Unmanned Aerial Vehicle Multispectral Imagery Research Articles

Estimating Summer Maize Biomass by Integrating UAV Multispectral Imagery with Crop Physiological Parameters

The aboveground biomass (AGB) of summer maize is an important indicator for assessing crop growth status and predicting yield, playing a significant role in agricultural management and decision-making. Traditional on-site measurements of AGB are limited, due to low efficiency and a lack of spatial information. The development of unmanned aerial vehicle (UAV) technology in agriculture offers a rapid and cost-effective method for obtaining crop growth information, but currently, the prediction accuracy of summer maize AGB based on UAVs is limited. This study focuses on the entire growth period of summer maize. Multispectral images of six key growth stages of maize were captured using a DJI Phantom 4 Pro, and color indices and elevation data (DEM) were extracted from these growth stage images. Combining measured data such as summer maize AGB and plant height, which were collected on the ground, and based on the three machine learning algorithms of partial least squares regression (PLSR), random forest (RF), and long short-term memory (LSTM), an input feature analysis of PH was carried out, and a prediction model of summer maize AGB was constructed. The results show that: (1) using unmanned aerial vehicle spectral data (CIS) alone to predict the biomass of summer maize has relatively poor prediction accuracy. Among the three models, the LSTM (CIS) model has the best simulation effect, with a coefficient of determination (R2) ranging from 0.516 to 0.649. The R2 of the RF (CIS) model is 0.446–0.537. The R2 of the PLSR (CIS) model is 0.323–0.401. (2) After adding plant height (PH) data, the accuracy and stability of model prediction significantly improved. R2 increased by about 25%, and both RMSE and NRSME decreased by about 20%. Among the three prediction models, the LSTM (PH + CIS) model had the best performance, with R2 = 0.744, root mean square error (RSME) = 4.833 g, and normalized root mean square error (NRSME) = 0.107. Compared to using only color indices (CIS) as the model input, adding plant height (PH) significantly enhances the prediction effect of AGB (aboveground biomass) prediction in key growth periods of summer maize. This method can serve as a reference for the precise monitoring of crop biomass status through remote sensing with unmanned aerial vehicles.

Enhancing Turbidity Predictions in Coastal Environments by Removing Obstructions from Unmanned Aerial Vehicle Multispectral Imagery Using Inpainting Techniques

High-resolution remote sensing of turbidity in the coastal environment with unmanned aerial vehicles (UAVs) can be adversely affected by the presence of obstructions of vessels and marine objects in images, which can introduce significant errors in turbidity modeling and predictions. This study evaluates the use of two deep-learning-based inpainting methods, namely, Decoupled Spatial–Temporal Transformer (DSTT) and Deep Image Prior (DIP), to recover the obstructed information. Aerial images of turbidity plumes in the coastal environment were first acquired using a UAV system with a multispectral sensor that included obstructions on the water surface at various obstruction percentages. The performance of the two inpainting models was then assessed through both qualitative and quantitative analyses of the inpainted data, focusing on the accuracy of turbidity retrieval. The results show that the DIP model performs well across a wide range of obstruction percentages from 10 to 70%. In comparison, the DSTT model produces good accuracy only with low percentages of less than 20% and performs poorly when the obstruction percentage increases.

Efficient physics-informed transfer learning to quantify biochemical traits of winter wheat from UAV multispectral imagery

Accurate and efficient estimation of biochemical traits, including leaf index area (LAI), leaf chlorophyll content (LCC) and canopy chlorophyll content (CCC), is crucial for crop growth monitoring in agricultural management. Recent advancements in unmanned aerial vehicle (UAV) multispectral remote sensing have enabled fast and cost-effective measurements of these traits. However, traditional statistical regression models trained on specific datasets lack scalability and transferability across practical field conditions without retraining. This study proposed an efficient physics-informed transfer learning model (PITL) for winter wheat biochemical traits estimation from UAV multispectral data. The PITL integrates the strengths of physical radiative transfer simulations and deep neural network architectures through transfer learning to improve the estimation of biochemical traits from UAV multispectral data. The PITL was tested with convolutional neural network (CNN), deep neural network (DNN), and long short-term memory (LSTM) architectures. Results indicated that PITLDNN had better accuracy than PITLCNN and PITLLSTM models in predicting LAI (R2=0.94, RMSE = 0.32 m2/m2), LCC (R2=0.81, RMSE = 5.20 μg/cm2) and CCC (R2=0.928, RMSE = 0.2 g/m2). Moreover, PITLDNN demonstrated higher capability in computational efficiency, making it suitable for processing large volumes of UAV multispectral data in crop growth monitoring applications. Furthermore, PITL's integration of radiative transfer knowledge with labeled field data yielded higher predictive accuracy compared to physically-based inversion model, pure data-driven deep neural network approaches, and hybrid models. This study highlighted the performance of PITLDNN in accurately and efficiently quantifing biochemical traits from UAV multispectral data, thereby providing timely and accurate information for guiding crop growth monitoring applications.

Estimating Leaf Area Index in Apple Orchard by UAV Multispectral Images with Spectral and Texture Information

The Leaf Area Index (LAI) strongly influences vegetation evapotranspiration and photosynthesis rates. Timely and accurately estimating the LAI is crucial for monitoring vegetation growth. The unmanned aerial vehicle (UAV) multispectral digital camera platform has been proven to be an effective tool for this purpose. Currently, most remote sensing estimations of LAIs focus on cereal crops, with limited research on economic crops such as apples. In this study, a method for estimating the LAI of an apple orchard by extracting spectral and texture information from UAV multispectral images was proposed. Specifically, field measurements were conducted to collect LAI data for 108 sample points during the final flowering (FF), fruit setting (FS), and fruit expansion (FE) stages of apple growth in 2023. Concurrently, UAV multispectral images were obtained to extract spectral and texture information (Gabor transform). The Support Vector Regression Recursive Feature Elimination (SVR-REF) was employed to select optimal features as inputs for constructing models to estimate the LAI. Finally, the optimal model was used for LAI mapping. The results indicate that integrating spectral and texture information effectively enhances the accuracy of LAI estimation, with the relative prediction deviation (RPD) for all models being greater than 2. The Categorical Boosting (CatBoost) model established for FF exhibits the highest accuracy, with a validation set R2, root mean square error (RMSE), and RPD of 0.867, 0.203, and 2.482, respectively. UAV multispectral imagery proves to be valuable in estimating apple orchard LAIs, offering real-time monitoring of apple growth and providing a scientific basis for orchard management.

Inversion of Cotton Soil and Plant Analytical Development Based on Unmanned Aerial Vehicle Multispectral Imagery and Mixed Pixel Decomposition

In order to improve the accuracy of multispectral image inversion of soil and plant analytical development (SPAD) of the cotton canopy, image segmentation methods were utilized to remove the background interference, such as soil and shadow in UAV multispectral images. UAV multispectral images of cotton bud stage canopies at three different heights (30 m, 50 m, and 80 m) were acquired. Four methods, namely vegetation index thresholding (VIT), supervised classification by support vector machine (SVM), spectral mixture analysis (SMA), and multiple endmember spectral mixture analysis (MESMA), were used to segment cotton, soil, and shadows in the multispectral images of cotton. The segmented UAV multispectral images were used to extract the spectral information of the cotton canopy, and eight vegetation indices were calculated to construct the dataset. Partial least squares regression (PLSR), Random forest (FR), and support vector regression (SVR) algorithms were used to construct the inversion model of cotton SPAD. This study analyzed the effects of different image segmentation methods on the extraction accuracy of spectral information and the accuracy of SPAD modeling in the cotton canopy. The results showed that (1) The accuracy of spectral information extraction can be improved by removing background interference such as soil and shadows using four image segmentation methods. The correlation between the vegetation indices calculated from MESMA segmented images and the SPAD of the cotton canopy was improved the most; (2) At three different flight altitudes, the vegetation indices calculated by the MESMA segmentation method were used as the input variable, and the SVR model had the best accuracy in the inversion of cotton SPAD, with R2 of 0.810, 0.778, and 0.697, respectively; (3) At a flight altitude of 80 m, the R2 of the SVR models constructed using vegetation indices calculated from images segmented by VIT, SVM, SMA, and MESMA methods were improved by 2.2%, 5.8%, 13.7%, and 17.9%, respectively, compared to the original images. Therefore, the MESMA mixed pixel decomposition method can effectively remove soil and shadows in multispectral images, especially to provide a reference for improving the inversion accuracy of crop physiological parameters in low-resolution images with more mixed pixels.

Yield Prediction Models for Rice Varieties Using UAV Multispectral Imagery in the Amazon Lowlands of Peru

Rice is cataloged as one of the most widely cultivated crops globally, providing food for a large proportion of the global population. Integrating Geographic Information Systems (GISs), such as unmanned aerial vehicles (UAVs), into agricultural practices offers numerous benefits. UAVs, equipped with imaging sensors and geolocation technology, enable precise crop monitoring and management, enhancing yield and efficiency. However, Peru lacks sufficient experience with the application of these technologies, making them somewhat unfamiliar in the context of modern agriculture. In this study, we conducted experiments involving four distinct rice varieties (n = 24) at various stages of growth to predict yield using vegetation indices (VIs). A total of nine VIs (NDVI, GNDVI, ReCL, CIgreen, MCARI, SAVI, CVI, LCI, and EVI) were assessed across four dates: 88, 103, 116, and 130 days after sowing (DAS). Pearson correlation analysis, principal component analysis (PCA), and multiple linear regression were used to build prediction models. The results showed a general prediction model (including all the varieties) with the best performance at 130 days after sowing (DAS) using NDVI, EVI, and SAVI, with a coefficient of determination (adjusted-R2 = 0.43). The prediction models by variety showed the best performance for Esperanza at 88 DAS (adjusted-R2 = 0.94) using EVI as the vegetation index. The other varieties showed their best performance using different indices at different times: Capirona (LCI and CIgreen, 130 DAS, adjusted-R2 = 0.62); Conquista Certificada (MCARI, 116 DAS, R2 = 0.52); and Conquista Registrada (CVI and LCI, 116 DAS, adjusted-R2 = 0.79). These results provide critical information for optimizing rice crop management and support the use of unmanned aerial vehicles (UAVs) to inform timely decision making and mitigate yield losses in Peruvian agriculture.

Improving Radiometric Block Adjustment for UAV Multispectral Imagery under Variable Illumination Conditions

Unmanned aerial vehicles (UAVs) equipped with multispectral cameras offer great potential for applications in precision agriculture. A critical challenge that limits the deployment of this technology is the varying ambient illumination caused by cloud movement. Rapidly changing solar irradiance primarily affects the radiometric calibration process, resulting in reflectance distortion and heterogeneity in the final generated orthomosaic. In this study, we optimized the radiometric block adjustment (RBA) method, which corrects for changing illumination by comparing adjacent images and from incidental observations of reference panels to produce accurate and uniform reflectance orthomosaics regardless of variable illumination. The radiometric accuracy and uniformity of the generated orthomosaic could be enhanced by improving the weights of the information from the reference panels and by reducing the number of tie points between adjacent images. Furthermore, especially for crop monitoring, we proposed the RBA-Plant method, which extracts tie points solely from vegetation areas, to further improve the accuracy and homogeneity of the orthomosaic for the vegetation areas. To validate the effectiveness of the optimization techniques and the proposed RBA-Plant method, visual and quantitative assessments were conducted on a UAV-image dataset collected under fluctuating solar irradiance conditions. The results demonstrated that the optimized RBA and RBA-Plant methods outperformed the current empirical line method (ELM) and sensor-corrected approaches, showing significant improvements in both radiometric accuracy and homogeneity. Specifically, the average root mean square error (RMSE) decreased from 0.084 acquired by the ELM to 0.047, and the average coefficient of variation (CV) decreased from 24% (ELM) to 10.6%. Furthermore, the orthomosaic generated by the RBA-Plant method achieved the lowest RMSE and CV values, 0.039 and 6.8%, respectively, indicating the highest accuracy and best uniformity. In summary, although UAVs typically incorporate lighting sensors for illumination correction, this research offers different methods for improving uniformity and obtaining more accurate reflectance values from orthomosaics.

Predicting canopy chlorophyll concentration in citronella crop using machine learning algorithms and spectral vegetation indices derived from UAV multispectral imagery

Unmanned Aerial Vehicle (UAV) remote sensing in precision agriculture (PA) is a promising technique for managing crop inputs. The high spatial-resolution data enables extensive examination of the crop canopy, including identifying low-chlorophyll patches for initiating prophylactic measures as early as possible. In this study DJI M-200 equipped with MicaSense Altum multispectral camera was used to acquire multispectral images of citronella fields under various nutrient treatments at different growth stages. The study assessed the relevance of Machine Learning (ML) on multispectral images to predict citronella chlorophyll concentrations. To predict the crop canopy chlorophyll concentration for citronella, we evaluated Random Forest (RF), Support Vector Machine (SVM) and Partial Least Square (PLS) machine learning models. Based on the Rsquare value of all three models, the SVM machine learning model could best predict the crop canopy chlorophyll concentration with coefficient of determination R2 (0.79), root mean square error (0.05) and mean absolute error (0.04) respectively. The correlation between predicted chlorophyll contents and selected Vegetation Indices (VIs) also demonstrated significant relationship, implying that these can serve as a valuable indicator of crop canopy chlorophyll, which in turn can be used for providing insights into crop health, vigor, and productivity. This study showed that by employing UAV multispectral images and machine learning models the spatial distributions of chlorophyll concentration in a citronella crop at different growth phases can be predicted accurately.

A dataset of UAV multispectral images for a banana Fusarium wilt survey

Banana Fusarium wilt, also known as "banana cancer", currently poses a significant threat to banana production worldwide. Timely and accurate identification of Fusarium wilt disease is crucial for effective disease control and optimizing agricultural planting structure. To explore the use of unmanned aerial vehicle (UAV) remote sensing for identifying banana wilt disease, in this study, we obtained comprehensive experimental data on wilted banana plants in a banana plantation in Long'an County, Guangxi. The dataset includes UAV multispectral reflectance data and ground survey data on the incidence of banana wilt disease. The UAV multispectral imagery was acquired under clear, cloud-free, and well-illuminated weather conditions, at a flying altitude of 120 meters with a spatial resolution of 8 cm/pixel. The ground sample points survey on the occurrence of banana wilt disease was conducted using a high-precision handheld GPS unit to collect latitude and longitude information for each sample point, and the positions were matched with the corresponding center positions of banana plants in the UAV imagery indoors. The dataset provides high- spatial-resolution UAV multispectral imagery data and accurate ground sample point survey data. It can be applied in the research and monitoring of UAV remote sensing identification and surveillance of banana wilt disease. It plays a crucial role in advancing research and application of remote sensing monitoring for agricultural crop diseases.

Quantification of species composition in grass-clover swards using RGB and multispectral UAV imagery and machine learning.

Growing grass-legume mixtures for forage production improves both yield productivity and nutritional quality, while also benefiting the environment by promoting species biodiversity and enhancing soil fertility (through nitrogen fixation). Consequently, assessing legume proportions in grass-legume mixed swards is essential for breeding and cultivation. This study introduces an approach for automated classification and mapping of species in mixed grass-clover swards using object-based image analysis (OBIA). The OBIA procedure was established for both RGB and ten band multispectral (MS) images capturedby an unmanned aerial vehicle (UAV). The workflow integrated structural (canopy heights) and spectral variables (bands, vegetation indices) along with a machine learning algorithm (Random Forest) to perform image segmentation and classification. Spatial k-fold cross-validation was employed to assess accuracy. Results demonstrated good performance, achieving an overall accuracy of approximately 70%, for both RGB and MS-based imagery, with grass and clover classes yielding similar F1 scores, exceeding 0.7 values. The effectiveness of the OBIA procedure and classification was examined by analyzing correlations between predicted clover fractions and dry matter yield (DMY) proportions. This quantification revealed a positive and strong relationship, with R2 values exceeding 0.8 for RGB and MS-based classification outcomes. This indicates the potential of estimating (relative) clover coverage, which could assist breeders but also farmers in a precision agriculture context.

Assessing radiometric calibration methods for multispectral UAV imagery and the influence of illumination, flight altitude and flight time on reflectance, vegetation index and inversion of winter wheat AGB and LAI

Radiometric correction is essential to unmanned aerial vehicle (UAV) precision agriculture applications, especially for crop multi-period analysis and quantitative inversion of phenotypic and physiological parameters. In this study, we investigated the performance of four radiometric correction methods: “Camera only” (A), “Camera and sun irradiance” (B) and “Camera, sun irradiance and sun angle” (C) methods provided by Pix4d Mapper and an irradiance sensor-based method (D) implemented with the R software. UAV campaigns were conducted at flight altitudes of 30 m, 60 m and 90 m under clear and cloudy skies at different times over an experimental field of winter wheat. The influences of illumination, flight altitude and flight time on reflectance and vegetation indexes derived from different radiometric calibration methods were analyzed. Moreover, we further assessed the impact of reflectance and vegetation index variations on the above-ground biomass (AGB) and leaf area index (LAI) estimations of winter wheat. Results showed that method D is a promising approach for radiometric calibration, which produced consistent reflectance with method A at noon (e.g., 10:44:41 AM March 17 and 11:39:37 AM April 7) and mid-afternoon (e.g., 15:30:02 PM May 1) and more reliable reflectance in the morning (e.g., 7:15:44 AM March 17). Methods B and C seriously underestimated the reflectance at 10:44:41 AM March 17 and 11:39:37 AM April 7. This was caused by the incorrect attitude angles of the irradiance sensor and further miscalculation of the solar irradiance by Pix4d Mapper. Interestingly, these errors were eliminated in the calculation of the ratio type of vegetation indexes. Methods B and C yielded vegetation indexes that were almost the same as those of methods A and D. Under cloudy sky, when direct sunlight is not blocked by clouds, method D was effective; with cloud occlusion, results on April 24 indicated that the method failed to estimate the total sunlight and direct sunlight ratio. Reflectance and vegetation indexes obtained from different radiometric correction methods were all obviously affected by flight altitude. Furthermore, the variations of NDVI with flight altitude increasing were different with reflectance. Changes in reflectance and vegetation indexes with flight altitude had little effect on the correlation coefficients yielded with AGB, but showed a significant influence on those of LAI. Through analyzing the variation of observed solar radiation at different time intervals, caution is recommended when using the assumption that solar radiation is stable during the UAV flight. The longer the flight, the greater the variation in solar radiation, especially at sunrise and sunset. Even at noon, when the flight duration is 30 min, the change in solar radiation may exceed 10 % (e.g., 16.5 % on March 20).

Assessment of the Health Status of Old Trees of Platycladus orientalis L. Using UAV Multispectral Imagery

Assessing the health status of old trees is crucial for the effective protection and health management of old trees. In this study, we utilized an unmanned aerial vehicle (UAV) equipped with multispectral cameras to capture images for the rapid assessment of the health status of old trees. All trees were classified according to health status into three classes: healthy, declining, and severe declining trees, based on the above-ground parts of the trees. Two traditional machine learning algorithms, Support Vector Machines (SVM) and Random Forest (RF), were employed to assess their health status. Both algorithms incorporated selected variables, as well as additional variables (aspect and canopy area). The results indicated that the inclusion of these additional variables improved the overall accuracy of the models by 8.3% to 13.9%, with kappa values ranging from 0.166 and 0.233. Among the models tested, the A-RF model (RF with aspect and canopy area variables) demonstrated the highest overall accuracy (75%) and kappa (0.571), making it the optimal choice for assessing the health condition of old trees. Overall, this research presents a novel and cost-effective approach to assessing the health status of old trees.

Water Quality Inversion of a Typical Rural Small River in Southeastern China Based on UAV Multispectral Imagery: A Comparison of Multiple Machine Learning Algorithms

Remote sensing technology applications for water quality inversion in large rivers are common. However, their application to medium/small-sized water bodies within rural areas is limited due to the low spatial resolution of remote sensing images. In this work, a typical small rural river was selected, and high-resolution unmanned aerial vehicle (UAV) multispectral images and ground monitoring data of the river were obtained. Then, a comparative analysis of three univariate regression models and nine machine learning models (Ridge Regression (RR), Support Vector Regression (SVR), Grid Search Support Vector Regression (GS-SVR), Random Forest (RF), Grid Search Random Forest (GS-RF), eXtreme Gradient Boosting (XGBoost), Deep Neural Networks (DNN), Convolutional Neural Networks (CNN), and Catboost Regression (CBR)) for their accuracy in the prediction of turbidity (TUB), total nitrogen (TN), and total phosphorus (TP) was performed. TUB can be achieved by simple statistical regression models. The CBR model exhibited the best performance for the three index inversions on the test set evaluation metrics: R2 (0.90~0.92), RMSE (7.57 × 10−3~1.59 mg/L), MAE (0.01~1.30 mg/L), RPD (3.21~3.56), and NSE (0.84~0.92). The water pollution of the study area was closely related to its land-use pattern, excessive and irrational fertilizer application, and distribution of pollutant outlets.

Remote sensing monitoring of rice growth under Cnaphalocrocis medinalis (Guenée) damage by integrating satellite and UAV remote sensing data

ABSTRACT Satellite remote sensing is commonly used for large-scale agricultural monitoring, but the low spatial resolution of its imagery does not allow it to present details of crop growth. Combining satellite remote sensing and unmanned aerial vehicle (UAV) remote sensing to complement each other’s advantages may be a way to realize large-scale and precision agricultural monitoring. Therefore, to explore the effect of the fusion method of UAV remote sensing data and satellite remote sensing data on improving satellite monitoring of the growth of damaged rice and to achieve large-scale accurate monitoring of the growth characteristics of damaged rice, in this study, the research object was rice infested by Cnaphalocrocis medinalis Guenee. We selected SuperDove satellite imagery on 14 August 2022 and UAV multispectral imagery on 15 August 2022 for vegetation index calculation and fused the two by a scale transformation method. We constructed LAI monitoring models using multiple linear regression (MLR), back-propagation neural network (BPNN) and support vector machine regression (SVR) to evaluate the effect of satellite data on LAI inversion for affected rice before and after fusion. The results showed that (1) the learning ability of the machine learning model represented by BPNN and SVR was better than that of the traditional regression model represented by MLR. The SVR model had the best inversion effect on the LAI. (2) The satellite data after fusion with UAV data significantly improved the inversion accuracy of the LAI of damaged rice. Compared with the original satellite data, with the combined data, R2 increased by approximately 0.3, RMSE and MAE decreased by approximately 0.1. Moreover, the spatial details of remote sensing images were clearer, and the spatial matching degree with the UAV image inversion results was higher. This study can provide theoretical and technical support for large-scale and high-precision monitoring of affected rice.

Estimation of the Bio-Parameters of Winter Wheat by Combining Feature Selection with Machine Learning Using Multi-Temporal Unmanned Aerial Vehicle Multispectral Images

Accurate and timely monitoring of biochemical and biophysical traits associated with crop growth is essential for indicating crop growth status and yield prediction for precise field management. This study evaluated the application of three combinations of feature selection and machine learning regression techniques based on unmanned aerial vehicle (UAV) multispectral images for estimating the bio-parameters, including leaf area index (LAI), leaf chlorophyll content (LCC), and canopy chlorophyll content (CCC), at key growth stages of winter wheat. The performance of Support Vector Regression (SVR) in combination with Sequential Forward Selection (SFS) for the bio-parameters estimation was compared with that of Least Absolute Shrinkage and Selection Operator (LASSO) regression and Random Forest (RF) regression with internal feature selectors. A consumer-grade multispectral UAV was used to conduct four flight campaigns over a split-plot experimental field with various nitrogen fertilizer treatments during a growing season of winter wheat. Eighteen spectral variables were used as the input candidates for analyses against the three bio-parameters at four growth stages. Compared to LASSO and RF internal feature selectors, the SFS algorithm selects the least input variables for each crop bio-parameter model, which can reduce data redundancy while improving model efficiency. The results of the SFS-SVR method show better accuracy and robustness in predicting winter wheat bio-parameter traits during the four growth stages. The regression model developed based on SFS-SVR for LAI, LCC, and CCC, had the best predictive accuracy in terms of coefficients of determination (R2), root mean square error (RMSE) and relative predictive deviation (RPD) of 0.967, 0.225 and 4.905 at the early filling stage, 0.912, 2.711 μg/cm2 and 2.872 at the heading stage, and 0.968, 0.147 g/m2 and 5.279 at the booting stage, respectively. Furthermore, the spatial distributions in the retrieved winter wheat bio-parameter maps accurately depicted the application of the fertilization treatments across the experimental field, and further statistical analysis revealed the variations in the bio-parameters and yield under different nitrogen fertilization treatments. This study provides a reference for monitoring and estimating winter wheat bio-parameters based on UAV multispectral imagery during specific crop phenology periods.

Evaluation of canopy fraction-based vegetation indices, derived from multispectral UAV imagery, to map water status variability in a commercial vineyard

AbstractWater stress is a major factor affecting grapevine yield and quality. Standard methods for measuring water stress, such as midday stem water potential (ΨSWP), are laborious and time-consuming for intra-block variability mapping. In this study, we investigate water status variability within a 2.42-ha commercial Cabernet Sauvignon block with a standard vertical trellis system, using remote sensing (RS) tools, specifically canopy fraction-based vegetation indices (VIs) derived from multispectral unmanned aerial vehicle (UAV) imagery, as well as standard reference methods to evaluate soil and plant water status. A total of 31 target vines were monitored for ΨSWP during the whole growing season. The highest variability was at véraison when the highest atmospheric demand occurred. The ΨSWP variability present in the block was contrasted with soil water content (SWC) measurements, showing similar patterns. With spatial and temporal water stress variability confirmed for the block, the relationship between the ΨSWP measured in the field and fraction-based VIs obtained from multispectral UAV data was analysed. Four UAV flights were obtained, and five different VIs were evaluated per target vine across the vineyard. The VI correlation to ΨSWP was further evaluated by comparing VI obtained from canopy fraction (VIcanopy) versus the mean (VImean). It was found that using canopy fraction-based VIs did not significantly improve the correlation with ΨSWP (NDVIcanopyr = 0.57 and NDVImeanr = 0.53), however fractional cover (fcover) did seem to show a similar trend to plant water stress with decreasing canopy size corresponding with water stress classes. A subset of 14 target vines were further evaluated to evaluate if additional parameters (maximum temperature, relative humidity (RH), vapour pressure deficit, SWC and fractional cover) could serve as potential water stress indicators for future mapping. Results showed that the integration of NDVIcanopy and NDREmean with additional information could be used as an indicator for mapping water stress variability within a block.

Modelling of Kuibyshev reservoir shallow water depths by bathymetric surveys and multispectral UAV imagery data: A case study

This study investigates the potential of using multispectral Unmanned Aerial Vehicle (UAV) imagery to model the shallow water depths of the Kuibyshev Reservoir, Russia. Traditional methods like boom soundings and echo sounders, while accurate, are labor-intensive and costly. By leveraging multispectral data from UAVs, we aim to provide a more efficient and detailed approach to bathymetric mapping. Our methodology involved conducting bathymetric surveys with a Garmin GPS Map 178C and a Geoscan 401 Geodesy UAV equipped with a MicaSense RedEdge-MX camera. We performed correlation analysis and modelled depth using various regression techniques, identifying the Decision Tree Regressor as the top-performing model with an R² value of 0.98. Our findings suggest that UAV multispectral bathymetry is a viable alternative for local-scale shallow water mapping, with significant implications for reservoir management and ecological studies.

Use of Drone Imagery to Predict Leaf Nitrogen Content of Sugarcane Cultivated Under Organic Fertilizer Application

This study investigated the potential of unmanned aerial vehicle (UAV) based multispectral imagery (MI) to predict the leaf nitrogen (N) content of sugarcane (Saccharum officinarum L.). MI of canopy cover of two sugarcane varieties (Co 775 and SL 96 128) applied with different doses of N (0 – 550 kg/ha) were captured at 4½ months after planting. These images were used to calculate 10 different vegetation indices (VIs). Five machine learning (ML) models were tested for their potential to predict leaf N status using the most appropriate VIs. The correlation analysis showed that DVI (Difference Vegetation Index) was the most powerful VI for the prediction of leaf N (r = 0.81), followed by the RVI (Ratio Vegetation Index) and NDVI (Normalized Difference Vegetation Index) (R2= 0.78 and 0.77, respectively). A threshold correlation (r > 0.6) was applied to select predictive variables for ML models and performance was evaluated using a validation data set of leaf N content. Individual variety testing revealed that PLSR (Partial Least Squares Regression) and SVR (Support Vector Regression) models as the best prediction models with the highest Coefficient of determination (R2>0.72) and the lowest Root Mean Square Error values (RMSE<0.11). When both variety data were pooled, RF (Random Forest) demonstrated the highest predictive performance on the validation dataset, with an R2 value of 0.66 with a RMSE value of 0.12. Generally, the prediction accuracy of models was less when data from both varieties were pooled. This study postulated the potential for the fusion of UAV MI and ML approaches to predict leaf N states and the importance of developing varietal-specific prediction models for the sugarcane vegetation.

Remote sensing estimation of sugar beet SPAD based on un-manned aerial vehicle multispectral imagery.

Accurate, non-destructive and cost-effective estimation of crop canopy Soil Plant Analysis De-velopment(SPAD) is crucial for precision agriculture and cultivation management. Unmanned aerial vehicle (UAV) platforms have shown tremendous potential in predicting crop canopy SPAD. This was because they can rapidly and accurately acquire remote sensing spectral data of the crop canopy in real-time. In this study, a UAV equipped with a five-channel multispectral camera (Blue, Green, Red, Red_edge, Nir) was used to acquire multispectral images of sugar beets. These images were then combined with five machine learning models, namely K-Nearest Neighbor, Lasso, Random Forest, RidgeCV and Support Vector Machine (SVM), as well as ground measurement data to predict the canopy SPAD of sugar beets. The results showed that under both normal irrigation and drought stress conditions, the SPAD values in the normal ir-rigation treatment were higher than those in the water-limited treatment. Multiple vegetation indices showed a significant correlation with SPAD, with the highest correlation coefficient reaching 0.60. Among the SPAD prediction models, different models showed high estimation accuracy under both normal irrigation and water-limited conditions. The SVM model demon-strated a good performance with a correlation coefficient (R2) of 0.635, root mean square error (Rmse) of 2.13, and relative error (Re) of 0.80% for the prediction and testing values under normal irrigation. Similarly, for the prediction and testing values under drought stress, the SVM model exhibited a correlation coefficient (R2) of 0.609, root mean square error (Rmse) of 2.71, and rela-tive error (Re) of 0.10%. Overall, the SVM model showed good accuracy and stability in the pre-diction model, greatly facilitating high-throughput phenotyping research of sugar beet canopy SPAD.

Prediction of vertical distribution of SPAD values within maize canopy based on unmanned aerial vehicles multispectral imagery.

Real-time monitoring of canopy chlorophyll content is significant in understanding crop growth status and guiding precision agricultural management. Remote sensing methods have demonstrated great potential in this regard. However, the spatiotemporal heterogeneity of chlorophyll content within crop canopies poses challenges to the accuracy and stability of remote sensing estimation models. Hence, this study aimed to develop a novel method for estimating canopy chlorophyll content (represented by SPAD values) in maize (Zea mays L.) canopies. Firstly, we investigated the spatiotemporal distribution patterns of maize canopy SPAD values under varying nitrogen application rates and different growth stages. The results revealed a non-uniform, "bell-shaped" curve distribution of maize canopy SPAD values in the vertical direction. Nitrogen application significantly influenced the distribution structure of SPAD values within the canopy. Secondly, we achieved satisfactory results by fitting the Lorentz peak distribution function to the SPAD values of different leaf positions in maize. The fitting performance, evaluated using R2 and RMSE, ranged from 0.69 to 0.98 and 0.45 to 3.59, respectively, for the year 2021, and from 0.69 to 0.77 and 2.38 to 6.51, respectively, for the year 2022.Finally, based on the correlation between canopy SPAD values and vegetation indices (VIs) at different growth stages, we identified the sensitive leaf positions for the selected CCCI (Canopy Chlorophyll Index) in each growth stage. The 6th (r = 0.662), 4th (r = 0.816), 12th (r = 0.722), and 12th (r = 0.874) leaf positions exhibited the highest correlations. Compared to the estimation model using canopy wide SPAD values, the model based on sensitive leaf positions showed improved accuracy, with increases of 34%, 3%, 20%, and 3% for each growth stage, respectively. In conclusion, the findings of this study contribute to the enhancement of chlorophyll content estimation models in crop canopies and provide valuable insights for the integration of crop growth models with remote sensing methods.