UAV-based Multispectral Imagery Research Articles

Quantitative Analysis of Atrazine Impact on UAV-Derived Multispectral Indices and Correlated Plant Pigment Alterations: A Heatmap Approach

Utilizing Unmanned Aerial Vehicle (UAV) multispectral technology offers a non-destructive and efficient approach to monitoring plant health and stress responses by analyzing reflectance data across various wavelengths. This study integrates UAV-based multispectral imagery with ground-measured sample data to evaluate the impact of atrazine (ATR) on chlorophyll a, chlorophyll b, carotenoids, and anthocyanins in Red Cos lettuce. The results indicate a significant increase (p < 0.05) in ATR concentration in lettuce with soil application, leading to notable reductions in pigment concentrations. Heatmap analysis reveals that EVI shows the strongest negative correlations with pigment classes (coefficients ranging between −0.75 to −0.85), while NDVI, GNDVI, and BNDVI exhibit the strongest positive correlations with pigments (coefficients > 0.75). These findings highlight the potential of this innovative technique in predicting pigment concentrations and emphasize its importance in monitoring pesticide effects for sustainable agriculture.

Bitou bush detection and mapping using UAV-based multispectral and hyperspectral imagery and artificial intelligence

The use of Unmanned Aerial Vehicles (UAVs) for remote sensing (RS) of vegetation presents a valuable platform for weed monitoring, owing to the high spatial resolution of collected images. Accurate segmentation and mapping of weed spatial distribution plays a pivotal role in achieving effective management and ensures efficient and sustainable utilization of weed control measures. Furthermore, UAV-based RS provides a rapid way of assessing phenological development stages of weed species such as flowering and fruiting. These are often critical stages required for the separation of weed species from surrounding vegetation and are difficult to capture with traditional low resolution airborne and satellite RS imagery. Bitou bush is a shrub and a serious environmental weed of coastal areas of New South Wales (NSW). The primary objective of this study is to develop a model for bitou bush mapping from collected multispectral (MS) and hyperspectral (HS) imagery on location in NSW, Australia by employing various classical machine learning (ML) and deep learning (DL) techniques. The performance of Random forests (RF), Support vector machine (SVM), Extreme gradient boosting (XGB), and K-nearest neighbors (KNN) models is evaluated, achieving overall validation accuracies of 78%, 74%, 80%, and 69%, respectively for bitou bush detection using MS imagery. Subsequently, these models are assessed on HS data, resulting in overall validation accuracies of 77%, 86%, 86%, and 80% for RF, SVM, XGB, and KNN, respectively. Moreover, the DL U-Net model achieved an overall validation accuracy of 92%, outperforming the classical ML models in MS data segmentation tasks. The results of this study highlight the superior performance of the U-Net model in comparison to classical ML models in RS data segmentation, indicating the value of DL techniques for more accurate and robust RS applications such as bitou bush detection and mapping. The insights gained from this research will aid researchers and land managers select appropriate models based on the complexity and characteristics of their RS datasets. Moreover, the integration of UAV RS and artificial intelligence (AI) provide a valuable and efficient platform for bitou bush monitoring and management practices, ultimately enhancing the efficiency and sustainability of weed control efforts.

Estimation of Winter Wheat Yield Using Multiple Temporal Vegetation Indices Derived from UAV-Based Multispectral and Hyperspectral Imagery

Winter wheat is a major food source for the inhabitants of North China. However, its yield is affected by drought stress during the growing period. Hence, it is necessary to develop drought-resistant winter wheat varieties. For breeding researchers, yield measurement, a crucial breeding indication, is costly, labor-intensive, and time-consuming. Therefore, in order to breed a drought-resistant variety of winter wheat in a short time, field plot scale crop yield estimation is essential. Unmanned aerial vehicles (UAVs) have developed into a reliable method for gathering crop canopy information in a non-destructive and time-efficient manner in recent years. This study aimed to evaluate strategies for estimating crop yield using multispectral (MS) and hyperspectral (HS) imagery derived from a UAV in single and multiple growth stages of winter wheat. To accomplish our objective, we constructed a simple linear regression model based on the single growth stages of booting, heading, flowering, filling, and maturation and a multiple regression model that combined these five growth stages to estimate winter wheat yield using 36 vegetation indices (VIs) calculated from UAV-based MS and HS imagery, respectively. After comparing these regression models, we came to the following conclusions: (1) the flowering stage of winter wheat showed the highest correlation with crop yield for both MS and HS imagery; (2) the VIs derived from the HS imagery performed better in terms of estimation accuracy than the VIs from the MS imagery; (3) the regression model that combined the information of five growth stages presented better accuracy than the one that considered the growth stages individually. The best estimation regression model for winter wheat yield in this study was the multiple linear regression model constructed by the VI of ‘b1−b2/b3−b4’ derived from HS imagery, incorporating the five growth stages of booting, heading, flowering, filling, and maturation with r of 0.84 and RMSE of 0.69 t/ha. The corresponding central wavelengths were 782 nm, 874 nm, 762 nm, and 890 nm, respectively. Our study indicates that the multiple temporal VIs derived from UAV-based HS imagery are effective tools for breeding researchers to estimate winter wheat yield on a field plot scale.

Assessing the Water Status and Leaf Pigment Content of Olive Trees: Evaluating the Potential and Feasibility of Unmanned Aerial Vehicle Multispectral and Thermal Data for Estimation Purposes

Global warming presents a significant threat to the sustainability of agricultural systems, demanding increased irrigation to mitigate the impacts of prolonged dry seasons. Efficient water management strategies, including deficit irrigation, have thus become essential, requiring continuous crop monitoring. However, conventional monitoring methods are laborious and time-consuming. This study investigates the potential of aerial imagery captured by unmanned aerial vehicles (UAVs) to predict critical water stress indicators—relative water content (RWC), midday leaf water potential (ΨMD), stomatal conductance (gs)—as well as the pigment content (chlorophyll ab, chlorophyll a, chlorophyll b and carotenoids) of trees in an olive orchard. Both thermal and spectral vegetation indices are calculated and correlated using linear and exponential regression models. The results reveal that the thermal vegetation indices contrast in estimating the water stress indicators, with the Crop Water Stress Index (CWSI) demonstrating higher precision in predicting the RWC (R2 = 0.80), ΨMD (R2 = 0.61) and gs (R2 = 0.72). Additionally, the Triangular Vegetation Index (TVI) shows superior accuracy in predicting the chlorophyll ab (R2 = 0.64) and chlorophyll a (R2 = 0.61), while the Modified Chlorophyll Absorption in Reflectance Index (MCARI) proves most effective for estimating the chlorophyll b (R2 = 0.52). This study emphasizes the potential of UAV-based multispectral and thermal infrared imagery in precision agriculture, enabling assessments of the water status and pigment content. Moreover, these results highlight the vital importance of this technology in optimising resource allocation and enhancing olive production, critical steps towards sustainable agriculture in the face of global warming.

Analyze the Relationship Between Aboveground Biomass and NDVI Values Derived from UAV Multispectral Imagery

Aboveground biomass is usually estimated using an allometric method. Diameter at breast height (DBH) is one of the important variables that is used in estimating aboveground biomass. There is a relationship between aboveground biomass values with size of trees, DBH, tree height, crown diameter and greenness of trees. In remote sensing, the tree biophysical parameters can be derived from digital image processing and greenness of the trees can be assessed using normalized difference vegetation index (NDVI). Thus, the aim of this study is to analyze the relationship between aboveground biomass and NDVI values using UAV-based multispectral imagery. Three developed allometric equations were used to analyse the relationship between aboveground biomass and NDVI values. The results show that there is a moderate relationship between the NDVI values derived from multispectral imagery and estimated aboveground biomass with average R2 is 0.55. Altogether, 347 samples of data have been successfully collected at the study in Seksyen U11, Bukit Bandaraya, Shah Alam with the average diameter at breast height (DBH) is 29.457cm.

Aerial Identification of Fruit Maturity in Amazonian Palms via Plant-Canopy Modeling

UAV-based multispectral imagery was used to characterize and associate the canopy features of the Moriche palm with the maturity state of its fruits, by correlating variations in the palm’s reflectance at different wavelengths throughout the phenological cycle. Several approaches for feature extraction were compared based on vegetation indices and graph-based models. A comprehensive dataset was collected and labeled, containing spatial–temporal variations in the features. Experimental results reported an accuracy of 72% in the estimation of the fruit maturity state, applying the proposed system to the dense forests of Colombia Amazonian region. Also, this UAV-based vision system enables monitoring, inventorying, palm identification, and fruit maturity identification, providing support to the local indigenous organizations of the Amazon.

Areca Yellow Leaf Disease Severity Monitoring Using UAV-Based Multispectral and Thermal Infrared Imagery

The areca nut is the primary economic source for some farmers in southeast Asia. However, the emergence of areca yellow leaf disease (YLD) has seriously reduced the annual production of areca nuts. There is an urgent need for an effective method to monitor the severity of areca yellow leaf disease (SAYD). This study selected an areca orchard with a high incidence of areca YLD as the study area. An unmanned aerial vehicle (UAV) was used to acquire multispectral and thermal infrared data from the experimental area. The ReliefF algorithm was selected as the feature selection algorithm and ten selected vegetation indices were used as the feature variables to build six machine-learning classification models. The experimental results showed that the combination of ReliefF and the Random Forest algorithm achieved the highest accuracy in the prediction of SAYD. Compared to manually annotated true values, the R2 value, root mean square error, and mean absolute percentage error reached 0.955, 0.049, and 1.958%, respectively. The Pearson correlation coefficient between SAYD and areca canopy temperature (CT) was 0.753 (p value < 0.001). The experimental region was partitioned, and a nonlinear fit was performed using CT versus SAYD. Cross-validation was performed on different regions, and the results showed that the R2 value between the predicted result of SAYD by the CT and actual value reached 0.723. This study proposes a high-precision SAYD prediction method and demonstrates the correlation between the CT and SAYD. The results and methods can also provide new research insights and technical tools for botanical researchers and areca practitioners, and have the potential to be extended to more plants.

Tree Height and Crown Extraction From UAV-Based Multispectral Imagery

Conventional methods in measuring tree height and crown diameter are time consuming compared to the advanced technology of unmanned aerial vehicle (UAV) multispectral imagery. UAV multispectral imagery is widely used in vegetation analysis such as crop analysis, vegetation monitoring, precise farming and vegetation health assessment. Thus, the purpose of this research is to extract the individual tree height and crown diameter from UAV- based multispectral imagery using the integration of geospatial techniques. Altogether, the total of 395 individual trees were extracted from the study area of Section U11, Shah Alam using the Support Vector Machine (SVM) classifier. Tree height values were extracted from normalized digital surface model (nDSM) using the zonal statistics tool with the tree height range between 1.568m to 27.850m. The range for derived crown diameters is between 0.919m to 24.506m. The final map shows the distribution of tree height and tree crown extraction from the UAV-based multispectral imagery. The spatial distribution data of tree height and crown diameter are beneficial especially in landscaping and identifying the potential of tree hazard in the urban area.

Multimodal Deep Learning for Rice Yield Prediction Using UAV-Based Multispectral Imagery and Weather Data

Precise yield predictions are useful for implementing precision agriculture technologies and making better decisions in crop management. Convolutional neural networks (CNNs) have recently been used to predict crop yields in unmanned aerial vehicle (UAV)-based remote sensing studies, but weather data have not been considered in modeling. The aim of this study was to explore the potential of multimodal deep learning on rice yield prediction accuracy using UAV multispectral images at the heading stage, along with weather data. The effects of the CNN architectures, layer depths, and weather data integration methods on the prediction accuracy were evaluated. Overall, the multimodal deep learning model integrating UAV-based multispectral imagery and weather data had the potential to develop more precise rice yield predictions. The best models were those trained with weekly weather data. A simple CNN feature extractor for UAV-based multispectral image input data might be sufficient to predict crop yields accurately. However, the spatial patterns of the predicted yield maps differed from model to model, although the prediction accuracy was almost the same. The results indicated that not only the prediction accuracies, but also the robustness of within-field yield predictions, should be assessed in further studies.

Towards complete tree crown delineation by instance segmentation with Mask R–CNN and DETR using UAV-based multispectral imagery and lidar data

Precise single tree delineation allows for a more reliable determination of essential parameters such as tree species, height and vitality. Methods of instance segmentation are powerful neural networks for detecting and segmenting single objects and have the potential to push the accuracy of tree segmentation methods to a new level. In this study, two instance segmentation methods, Mask R–CNN and DETR, were applied to precisely delineate single tree crowns using multispectral images and images generated from UAV lidar data. The study area was in Bavaria, 35 km north of Munich (Germany), comprising a mixed forest stand of around 7 ha characterised mainly by Norway spruce (Picea abies) and large groups of European beeches (Fagus sylvatica) with 181–236 trees per ha. The data set, consisting of multispectral images and lidar data, was acquired using a Micasense RedEdge-MX dual camera system and a Riegl miniVUX-1UAV lidar scanner, both mounted on a hexacopter (DJI Matrice 600 Pro). At an altitude of approximately 85 m, two flight missions were conducted at an airspeed of 5 m/s, leading to a ground resolution of 5 cm and a lidar point density of 560 points/m2. In total, 1408 trees were marked by visual interpretation of the remote sensing data for training and validating the classifiers. Additionally, 125 trees were surveyed by tacheometric means used to test the optimized neural networks. The evaluations showed that segmentation using only multispectral imagery performed slightly better than with images generated from lidar data. In terms of F1 score, Mask R–CNN with color infrared (CIR) images achieved 92% in coniferous, 85% in deciduous and 83% in mixed stands. Compared to the images generated by lidar data, these scores are the same for coniferous and slightly worse for deciduous and mixed plots, by 4% and 2%, respectively. DETR with CIR images achieved 90% in coniferous, 81% in deciduous and 84% in mixed stands. These scores were 2%, 1%, and 2% worse, respectively, compared to the lidar data images in the same test areas. Interestingly, four conventional segmentation methods performed significantly worse than CIR-based and lidar-based instance segmentations. Additionally, the results revealed that tree crowns were more accurately segmented by instance segmentation. All in all, the results highlight the practical potential of the two deep learning-based tree segmentation methods, especially in comparison to baseline methods.

Estimation of Fv/Fm in Spring Wheat Using UAV-Based Multispectral and RGB Imagery with Multiple Machine Learning Methods

The maximum quantum efficiency of photosystem II (Fv/Fm) is a widely used indicator of photosynthetic health in plants. Remote sensing of Fv/Fm using MS (multispectral) and RGB imagery has the potential to enable high-throughput screening of plant health in agricultural and ecological applications. This study aimed to estimate Fv/Fm in spring wheat at an experimental base in Hanghou County, Inner Mongolia, from 2020 to 2021. RGB and MS images were obtained at the wheat flowering stage using a Da-Jiang Phantom 4 multispectral drone. A total of 51 vegetation indices were constructed, and the measured Fv/Fm of wheat on the ground was obtained simultaneously using a Handy PEA plant efficiency analyzer. The performance of 26 machine learning algorithms for estimating Fv/Fm using RGB and multispectral imagery was compared. The findings revealed that a majority of the multispectral vegetation indices and approximately half of the RGB vegetation indices demonstrated a strong correlation with Fv/Fm, as evidenced by an absolute correlation coefficient greater than 0.75. The Gradient Boosting Regressor (GBR) was the optimal estimation model for RGB, with the important features being RGBVI and ExR. The Huber model was the optimal estimation model for MS, with the important feature being MSAVI2. The Automatic Relevance Determination (ARD) was the optimal estimation model for the combination (RGB + MS), with the important features being SIPI, ExR, and VEG. The highest accuracy was achieved using the ARD model for estimating Fv/Fm with RGB + MS vegetation indices on the test sets (Test set MAE = 0.019, MSE = 0.001, RMSE = 0.024, R2 = 0.925, RMSLE = 0.014, MAPE = 0.026). The combined analysis suggests that extracting vegetation indices (SIPI, ExR, and VEG) from RGB and MS remote images by UAV as input variables of the model and using the ARD model can significantly improve the accuracy of Fv/Fm estimation at flowering stage. This approach provides new technical support for rapid and accurate monitoring of Fv/Fm in spring wheat in the Hetao Irrigation District.

Winter wheat yield prediction using convolutional neural networks and UAV-based multispectral imagery

Winter wheat yield prediction using convolutional neural networks and UAV-based multispectral imagery

Detection of the Monitoring Window for Pine Wilt Disease Using Multi-Temporal UAV-Based Multispectral Imagery and Machine Learning Algorithms

Pine wilt disease (PWD) has caused huge economic and environmental losses since it invaded China. Although early monitoring is an effective way to control this hazard, the monitoring window for the early stage is hard to identify, and varies in different hosts and environments. We used UAV-based multispectral images of Pinus thunbergii forest in East China to identify the change in the number of infected trees in each month of the growing season. We built classification models to detect different PWD infection stages by testing three machine learning algorithms—random forest, support vector machine, and linear discriminant analysis—and identified the best monitoring period for each infection stage (namely, green attack, early, middle, and late). From the obtained results, the early monitoring window period was determined to be in late July, whereas the monitoring window for middle and late PWD stages ranged from mid-August to early September. We also identified four important vegetation indices to monitor each infection stage. In conclusion, this study demonstrated the effectiveness of using machine learning algorithms to analyze multitemporal multispectral data to establish a window for early monitoring of pine wilt disease infestation. The results could provide a reference for future research and guidance for the control of pine wilt disease.

Transferability of Models for Predicting Rice Grain Yield from Unmanned Aerial Vehicle (UAV) Multispectral Imagery across Years, Cultivars and Sensors

Timely and accurate prediction of crop yield prior to harvest is vital for precise agricultural management. Unmanned aerial vehicles (UAVs) provide a fast and convenient approach to crop yield prediction, but most existing crop yield models have rarely been tested across different years, cultivars and sensors. This has limited the ability of these yield models to be transferred to other years or regions or to be potentially used with data from other sensors. In this study, UAV-based multispectral imagery was used to predict rice grain yield at the booting and filling stages from four field experiments, involving three years, two rice cultivars, and two UAV sensors. Reflectance and texture features were extracted from the UAV imagery, and vegetation indices (VIs) and normalized difference texture indices (NDTIs) were computed. The models were independently validated to test the stability and transferability across years, rice cultivars, and sensors. The results showed that the red edge normalized difference texture index (RENDTI) was superior to other texture indices and vegetation indices for model regression with grain yield in most cases. However, the green normalized difference texture index (GNDTI) achieved the highest prediction accuracy in model validation across rice cultivars and sensors. The yield prediction model of Japonica rice achieved stronger transferability to Indica rice with root mean square error (RMSE), bias, and relative RMSE (RRMSE) of 1.16 t/ha, 0.08, and 11.04%, respectively. Model transferability was improved significantly between different sensors after band correction with a decrease of 15.05–59.99% in RRMSE. Random forest (RF) was found to be a good solution to improve the model transferability across different years and cultivars and obtained the highest prediction accuracy with RMSE, bias, and RRMSE of 0.94 t/ha, −0.21, and 9.37%, respectively. This study provides a valuable reference for crop yield prediction when existing models are transferred across different years, cultivars and sensors.

Prediction of maize crop coefficient from UAV multisensor remote sensing using machine learning methods

In the upcoming irrigation management in agricultural production, accurate mapping of crop water consumption with a high spatial and temporal resolution at a farm scale is needed. In this study, we developed models for crop coefficients (Kc) estimation using unmanned aerial vehicle (UAV) remote sensing and machine learning (ML) techniques for irrigated maize in a semi-arid region in Northwest China. Kc values were calculated using a procedure given in FAO56 manual using field measurements. Multispectral vegetation indices (VIs), vegetation fraction (VF), thermal-based VIs, and texture information (TI) were derived from UAV-based multispectral, RGB, and thermal infrared imagery, respectively. These remotely sensed variables and their combinations were used to develop prediction models using six ML algorithms (linear regression-LR, polynomial regression-PR, exponential regression-ER, random forest regression-RFR, support vector regression-SVR, and deep neural network-DNN). Among these models, the RFR with the highest accuracy (R2 = 0.69, RMSE = 0.1019) was recommended to estimate maize Kc. The multispectral and thermal-based VIs and texture of the near-infrared band had greater contributions than RGB-based VF and TI in the Kc-RFR model under different irrigation treatments. Furthermore, the maize Kc-RFR prediction model had high accuracy in estimating cumulative evapotranspiration (R2 = 0.89, RMSE = 15.0 mm/stage) during different growth stages and daily soil water content (R2 = 0.85, RMSE = 0.0089 m3/m3) in the root zone. These results show that the integration of UAV remote sensing and ML provides a promising tool to help farmers make decisions using timely mapped crop water consumption, especially under water shortages or drought conditions.

Digital mapping of coffee ripeness using UAV-based multispectral imagery

Timely and accurate monitoring of coffee ripeness is essential for harvest planning, especially in mountainous areas where the harvest is performed manually due to the limited use of agricultural mechanization. The increasing temporal and spatial resolutions of remote sensing based on low-altitude unmanned aerial vehicles (UAV) provides a feasible way to monitor the fruit ripeness variability. Due to these facts, this study was aimed to: (1) predict the fruit ripeness using spectral and textural variables; and (2) to determine the best variables for developing spatio-temporal variability maps of the fruit ripeness. To do so, an experiment with six arabica coffee fields was set up. During the coffee ripeness stage in the 2018–2019 and 2020–2021 seasons, seven flights were carried out using a quadcopter equipped with a five-band multispectral camera. After that, 12 spectral and 64 textural variables composed of bands and vegetation indices were obtained. For the same period, the percentage of unripe fruits (fruit ripeness) was determined using an irregular grid on each field. Then, the fruit ripeness was predicted with six machine learning (ML) algorithms using as input (1) the spectral variables and (2) the combination of spectral and textural variables. Among the evaluated ML algorithms, the random forest presented the higher accuracy, in which the model using the spectral and textural variables (r2 = 0.71 and RMSE = 11.47%) presented superior performance than the model based solely on spectral variables (r2 = 0.67 and RMSE = 12.09%). Finally, this study demonstrated the feasibility of using spectral and textural variables derived from UAV imagery for mapping and monitoring the spatiotemporal changes in the fruit ripeness at a fine scale.

Identification of symptoms related to potato Verticillium wilt from UAV-based multispectral imagery using an ensemble of gradient boosting machines

Potato Verticillium wilt is a harmful and widespread disease which causes losses between 20% and 50% of crop production in Colombia. Diagnostics of Verticillium wilt in potato crops is challenging due that the known symptoms are often associated with other diseases. While recent studies have explored the potential of high spatial resolution imagery for detection of diseases and physiological disorders in potato crops, results are not-conclusive yet. Thus, to contribute to improving identification of Verticillium wilt symptoms, we evaluate a machine learning method to classify disease severity from multidate UAV-based multispectral images. It uses an ensemble of gradient boosting machines (GBMs) to differentiate, at grid level, six levels of Verticillim wilt disease severity. The proposed method was tested in a commercial potato crop in Cundinamarca, one of the top potato production regions in Colombia, using multispectral images acquired in four different field campaigns conducted between 99 and 141 days after planting. Accuracy assessment of results indicates that classification of severity levels of Verticillium wilt infection was satisfactory with F1 scores of 82% for the training dataset and 84% for the testing dataset. This study results shows both potential and challenges of using machine learning ensemble methods for identification of Verticillium wilt symptoms in potato crops from high spatial resolution multispectral images.

A new comprehensive index for monitoring maize lodging severity using UAV-based multi-spectral imagery

Lodging significantly reduces crop yield and grain quality. Timely and accurately monitoring crop lodging severity to help farmers to adjust the field management and settle reasonable insurance claim. The purpose of this paper is to effectively monitor maize lodging severity using unmanned aerial vehicle (UAV) with a multi-spectral camera. Considering the plant height (PH), lodging angle (LA) and SPAD, each agronomic trait was transformed into a membership matrix by giving each factor a weight. The comprehensive superiority of each evaluation result based on agronomic trait (PH, LA, SPAD) was calculated by using the fuzzy comprehensive evaluation (FCE) method, and the comprehensive lodging evaluation index (CLEI) was constructed. The relationships between the spectral reflectance (SR), texture feature (TF), vegetation index (VI) and PH, LA, SPAD, CLEI were established. The CLEI was calculated and mapped by using the sensitive features derived from UAV-based multi-spectral imagery. According to the distribution of CLEI, the research area was divided into five grades, including non-lodging (NL), light lodging (LL), moderate lodging (ML), severe lodging (SL) and very severe lodging (VSL). The results showed that among the CLEI models constructed by single feature, VI performed best and the R2 of cross-validation was 0.66 which was higher than agronomic trait models (the R2 of PH, LA, and SPAD models were 0.56, 0.58, 0.41, respectively). The R2 and nRMSE of cross-validation of the CLEI model constructed by the top three sensitive features (CIre, NDRE, Re_Mea) were 0.80 and 23.78%, while the R2 of the PH, LA, and SPAD models were 0.74, 0.71, 0.46, respectively. The accuracy of the new comprehensive index (CLEI) model was better than that of single agronomic trait (PH, LA and SPAD) model. The CLEI proposed in this paper using UAV-based multi-spectral imagery can effectively monitor the lodging severity of maize.

Identification of Male and Female Parents for Hybrid Rice Seed Production Using UAV-Based Multispectral Imagery

Identifying and extracting male and female parent of hybrid rice and then accurately judging the spikelet flowering of male parents is the basis of hybrid rice pollination. Currently, male parent flowering information extraction for hybrid rice is basically obtained by manual recognition. In this study, remote sensing images of parental rice fields were obtained with a multispectral camera carried by a UAV (Umanned Aerial Vehicle). Six kinds of visible light vegetation indices and four kinds of multispectral vegetation indices, together with two classification methods, pixel-based supervised classification and sample-based object-oriented classification, were applied to identify the male and female parents of hybrid rice, after which the accuracies of the methods were compared. The results showed that the visible vegetation index had a better effect in pixel-based supervised classification. The kappa coefficient of ExGR (Excess Green minus Excess Red index) classification was 0.9256 and the total accuracy was 0.9552. The extraction accuracy was higher than that of the other vegetation indices and object-oriented classification. In pixel-based supervised classification, the maximum likelihood method achieved the highest identification accuracy and shortest calculation time. Taking the remote sensing images obtained with a UAV as a data source, maximum likelihood supervised classification based on ExGR index can more effectively and quickly identify the field information of male and female parents of hybrid rice so as to provide a reference for determining optimal pollination timing for hybrid rice in large-scale seed production farms.

An assessment of background removal approaches for improved estimation of rice leaf nitrogen concentration with unmanned aerial vehicle multispectral imagery at various observation times

Background effect is a crucial limitation for the monitoring of leaf nitrogen concentration (LNC) in crops with unmanned aerial vehicle (UAV) multispectral imagery. Some background removal approaches have been developed for improve the estimation of LNC, but their performances are not compared in one study and it is unclear whether they are sensitive to the observation time of UAV imagery. This study evaluated three background removal approaches, i.e., the soil-adjusted vegetation index (SAVI) approach, the green pixel vegetation index approach (GPVI) and abundance adjusted vegetation index (AAVI), for estimating rice LNC from UAV-based multispectral imagery at individual and across growth stages as well as different observation times of the day. The red edge chlorophyll index (CIre) was chosen as the common basis for the last two approaches. In particular, the AAVI approach was refined with a higher number of endmembers and automated endmember extraction, and further evaluated for assessing the effect of separating sunlit components from shaded components of the canopy. Our results demonstrated that the vegetation indices (VIs) for off-noon observation times showed better relationships with LNC than those for noon at individual and across growth stages. Compared to both SAVI and CIre-green, the AACIre for all pixels (AACIre-all) exhibited the weakest sensitivity to observation time and yielded the best relationships for single-stage (jointing: r2=0.70, booting: r2=0.76, heading: r2=0.70) and across-stage (r2=0.66) models. Among the AAVIs derived from three categories of pixels, the AACIre-sunlit (R2 =0.90, RMSE=0.17%, Bias=0.03%) outperformed AACIre-all (R2 =0.85, RMSE=0.23%, Bias=0.08%) and then AACIre-shaded (R2 =0.38, RMSE=0.49%, Bias=0.40%) remarkably for the estimation accuracy of LNC. This study suggests that the refined AAVI approach has great value in reducing the background effect for more accurate monitoring of growth parameters and could be extended to other crops and regions for improved precision crop management and field-based high-throughput phenotyping.