Estimating Maize Above-Ground Biomass Using 3D Point Clouds of Multi-Source Unmanned Aerial Vehicle Data at Multi-Spatial Scales

Spatial differentiation of carbon emissions from energy consumption based on machine learning algorithm: A case study during 2015-2020 in Shaanxi, China

Carbon emissions resulting from energy consumption have become a pressing issue for governments worldwide. Accurate estimation of carbon emissions using satellite remote sensing data has become a crucial research problem. Previous studies relied on statistical regression models that failed to capture the complex nonlinear relationships between carbon emissions and characteristic variables. In this study, we propose a machine learning algorithm for carbon emissions, a Bayesian optimized XGboost regression model, using multi-year energy carbon emission data and nighttime lights (NTL) remote sensing data from Shaanxi Province, China. Our results demonstrate that the XGboost algorithm outperforms linear regression and four other machine learning models, with an R2 of 0.906 and RMSE of 5.687. We observe an annual increase in carbon emissions, with high-emission counties primarily concentrated in northern and central Shaanxi Province, displaying a shift from discrete, sporadic points to contiguous, extended spatial distribution. Spatial autocorrelation clustering reveals predominantly high-high and low-low clustering patterns, with economically developed counties showing high-emission clustering and economically relatively backward counties displaying low-emission clustering. Our findings show that the use of NTL data and the XGboost algorithm can estimate and predict carbon emissions more accurately and provide a complementary reference for satellite remote sensing image data to serve carbon emission monitoring and assessment. This research provides an important theoretical basis for formulating practical carbon emission reduction policies and contributes to the development of techniques for accurate carbon emission estimation using remote sensing data.

A Comprehensive Review of LiDAR Applications in Crop Management for Precision Agriculture

Precision agriculture has revolutionized crop management and agricultural production, with LiDAR technology attracting significant interest among various technological advancements. This extensive review examines the various applications of LiDAR in precision agriculture, with a particular emphasis on its function in crop cultivation and harvests. The introduction provides an overview of precision agriculture, highlighting the need for effective agricultural management and the growing significance of LiDAR technology. The prospective advantages of LiDAR for increasing productivity, optimizing resource utilization, managing crop diseases and pesticides, and reducing environmental impact are discussed. The introduction comprehensively covers LiDAR technology in precision agriculture, detailing airborne, terrestrial, and mobile systems along with their specialized applications in the field. After that, the paper reviews the several uses of LiDAR in agricultural cultivation, including crop growth and yield estimate, disease detection, weed control, and plant health evaluation. The use of LiDAR for soil analysis and management, including soil mapping and categorization and the measurement of moisture content and nutrient levels, is reviewed. Additionally, the article examines how LiDAR is used for harvesting crops, including its use in autonomous harvesting systems, post-harvest quality evaluation, and the prediction of crop maturity and yield. Future perspectives, emergent trends, and innovative developments in LiDAR technology for precision agriculture are discussed, along with the critical challenges and research gaps that must be filled. The review concludes by emphasizing potential solutions and future directions for maximizing LiDAR's potential in precision agriculture. This in-depth review of the uses of LiDAR gives helpful insights for academics, practitioners, and stakeholders interested in using this technology for effective and environmentally friendly crop management, which will eventually contribute to the development of precision agricultural methods.

Accurate estimation of fractional vegetation cover for winter wheat by integrated unmanned aerial systems and satellite images

Accurate estimation of fractional vegetation cover (FVC) is essential for crop growth monitoring. Currently, satellite remote sensing monitoring remains one of the most effective methods for the estimation of crop FVC. However, due to the significant difference in scale between the coarse resolution of satellite images and the scale of measurable data on the ground, there are significant uncertainties and errors in estimating crop FVC. Here, we adopt a Strategy of Upscaling-Downscaling operations for unmanned aerial systems (UAS) and satellite data collected during 2 growing seasons of winter wheat, respectively, using backpropagation neural networks (BPNN) as support to fully bridge this scale gap using highly accurate the UAS-derived FVC (FVCUAS) to obtain wheat accurate FVC. Through validation with an independent dataset, the BPNN model predicted FVC with an RMSE of 0.059, which is 11.9% to 25.3% lower than commonly used Long Short-Term Memory (LSTM), Random Forest Regression (RFR), and traditional Normalized Difference Vegetation Index-based method (NDVI-based) models. Moreover, all those models achieved improved estimation accuracy with the Strategy of Upscaling-Downscaling, as compared to only upscaling UAS data. Our results demonstrate that: (1) establishing a nonlinear relationship between FVCUAS and satellite data enables accurate estimation of FVC over larger regions, with the strong support of machine learning capabilities. (2) Employing the Strategy of Upscaling-Downscaling is an effective strategy that can improve the accuracy of FVC estimation, in the collaborative use of UAS and satellite data, especially in the boundary area of the wheat field. This has significant implications for accurate FVC estimation for winter wheat, providing a reference for the estimation of other surface parameters and the collaborative application of multisource data.

Aboveground biomass estimation of wetland vegetation at the species level using unoccupied aerial vehicle RGB imagery

Wetland vegetation biomass is an essential indicator of wetland health, and its estimation has become an active area of research. Zizania latifolia (Z. latifolia) is the dominant species of emergent vegetation in Honghu Wetland, and monitoring its aboveground biomass (AGB) can provide a scientific basis for the protection and restoration of this and other wetlands along the Yangtze River. This study aimed to develop a method for the AGB estimation of Z. latifolia in Honghu Wetland using high-resolution RGB imagery acquired from an unoccupied aerial vehicle (UAV). The spatial distribution of Z. latifolia was first extracted through an object-based classification method using the field survey data and UAV RGB imagery. Linear, quadratic, exponential and back propagation neural network (BPNN) models were constructed based on 17 vegetation indices calculated from RGB images to invert the AGB. The results showed that: (1) The visible vegetation indices were significantly correlated with the AGB of Z. latifolia. The absolute value of the correlation coefficient between the AGB and CIVE was 0.87, followed by ExG (0.866) and COM2 (0.837). (2) Among the linear, quadratic, and exponential models, the quadric model based on CIVE had the highest inversion accuracy, with a validation R² of 0.37, RMSE and MAE of 853.76 g/m² and 671.28 g/m², respectively. (3) The BPNN model constructed with eight factors correlated with the AGB had the best inversion effect, with a validation R² of 0.68, RMSE and MAE of 732.88 g/m² and 583.18 g/m², respectively. ​Compared to the quadratic model constructed by CIVE, the BPNN model achieved better results, with a reduction of 120.88 g/m² in RMSE and 88.10 g/m² in MAE. This study indicates that using UAV-based RGB images and the BPNN model provides an effective and accurate technique for the AGB estimation of dominant wetland species, making it possible to efficiently and dynamically monitor wetland vegetation cost-effectively.

Growth Monitoring and Yield Estimation of Maize Plant Using Unmanned Aerial Vehicle (UAV) in a Hilly Region

More than 66% of the Nepalese population has been actively dependent on agriculture for their day-to-day living. Maize is the largest cereal crop in Nepal, both in terms of production and cultivated area in the hilly and mountainous regions of Nepal. The traditional ground-based method for growth monitoring and yield estimation of maize plant is time consuming, especially when measuring large areas, and may not provide a comprehensive view of the entire crop. Estimation of yield can be performed using remote sensing technology such as Unmanned Aerial Vehicles (UAVs), which is a rapid method for large area examination, providing detailed data on plant growth and yield estimation. This research paper aims to explore the capability of UAVs for plant growth monitoring and yield estimation in mountainous terrain. A multi-rotor UAV with a multi-spectral camera was used to obtain canopy spectral information of maize in five different stages of the maize plant life cycle. The images taken from the UAV were processed to obtain the result of the orthomosaic and the Digital Surface Model (DSM). The crop yield was estimated using different parameters such as Plant Height, Vegetation Indices, and biomass. A relationship was established in each sub-plot which was further used to calculate the yield of an individual plot. The estimated yield obtained from the model was validated against the ground-measured yield through statistical tests. A comparison of the Normalized Difference Vegetation Index (NDVI) and the Green-Red Vegetation Index (GRVI) indicators of a Sentinel image was performed. GRVI was found to be the most important parameter and NDVI was found to be the least important parameter for yield determination besides their spatial resolution in a hilly region.

Cumulative temporal vegetation indices from unoccupied aerial systems allow maize (Zea mays L.) hybrid yield to be estimated across environments with fewer flights

Unoccupied aerial systems (UAS) based high throughput phenotyping studies require further investigation to combine different environments and planting times into one model. Here 100 elite breeding hybrids of maize (Zea mays L.) were evaluated in two environment trials-one with optimal planting and irrigation (IHOT), and one dryland with delayed planting (DHOT). RGB (Red-Green-Blue) based canopy height measurement (CHM) and vegetation indices (VIs) were estimated from a UAS platform. Time series and cumulative VIs, by both summation (ΣVI-SUMs) and area under the curve (ΣVI-AUCs), were fit via machine learning regression modeling (random forest, linear, ridge, lasso, elastic net regressions) to estimate grain yield. VIs were more valuable predictors of yield to combine different environments than CHM. Time series VIs and CHM produced high accuracies (~68-72%), but inconsistent models. A little sacrifice in accuracy (~60-65%) produced consistent models using ΣVI-SUMs and CHM during pre-reproductive vegetative growth. Absence of VIs produced poorer accuracies (by about ~5-10%). Normalized difference type VIs produced maximum accuracies, and flowering times were the best times for UAS data acquisition. This study suggests that the best yielding varieties can be accurately predicted in new environments at or before flowering when combining multiple temporal flights and predictors.

Application of UAV Multisensor Data and Ensemble Approach for High-Throughput Estimation of Maize Phenotyping Traits

High-throughput estimation of phenotypic traits from UAV (unmanned aerial vehicle) images is helpful to improve the screening efficiency of breeding maize. Accurately estimating phenotyping traits of breeding maize at plot scale helps to promote gene mining for specific traits and provides a guarantee for accelerating the breeding of superior varieties. Constructing an efficient and accurate estimation model is the key to the application of UAV-based multiple sensors data. This study aims to apply the ensemble learning model to improve the feasibility and accuracy of estimating maize phenotypic traits using UAV-based red-green-blue (RGB) and multispectral sensors. The UAV images of four growth stages were obtained, respectively. The reflectance of visible light bands, canopy coverage, plant height (PH), and texture information were extracted from RGB images, and the vegetation indices were calculated from multispectral images. We compared and analyzed the estimation accuracy of single-type feature and multiple features for LAI (leaf area index), fresh weight (FW), and dry weight (DW) of maize. The basic models included ridge regression (RR), support vector machine (SVM), random forest (RF), Gaussian process (GP), and K-neighbor network (K-NN). The ensemble learning models included stacking and Bayesian model averaging (BMA). The results showed that the ensemble learning model improved the accuracy and stability of maize phenotypic traits estimation. Among the features extracted from UAV RGB images, the highest accuracy was obtained by the combination of spectrum, structure, and texture features. The model had the best accuracy constructed using all features of two sensors. The estimation accuracies of ensemble learning models, including stacking and BMA, were higher than those of the basic models. The coefficient of determination (R 2) of the optimal validation results were 0.852, 0.888, and 0.929 for LAI, FW, and DW, respectively. Therefore, the combination of UAV-based multisource data and ensemble learning model could accurately estimate phenotyping traits of breeding maize at plot scale.

Estimation of potato above-ground biomass based on unmanned aerial vehicle red-green-blue images with different texture features and crop height

Obtaining crop above-ground biomass (AGB) information quickly and accurately is beneficial to farmland production management and the optimization of planting patterns. Many studies have confirmed that, due to canopy spectral saturation, AGB is underestimated in the multi-growth period of crops when using only optical vegetation indices. To solve this problem, this study obtains textures and crop height directly from ultrahigh-ground-resolution (GDS) red-green-blue (RGB) images to estimate the potato AGB in three key growth periods. Textures include a grayscale co-occurrence matrix texture (GLCM) and a Gabor wavelet texture. GLCM-based textures were extracted from seven-GDS (1, 5, 10, 30, 40, 50, and 60 cm) RGB images. Gabor-based textures were obtained from magnitude images on five scales (scales 1-5, labeled S1-S5, respectively). Potato crop height was extracted based on the generated crop height model. Finally, to estimate potato AGB, we used (i) GLCM-based textures from different GDS and their combinations, (ii) Gabor-based textures from different scales and their combinations, (iii) all GLCM-based textures combined with crop height, (iv) all Gabor-based textures combined with crop height, and (v) two types of textures combined with crop height by least-squares support vector machine (LSSVM), extreme learning machine, and partial least squares regression techniques. The results show that (i) potato crop height and AGB first increase and then decrease over the growth period; (ii) GDS and scales mainly affect the correlation between GLCM- and Gabor-based textures and AGB; (iii) to estimate AGB, GLCM-based textures of GDS1 and GDS30 work best when the GDS is between 1 and 5 cm and 10 and 60 cm, respectively (however, estimating potato AGB based on Gabor-based textures gradually deteriorates as the Gabor convolution kernel scale increases); (iv) the AGB estimation based on a single-type texture is not as good as estimates based on multi-resolution GLCM-based and multiscale Gabor-based textures (with the latter being the best); (v) different forms of textures combined with crop height using the LSSVM technique improved by 22.97, 14.63, 9.74, and 8.18% (normalized root mean square error) compared with using only all GLCM-based textures, all Gabor-based textures, the former combined with crop height, and the latter combined with crop height, respectively. Therefore, different forms of texture features obtained from RGB images acquired from unmanned aerial vehicles and combined with crop height improve the accuracy of potato AGB estimates under high coverage.

Research hotspots and frontiers in agricultural multispectral technology: Bibliometrics and scientometrics analysis of the Web of Science

Multispectral technology has a wide range of applications in agriculture. By obtaining spectral information during crop production, key information such as growth, pests and diseases, fertilizer and pesticide application can be determined quickly, accurately and efficiently. The scientific analysis based on Web of Science aims to understand the research hotspots and areas of interest in the field of agricultural multispectral technology. The publications related to agricultural multispectral research in agriculture between 2002 and 2021 were selected as the research objects. The softwares of CiteSpace, VOSviewer, and Microsoft Excel were used to provide a comprehensive review of agricultural multispectral research in terms of research areas, institutions, influential journals, and core authors. Results of the analysis show that the number of publications increased each year, with the largest increase in 2019. Remote sensing, imaging technology, environmental science, and ecology are the most popular research directions. The journal Remote Sensing is one of the most popular publishers, showing a high publishing potential in multispectral research in agriculture. The institution with the most research literature and citations is the USDA. In terms of the number of papers, Mtanga is the author with the most published articles in recent years. Through keyword co-citation analysis, it is determined that the main research areas of this topic focus on remote sensing, crop classification, plant phenotypes and other research areas. The literature co-citation analysis indicates that the main research directions concentrate in vegetation index, satellite remote sensing applications and machine learning modeling. There is still a lot of room for development of multi-spectrum technology. Further development can be carried out in the areas of multi-device synergy, spectral fusion, airborne equipment improvement, and real-time image processing technology, which will cooperate with each other to further play the role of multi-spectrum in agriculture and promote the development of agriculture.

Classification of Toona sinensis Young Leaves Using Machine Learning and UAV-Borne Hyperspectral Imagery

Rapid and accurate distinction between young and old leaves of Toona sinensis in the wild is of great significance to the selection of T. sinensis varieties and the evaluation of relative yield. In this study, UAV hyperspectral imaging technology was used to obtain canopy hyperspectral data of biennial seedlings of different varieties of T. sinensis to distinguish young and old leaves. Five classification models were trained, namely Random Forest (RF), Artificial Neural Network (ANN), Decision Tree (DT), Partial Least Squares Discriminant Analysis (PLSDA), and Support Vector Machine (SVM). Raw spectra and six preprocessing methods were used to fit the best classification model. Satisfactory accuracy was obtained from all the five models using the raw spectra. The SVM model showed good performance on raw spectra and all preprocessing methods, and yielded higher accuracy, sensitivity, precision, and specificity than other models. In the end, the SVM model based on the raw spectra produced the most reliable and robust prediction results (99.62% accuracy and 99.23% sensitivity on the validation set only, and 100.00% for the rest). Three important spectral regions of 422.7~503.2, 549.2, and 646.2~687.2 nm were found to be highly correlated with the identification of young leaves of T. sinensis. In this study, a fast and effective method for identifying young leaves of T. sinensis was found, which provided a reference for the rapid identification of young leaves of T. sinensis in the wild.

Construction of Remote Sensing Model of Fresh Corn Biomass Based on Neural Network

Corn has a high yield and is widely used. Therefore, developing corn production and accurately estimating corn biomass yield are of great significance to improving people's lives, developing rural economy and climate issues. In this paper, a 3-layer BP neural network model is constructed by using the LM algorithm as the training algorithm of the corn biomass BP network model. From the three aspects of elevation, slope, and aspect, combined with the BP neural network model of corn biomass, the spatial distribution of corn biomass in the study area is analyzed. The results showed that the average biomass per unit area of maize increased with the increase in altitude below 1000 m. There are relatively more human activities in low altitude areas, which are more active in forestry production. The best planting altitude of corn is 0 ∼ 1000 m. When the altitude is higher than 1000 m, the corn biomass gradually decreases. In terms of slope, if the slope is lower than 15°, the biomass of maize increases with the increase in slope. If the slope is lower than 15°, the biomass of maize decreases gradually with the increase in slope. The biomass of maize on sunny slope was higher than that on shady slope.

Estimation of Above-Ground Biomass of Winter Wheat Based on Consumer-Grade Multi-Spectral UAV

One of the problems of optical remote sensing of crop above-ground biomass (AGB) is that vegetation indices (VIs) often saturate from the middle to late growth stages. This study focuses on combining VIs acquired by a consumer-grade multiple-spectral UAV and machine learning regression techniques to (i) determine the optimal time window for AGB estimation of winter wheat and to (ii) determine the optimal combination of multi-spectral VIs and regression algorithms. UAV-based multi-spectral data and manually measured AGB of winter wheat, under five nitrogen rates, were obtained from the jointing stage until 25 days after flowering in the growing season 2020/2021. Forty-four multi-spectral VIs were used in the linear regression (LR), partial least squares regression (PLSR), and random forest (RF) models in this study. Results of LR models showed that the heading stage was the most suitable stage for AGB prediction, with R2 values varying from 0.48 to 0.93. Three PLSR models based on different datasets performed differently in estimating AGB in the training dataset (R2 = 0.74~0.92, RMSE = 0.95~2.87 t/ha, MAE = 0.75~2.18 t/ha, and RPD = 2.00~3.67) and validation dataset (R2 = 0.50~0.75, RMSE = 1.56~2.57 t/ha, MAE = 1.44~2.05 t/ha, RPD = 1.45~1.89). Compared with PLSR models, the performance of the RF models was more stable in the prediction of AGB in the training dataset (R2 = 0.95~0.97, RMSE = 0.58~1.08 t/ha, MAE = 0.46~0.89 t/ha, and RPD = 3.95~6.35) and validation dataset (R2 = 0.83~0.93, RMSE = 0.93~2.34 t/ha, MAE = 0.72~2.01 t/ha, RPD = 1.36~3.79). Monitoring AGB prior to flowering was found to be more effective than post-flowering. Moreover, this study demonstrates that it is feasible to estimate AGB for multiple growth stages of winter wheat by combining the optimal VIs and PLSR and RF models, which overcomes the saturation problem of using individual VI-based linear regression models.

Using Unmanned Aerial Vehicle-Based Multispectral Image Data to Monitor the Growth of Intercropping Crops in Tea Plantation

Aboveground biomass (AGB) and leaf area index (LAI) are important indicators to measure crop growth and development. Rapid estimation of AGB and LAI is of great significance for monitoring crop growth and agricultural site-specific management decision-making. As a fast and non-destructive detection method, unmanned aerial vehicle (UAV)-based imaging technologies provide a new way for crop growth monitoring. This study is aimed at exploring the feasibility of estimating AGB and LAI of mung bean and red bean in tea plantations by using UAV multispectral image data. The spectral parameters with high correlation with growth parameters were selected using correlation analysis. It was found that the red and near-infrared bands were sensitive bands for LAI and AGB. In addition, this study compared the performance of five machine learning methods in estimating AGB and LAI. The results showed that the support vector machine (SVM) and backpropagation neural network (BPNN) models, which can simulate non-linear relationships, had higher accuracy in estimating AGB and LAI compared with simple linear regression (LR), stepwise multiple linear regression (SMLR), and partial least-squares regression (PLSR) models. Moreover, the SVM models were better than other models in terms of fitting, consistency, and estimation accuracy, which provides higher performance for AGB (red bean: R 2 = 0.811, root-mean-square error (RMSE) = 0.137 kg/m2, normalized RMSE (NRMSE) = 0.134; mung bean: R 2 = 0.751, RMSE = 0.078 kg/m2, NRMSE = 0.100) and LAI (red bean: R 2 = 0.649, RMSE = 0.36, NRMSE = 0.123; mung bean: R 2 = 0.706, RMSE = 0.225, NRMSE = 0.081) estimation. Therefore, the crop growth parameters can be estimated quickly and accurately using the models established by combining the crop spectral information obtained by the UAV multispectral system using the SVM method. The results of this study provide valuable practical guidelines for site-specific tea plantations and the improvement of their ecological and environmental benefits.

Comparison of Multi-Methods for Identifying Maize Phenology Using PhenoCams

Accurately identifying the phenology of summer maize is crucial for both cultivar breeding and fertilizer controlling in precision agriculture. In this study, daily RGB images covering the entire growth of summer maize were collected using phenocams at sites in Shangqiu (2018, 2019 and 2020) and Nanpi (2020) in China. Four phenological dates, including six leaves, booting, heading and maturity of summer maize, were pre-defined and extracted from the phenocam-based images. The spectral indices, textural indices and integrated spectral and textural indices were calculated using the improved adaptive feature-weighting method. The double logistic function, harmonic analysis of time series, Savitzky–Golay and spline interpolation were applied to filter these indices and pre-defined phenology was identified and compared with the ground observations. The results show that the DLF achieved the highest accuracy, with the coefficient of determination (R2) and the root-mean-square error (RMSE) being 0.86 and 9.32 days, respectively. The new index performed better than the single usage of spectral and textural indices, of which the R2 and RMSE were 0.92 and 9.38 days, respectively. The phenological extraction using the new index and double logistic function based on the PhenoCam data was effective and convenient, obtaining high accuracy. Therefore, it is recommended the adoption of the new index by integrating the spectral and textural indices for extracting maize phenology using PhenoCam data.

Estimation of Plant Height and Aboveground Biomass of Toona sinensis under Drought Stress Using RGB-D Imaging

Rapid and accurate plant growth and biomass estimation is essential for formulating and implementing targeted forest cultivation measures. In this study, RGB-D imaging technology was used to obtain the RGB and depth imaging data for a Toona sinensis seedling canopy to estimate plant growth and aboveground biomass (AGB). Three hundred T. sinensis seedlings from 20 varieties were planted under five different drought stress treatments. The U-Net model was applied first to achieve highly accurate segmentation of plants from complex backgrounds. Simple linear regression (SLR) was used for plant height prediction, and the other three models, including multivariate linear (ML), random forest (RF) and multilayer perceptron (MLP) regression, were applied to predict the AGB and compared for optimal model selection. The results showed that the SLR model yields promising and reliable results for the prediction of plant height, with R2 and RMSE values of 0.72 and 1.89 cm, respectively. All three regression methods perform well in the prediction of AGB estimation. MLP yields the highest accuracy in predicting dry and fresh aboveground biomass compared to the other two regression models, with R2 values of 0.77 and 0.83, respectively. The combination of Gray, Green minus red (GMR) and Excess green index (ExG) was identified as the key predictor by RReliefF for predicting dry AGB. GMR was the most important in predicting fresh AGB. This study demonstrated that the merits of RGB-D and machine learning models are effective phenotyping techniques for plant height and AGB prediction, and can be used to assist dynamic responses to drought stress for breeding selection.

Corn Biomass Estimation by Integrating Remote Sensing and Long-Term Observation Data Based on Machine Learning Techniques

The accurate and timely estimation of regional crop biomass at different growth stages is of great importance in guiding crop management decision making. The recent availability of long time series of remote sensing data offers opportunities for crop monitoring. In this paper, four machine learning models, namely random forest (RF), support vector machine (SVM), artificial neural network (ANN), and extreme gradient boosting (XGBoost) were adopted to estimate the seasonal corn biomass based on field observation data and moderate resolution imaging spectroradiometer (MODIS) reflectance data from 2012 to 2019 in the middle reaches of the Heihe River basin, China. Nine variables were selected with the forward feature selection approach from among twenty-seven variables potentially influencing corn biomass: soil-adjusted total vegetation index (SATVI), green ratio vegetation index (GRVI), Nadir_B7 (2105–2155 nm), Nadir_B6 (1628–1652 nm), land surface water index (LSWI), normalized difference vegetation index (NDVI), Nadir_B4 (545–565 nm), and Nadir_B3 (459–479 nm). The results indicated that the corn biomass was suitably estimated (the coefficient of determination (R2) was between 0.72 and 0.78) with the four machine learning models. The XGBoost model performed better than the other three models (R2 = 0.78, root mean squared error (RMSE) = 2.86 t/ha and mean absolute error (MAE) = 1.86 t/ha). Moreover, the RF model was an effective method (R2 = 0.77, RMSE = 2.91 t/ha and MAE = 1.91 t/ha), with a performance comparable to that of the XGBoost model. This study provides a reference for estimating crop biomass from MOD43A4 datasets. In addition, the research demonstrates the potential of machine learning techniques to achieve a relatively accurate estimation of daily corn biomass at a large scale.

Integrating Spectral and Textural Information for Monitoring the Growth of Pear Trees Using Optical Images from the UAV Platform

With the recent developments of unmanned aerial vehicle (UAV) remote sensing, it is possible to monitor the growth condition of trees with the high temporal and spatial resolutions of data. In this study, the daily high-throughput RGB images of pear trees were captured from a UAV platform. A new index was generated by integrating the spectral and textural information using the improved adaptive feature weighting method (IAFWM). The inter-relationships of the air climatic variables and the soil’s physical properties (temperature, humidity and conductivity) were firstly assessed using principal component analysis (PCA). The climatic variables were selected to independently build a linear regression model with the new index when the cumulative variance explained reached 99.53%. The coefficient of determination (R2) of humidity (R2 = 0.120, p = 0.205) using linear regression analysis was the dominating influencing factor for the growth of the pear trees, among the air climatic variables tested. The humidity (%) in 40 cm depth of soil (R2 = 0.642, p < 0.001) using a linear regression coefficient was the largest among climatic variables in the soil. The impact of climatic variables on the soil was commonly greater than those in the air, and the R2 grew larger with the increasing depth of soil. The effects of the fluctuation of the soil-climatic variables on the pear trees’ growth could be detected using the sliding window method (SWM), and the maximum absolute value of coefficients with the corresponding day of year (DOY) of air temperature, soil temperature, soil humidity, and soil conductivity were confirmed as 221, 227, 228, and 226 (DOY), respectively. Thus, the impact of the fluctuation of climatic variables on the growth of pear trees can last 14, 8, 7, and 9 days, respectively. Therefore, it is highly recommended that the adoption of the integrated new index to explore the long-time impact of climate on pears growth be undertaken.

Applications of UAS in Crop Biomass Monitoring: A Review

Biomass is an important indicator for evaluating crops. The rapid, accurate and nondestructive monitoring of biomass is the key to smart agriculture and precision agriculture. Traditional detection methods are based on destructive measurements. Although satellite remote sensing, manned airborne equipment, and vehicle-mounted equipment can nondestructively collect measurements, they are limited by low accuracy, poor flexibility, and high cost. As nondestructive remote sensing equipment with high precision, high flexibility, and low-cost, unmanned aerial systems (UAS) have been widely used to monitor crop biomass. In this review, UAS platforms and sensors, biomass indices, and data analysis methods are presented. The improvements of UAS in monitoring crop biomass in recent years are introduced, and multisensor fusion, multi-index fusion, the consideration of features not directly related to monitoring biomass, the adoption of advanced algorithms and the use of low-cost sensors are reviewed to highlight the potential for monitoring crop biomass with UAS. Considering the progress made to solve this type of problem, we also suggest some directions for future research. Furthermore, it is expected that the challenge of UAS promotion will be overcome in the future, which is conducive to the realization of smart agriculture and precision agriculture.

Validation of UAV-based alfalfa biomass predictability using photogrammetry with fully automatic plot segmentation

Alfalfa is the most widely cultivated forage legume, with approximately 30 million hectares planted worldwide. Genetic improvements in alfalfa have been highly successful in developing cultivars with exceptional winter hardiness and disease resistance traits. However, genetic improvements have been limited for complex economically important traits such as biomass. One of the major bottlenecks is the labor-intensive phenotyping burden for biomass selection. In this study, we employed two alfalfa fields to pave a path to overcome the challenge by using UAV images with fully automatic field plot segmentation for high-throughput phenotyping. The first field was used to develop the prediction model and the second field to validate the predictions. The first and second fields had 808 and 1025 plots, respectively. The first field had three harvests with biomass measured in May, July, and September of 2019. The second had one harvest with biomass measured in September of 2019. These two fields were imaged one day before harvesting with a DJI Phantom 4 pro UAV carrying an additional Sentera multispectral camera. Alfalfa plot images were extracted by GRID software to quantify vegetative area based on the Normalized Difference Vegetation Index. The prediction model developed from the first field explained 50-70% (R Square) of biomass variation in the second field by incorporating four features from UAV images: vegetative area, plant height, Normalized Green-Red Difference Index, and Normalized Difference Red Edge Index. This result suggests that UAV-based, high-throughput phenotyping could be used to improve the efficiency of the biomass selection process in alfalfa breeding programs.

UAV-Based LiDAR for High-Throughput Determination of Plant Height and Above-Ground Biomass of the Bioenergy Grass Arundo donax

Replacing fossil fuels with cellulosic biofuels is a valuable component of reducing the drivers of climate change. This leads to a requirement to develop more productive bioenergy crops, such as Arundo donax with the aim of increasing above-ground biomass (AGB). However, direct measurement of AGB is time consuming, destructive, and labor-intensive. Phenotyping of plant height and biomass production is a bottleneck in genomics- and phenomics-assisted breeding. Here, an unmanned aerial vehicle (UAV) for remote sensing equipped with light detection and ranging (LiDAR) was tested for remote plant height and biomass determination in A. donax. Experiments were conducted on three A. donax ecotypes grown in well-watered and moderate drought stress conditions. A novel UAV-LiDAR data collection and processing workflow produced a dense three-dimensional (3D) point cloud for crop height estimation through a normalized digital surface model (DSM) that acts as a crop height model (CHM). Manual measurements of crop height and biomass were taken in parallel and compared to LiDAR CHM estimates. Stepwise multiple regression was used to estimate biomass. Analysis of variance (ANOVA) tests and pairwise comparisons were used to determine differences between ecotypes and drought stress treatments. We found a significant relationship between the sensor readings and manually measured crop height and biomass, with determination coefficients of 0.73 and 0.71 for height and biomass, respectively. Differences in crop heights were detected more precisely from LiDAR estimates than from manual measurement. Crop biomass differences were also more evident in LiDAR estimates, suggesting differences in ecotypes’ productivity and tolerance to drought. Based on these results, application of the presented UAV-LiDAR workflow will provide new opportunities in assessing bioenergy crop morpho-physiological traits and in delivering improved genotypes for biorefining.

Fusion of Spectral and Structural Information from Aerial Images for Improved Biomass Estimation

Efficient, precise and timely measurement of plant traits is important in the assessment of a breeding population. Estimating crop biomass in breeding trials using high-throughput technologies is difficult, as reproductive and senescence stages do not relate to reflectance spectra, and multiple growth stages occur concurrently in diverse genotypes. Additionally, vegetation indices (VIs) saturate at high canopy coverage, and vertical growth profiles are difficult to capture using VIs. A novel approach was implemented involving a fusion of complementary spectral and structural information, to calculate intermediate metrics such as crop height model (CHM), crop coverage (CC) and crop volume (CV), which were finally used to calculate dry (DW) and fresh (FW) weight of above-ground biomass in wheat. The intermediate metrics, CHM (R2 = 0.81, SEE = 4.19 cm) and CC (OA = 99.2%, Κ = 0.98) were found to be accurate against equivalent ground truth measurements. The metrics CV and CV×VIs were used to develop an effective and accurate linear regression model relationship with DW (R2 = 0.96 and SEE = 69.2 g/m2) and FW (R2 = 0.89 and SEE = 333.54 g/m2). The implemented approach outperformed commonly used VIs for estimation of biomass at all growth stages in wheat. The achieved results strongly support the applicability of the proposed approach for high-throughput phenotyping of germplasm in wheat and other crop species.

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

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.

Non-Destructive Detection of Tea Leaf Chlorophyll Content Using Hyperspectral Reflectance and Machine Learning Algorithms

Tea trees are kept in shaded locations to increase their chlorophyll content, which influences green tea quality. Therefore, monitoring change in chlorophyll content under low light conditions is important for managing tea trees and producing high-quality green tea. Hyperspectral remote sensing is one of the most frequently used methods for estimating chlorophyll content. Numerous studies based on data collected under relatively low-stress conditions and many hyperspectral indices and radiative transfer models show that shade-grown tea performs poorly. The performance of four machine learning algorithms-random forest, support vector machine, deep belief nets, and kernel-based extreme learning machine (KELM)-in evaluating data collected from tea leaves cultivated under different shade treatments was tested. KELM performed best with a root-mean-square error of 8.94 ± 3.05 μg cm-2 and performance to deviation values from 1.70 to 8.04 for the test data. These results suggest that a combination of hyperspectral reflectance and KELM has the potential to trace changes in the chlorophyll content of shaded tea leaves.