
Machine Learning Methods ◽  
Rainfed Farming

With the rising demand for food products and the direct impact of climate change on food production in many parts of the world, recent years have seen growing interest in the subject of food security and the role of rainfed farming in this area. Machine learning methods can be used to predict crop yield based on a combination of remote sensing data and data collected by ground weather stations. This paper argues that forecasting drylands farming yield can be reliable for management purpose under uncertain conditions using machine learning methods and remote sensing data and determines which indicators are most important in predicting the yield of chickpea. In this study, the yield of rainfed chickpea farms in 11 top chickpea producing counties in Kermanshah province, Iran, was predicted using three machine learning methods, namely support vector regression (SVR), random forest (RF), and K-nearest neighbors (KNN). To improve prediction accuracy, for each county, remote sensing data were overlaid by the satellite images of rainfed farms with a suitable slope and altitude for rainfed farming. An integrated database was created by combining weather data, remote sensing data, and chickpea yield statistics. The methods were evaluated using the leave-one-out cross-validation (LOOCV) technique and compared in terms of multiple measures. Given the sensitivity of rainfed chickpea yield to the time of data, the predictions were made in two scenarios: (1) using the averages of the data of all growing months, and (2) using the data of a combination of months. The results showed that RF provides more accurate yield predictions than other methods. The predictions of this method were 7–8% different from the statistics reported by the Statistical Center and the Ministry of Agriculture of Iran. It was found that for pre-harvest prediction of rainfed chickpea yield, using the data of the March–April period (the averages of two months) offers the best result in terms of the correlation coefficient for the relationship between the yield and the predictor indices.

Detecting a keystone species European aspen in boreal forests with airborne hyperspectral, LiDAR and UAV data with machine learning methods

2021 ◽  
Author(s):  
Timo Kumpula ◽  
Janne Mäyrä ◽  
Anton Kuzmin ◽  
Arto Viinikka ◽  
Sonja Kivinen ◽  
...  
Keyword(s):  
Machine Learning ◽  
Remote Sensing ◽  
Deep Learning ◽  
High Resolution ◽  
Boreal Forests ◽  
Tree Level ◽  
Support Vector ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
European Aspen

&lt;p&gt;Sustainable forest management increasingly highlights the maintenance of biological diversity and requires up-to-date information on the occurrence and distribution of key ecological features in forest environments. Different proxy variables indicating species richness and quality of the sites are essential for efficient detecting and monitoring forest biodiversity. European aspen (Populus tremula L.) is a minor deciduous tree species with a high importance in maintaining biodiversity in boreal forests. Large aspen trees host hundreds of species, many of them classified as threatened. However, accurate fine-scale spatial data on aspen occurrence remains scarce and incomprehensive.&lt;/p&gt;&lt;p&gt;&amp;#160;&lt;/p&gt;&lt;p&gt;We studied detection of aspen using different remote sensing techniques in Evo, southern Finland. Our study area of 83 km&lt;sup&gt;2&lt;/sup&gt; contains both managed and protected southern boreal forests characterized by Scots pine (Pinus sylvestris L.), Norway spruce (Picea abies (L.) Karst), and birch (Betula pendula and pubescens L.), whereas European aspen has a relatively sparse and scattered occurrence in the area. We collected high-resolution airborne hyperspectral and airborne laser scanning data covering the whole study area and ultra-high resolution unmanned aerial vehicle (UAV) data with RGB and multispectral sensors from selected parts of the area. We tested the discrimination of aspen from other species at tree level using different machine learning methods (Support Vector Machines, Random Forest, Gradient Boosting Machine) and deep learning methods (3D convolutional neural networks).&lt;/p&gt;&lt;p&gt;&amp;#160;&lt;/p&gt;&lt;p&gt;Airborne hyperspectral and lidar data gave excellent results with machine learning and deep learning classification methods The highest classification accuracies for aspen varied between 91-92% (F1-score). The most important wavelengths for discriminating aspen from other species included reflectance bands of red edge range (724&amp;#8211;727 nm) and shortwave infrared (1520&amp;#8211;1564 nm and 1684&amp;#8211;1706 nm) (Viinikka et al. 2020; M&amp;#228;yr&amp;#228; et al 2021). Aspen detection using RGB and multispectral data also gave good results (highest F1-score of aspen = 87%) (Kuzmin et al 2021). Different remote sensing data enabled production of a spatially explicit map of aspen occurrence in the study area. Information on aspen occurrence and abundance can significantly contribute to biodiversity management and conservation efforts in boreal forests. Our results can be further utilized in upscaling efforts aiming at aspen detection over larger geographical areas using satellite images.&lt;/p&gt;

scholarly journals Machine Learning to Estimate Surface Soil Moisture from Remote Sensing Data

Water ◽  
2020 ◽  
Vol 12 (11) ◽  
pp. 3223
Author(s):  
Hamed Adab ◽  
Renato Morbidelli ◽  
Carla Saltalippi ◽  
Mahmoud Moradian ◽  
Gholam Abbas Fallah Ghalhari
Keyword(s):  
Machine Learning ◽  
Remote Sensing ◽  
Soil Moisture ◽  
Surface Soil ◽  
Remote Sensing Data ◽  
Surface Soil Moisture ◽  
Machine Learning Methods ◽  
Land Use Types ◽  
Semi Arid Region ◽  
Semi Arid

Soil moisture is an integral quantity parameter in hydrology and agriculture practices. Satellite remote sensing has been widely applied to estimate surface soil moisture. However, it is still a challenge to retrieve surface soil moisture content (SMC) data in the heterogeneous catchment at high spatial resolution. Therefore, it is necessary to improve the retrieval of SMC from remote sensing data, which is important in the planning and efficient use of land resources. Many methods based on satellite-derived vegetation indices have already been developed to estimate SMC in various climatic and geographic conditions. Soil moisture retrievals were performed using statistical and machine learning methods as well as physical modeling techniques. In this study, an important experiment of soil moisture retrieval for investigating the capability of the machine learning methods was conducted in the early spring season in a semi-arid region of Iran. We applied random forest (RF), support vector machine (SVM), artificial neural network (ANN), and elastic net regression (EN) algorithms to soil moisture retrieval by optical and thermal sensors of Landsat 8 and knowledge of land-use types on previously untested conditions in a semi-arid region of Iran. The statistical comparisons show that RF method provided the highest Nash–Sutcliffe efficiency value (0.73) for soil moisture retrieval covered by the different land-use types. Combinations of surface reflectance and auxiliary geospatial data can provide more valuable information for SMC estimation, which shows promise for precision agriculture applications.

Launching an unmanned aerial vehicle remote sensing data carrier: concept, key components and prospects

2019 ◽  
Vol 13 (10) ◽  
pp. 1172-1185
Author(s):  
Xiaohan Liao ◽  
Huanyin Yue ◽  
Ronggao Liu ◽  
Xiangyong Luo ◽  
Bin Luo ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Unmanned Aerial Vehicle ◽  
Remote Sensing Data ◽  
Sensing Data ◽  
Data Carrier ◽  
Aerial Vehicle

scholarly journals Measurement of nitrogen content in rice by inversion of hyperspectral reflectance data from an unmanned aerial vehicle

2018 ◽  
Vol 48 (6) ◽  
Author(s):  
Du Wen ◽  
Xu Tongyu ◽  
Yu Fenghua ◽  
Chen Chunling
Keyword(s):  
Remote Sensing ◽  
Nitrogen Content ◽  
Unmanned Aerial Vehicle ◽  
Gaussian Process Regression ◽  
Remote Sensing Data ◽  
Principal Component ◽  
Ground Truth ◽  
Hyperspectral Remote Sensing ◽  
Inversion Algorithm ◽  
Aerial Vehicle

ABSTRACT: The Nitrogen content of rice leaves has a significant effect on growth quality and crop yield. We proposed and demonstrated a non-invasive method for the quantitative inversion of rice nitrogen content based on hyperspectral remote sensing data collected by an unmanned aerial vehicle (UAV). Rice canopy albedo images were acquired by a hyperspectral imager onboard an M600-UAV platform. The radiation calibration method was then used to process these data and the reflectance of canopy leaves was acquired. Experimental validation was conducted using the rice field of Shenyang Agricultural University, which was classified into 4 fertilizer levels: zero nitrogen, low nitrogen, normal nitrogen, and high nitrogen. Gaussian process regression (GPR) was then used to train the inversion algorithm to identify specific spectral bands with the highest contribution. This led to a reduction in noise and a higher inversion accuracy. Principal component analysis (PCA) was also used for dimensionality reduction, thereby reducing redundant information and significantly increasing efficiency. A comparison with ground truth measurements demonstrated that the proposed technique was successful in establishing a nitrogen inversion model, the accuracy of which was quantified using a linear fit (R2=0.8525) and the root mean square error (RMSE=0.9507). These results support the use of GPR and provide a theoretical basis for the inversion of rice nitrogen by UAV hyperspectral remote sensing.

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