Estimating the Leaf Area Index of Winter Wheat Based on Unmanned Aerial Vehicle RGB-Image Parameters

Multi-feature sparse representation based on adaptive graph constraint for cropland delineation

Cropland delineation is the basis of agricultural resource surveys and many algorithms for plot identification have been studied. However, there is still a vacancy in SRC for cropland delineation with the high-dimensional data extracted from UAV RGB photographs. In order to address this problem, a new sparsity-based classification algorithm is proposed. Firstly, the multi-feature association sparse model is designed by extracting the multi-feature of UAV RGB photographs. Next, the samples with similar characteristics are hunted with the breadth-first principle to construct a shape-adaptive window for each test. Finally, an algorithm, multi-feature sparse representation based on adaptive graph constraint (AMFSR), is obtained by solving the optimal objective iteratively. Experimental results show that the overall accuracy (OA) of AMFSR reaches 92.3546% and the Kappa is greater than 0.8. Furthermore, experiments have demonstrated that the model also has a generalization ability.

Combining spectral and texture feature of UAV image with plant height to improve LAI estimation of winter wheat at jointing stage

Introduction

Leaf area index (LAI) is a critical physiological and biochemical parameter that profoundly affects vegetation growth. Accurately estimating the LAI for winter wheat during jointing stage is particularly important for monitoring wheat growth status and optimizing variable fertilization decisions. Recently, unmanned aerial vehicle (UAV) data and machine/depth learning methods are widely used in crop growth parameter estimation. In traditional methods, vegetation indices (VI) and texture are usually to estimate LAI. Plant Height (PH) unlike them, contains information about the vertical structure of plants, which should be consider.

Methods

Taking Xixingdian Township, Cangzhou City, Hebei Province, China as the research area in this paper, and four machine learning algorithms, namely, support vector machine(SVM), back propagation neural network (BPNN), random forest (RF), extreme gradient boosting (XGBoost), and two deep learning algorithms, namely, convolutional neural network (CNN) and long short-term memory neural network (LSTM), were applied to estimate LAI of winter wheat at jointing stage by integrating the spectral and texture features as well as the plant height information from UAV multispectral images. Initially, Digital Surface Model (DSM) and Digital Orthophoto Map (DOM) were generated. Subsequently, the PH, VI and texture features were extracted, and the texture indices (TI) was further constructed. The measured LAI on the ground were collected for the same period and calculated its Pearson correlation coefficient with PH, VI and TI to pick the feature variables with high correlation. The VI, TI, PH and fusion were considered as the independent features, and the sample set partitioning based on joint x-y distance (SPXY) method was used to divide the calibration set and validation set of samples.

Results

The ability of different inputs and algorithms to estimate winter wheat LAI were evaluated. The results showed that (1) The addition of PH as a feature variable significantly improved the accuracy of the LAI estimation, indicating that wheat plant height played a vital role as a supplementary parameter for LAI inversion modeling based on traditional indices; (2) The combination of texture features, including normalized difference texture indices (NDTI), difference texture indices (DTI), and ratio texture indices (RTI), substantially improved the correlation between texture features and LAI; Furthermore, multi-feature combinations of VI, TI, and PH exhibited superior capability in estimating LAI for winter wheat; (3) Six regression algorithms have achieved high accuracy in estimating LAI, among which the XGBoost algorithm estimated winter wheat LAI with the highest overall accuracy and best results, achieving the highest R2 (R2 = 0.88), the lowest RMSE (RMSE=0.69), and an RPD greater than 2 (RPD=2.54).

Discussion

This study provided compelling evidence that utilizing XGBoost and integrating spectral, texture, and plant height information extracted from UAV data can accurately monitor LAI during the jointing stage of winter wheat. The research results will provide a new perspective for accurate monitoring of crop parameters through remote sensing.

A Remote Sensing Approach for Assessing Daily Cumulative Evapotranspiration Integral in Wheat Genotype Screening for Drought Adaptation

This study considers critical aspects of water management and crop productivity in wheat cultivation, specifically examining the daily cumulative actual evapotranspiration (ETa). Traditionally, ETa surface energy balance models have provided estimates at discrete time points, lacking a holistic integrated approach. Field trials were conducted with 22 distinct wheat varieties, grown under both irrigated and rainfed conditions over a two-year span. Leaf area index prediction was enhanced through a robust multiple regression model, incorporating data acquired from an unmanned aerial vehicle using an RGB sensor, and resulting in a predictive model with an R2 value of 0.85. For estimation of the daily cumulative ETa integral, an integrated approach involving remote sensing and energy balance models was adopted. An examination of the relationships between crop yield and evapotranspiration (ETa), while considering factors like year, irrigation methods, and wheat cultivars, unveiled a pronounced positive asymptotic pattern. This suggests the presence of a threshold beyond which additional water application does not significantly enhance crop yield. However, a genetic analysis of the 22 wheat varieties showed no correlation between ETa and yield. This implies opportunities for selecting resource-efficient wheat varieties while minimizing water use. Significantly, substantial disparities in water productivity among the tested wheat varieties indicate the possibility of intentionally choosing lines that can optimize grain production while minimizing water usage within breeding programs. The results of this research lay the foundation for the development of resource-efficient agricultural practices and the cultivation of crop varieties finely attuned to water-scarce regions.

Crop monitoring and biomass estimation based on downscaled remote sensing data in AquaCrop model (case study: Qazvin Plain, Iran)

Ensuring food security requires monitoring crop growth conditions prior to harvesting. This research encompass three stages: remote sensing, crop modeling, and establishing a connection between the two. Besides, Fusion satellite images have been implemented to monitor and investigate fodder corn in three study areas. In the remote sensing stage, after implementing the downscaling algorithm and producing the leaf area index (LAI) time series, the results were compared with the estimated values from Landsat 8 and MODIS images, which were associated with overestimation in all cases. Furthermore, the results exhibited statistically significance with R2 > 95% and P-value < 0.05. The AquaCrop model was first calibrated and implemented in the crop modeling section at each growth stage based on the observational data measured in each field. The accuracy of the simulated model was checked according to the results of statistical indicators with high accuracy (NRMSE = 10% and RMSE = 0.03 (Ton/ha)) at significant level (95%) and was associated with underestimation. Using the SVM decision support algorithm, the relationship between downscaled LAI and calibrated CC (crop canopy) was estimated. This relationship was generated with 70% of the data and subsequently validated using the remaining 30% (R2 = 0.99, NRMSE = 0.01). On this basis, CC values were predicted. Finally, biomass values were compared with observed biomass values. According to the results of statistical indicators (RMSE = 0.19 (Ton/ha), NRMSE = 0.01, R2 = 0.96, P-value < 0.05), biomass estimation was highly accurate. These results demonstrate the reliability and accuracy of the model and the proposed method for simulating and estimating biomass before harvesting.

Construction of a Winter Wheat Comprehensive Growth Monitoring Index Based on a Fuzzy Degree Comprehensive Evaluation Model of Multispectral UAV Data

Realizing real-time and rapid monitoring of crop growth is crucial for providing an objective basis for agricultural production. To enhance the accuracy and comprehensiveness of monitoring winter wheat growth, comprehensive growth indicators are constructed using measurements of above-ground biomass, leaf chlorophyll content and water content of winter wheat taken on the ground. This construction is achieved through the utilization of the entropy weight method (EWM) and fuzzy comprehensive evaluation (FCE) model. Additionally, a correlation analysis is performed with the selected vegetation indexes (VIs). Then, using unmanned aerial vehicle (UAV) multispectral orthophotos to construct VIs and extract texture features (TFs), the aim is to explore the potential of combining the two as input variables to improve the accuracy of estimating the comprehensive growth indicators of winter wheat. Finally, we develop comprehensive growth indicator inversion models based on four machine learning algorithms: random forest (RF); partial least squares (PLS); extreme learning machine (ELM); and particle swarm optimization extreme learning machine (PSO-ELM), and the optimal model is selected by comparing the accuracy evaluation indexes of the model. The results show that: (1) The correlation among the comprehensive growth indicators (CGIs) constructed by EWM (CGIewm) and FCE (CGIfce) and VIs are all improved to different degrees compared with the single indicators, among which the correlation between CGIfce and most of the VIs is larger. (2) The inclusion of TFs has a positive impact on the performance of the comprehensive growth indicator inversion model. Specifically, the inversion model based on ELM exhibits the most significant improvement in accuracy. The coefficient of determination (R2) values of ELM-CGIewm and ELM- CGIfce increased by 20.83% and 20.37%, respectively. (3) The CGIfce inversion model constructed by VIs and TFs as input variables and based on the ELM algorithm is the best inversion model (ELM-CGIfce), with R2 reaching 0.65. Particle swarm optimization (PSO) is used to optimize the ELM-CGIfce (PSO-ELM-CGIfce), and the precision is significantly improved compared with that before optimization, with R2 reaching 0.84. The results of the study can provide a favorable reference for regional winter wheat growth monitoring.

Use of High-Resolution Multispectral UAVs to Calculate Projected Ground Area in Corylus avellana L. Tree Orchard

In the last decade, research on Corylus avellana has focused on improving field techniques and hazelnut quality; however, climatic change and sustainability goals call for new agronomic management strategies. Precision management technologies could help improve resource use efficiency and increase grower income, but research on remote sensing systems and especially on drone devices is still limited. Therefore, the hazelnut is still linked to production techniques far from the so-called Agriculture 4.0. Unmanned aerial vehicles platforms are becoming increasingly available to satisfy the demand for rapid real-time monitoring for orchard management at spatial, spectral, and temporal resolutions, addressing the analysis of geometric traits such as canopy volume and area and vegetation indices. The objective of this study is to define a rapid procedure to calculate geometric parameters of the canopy, such as canopy area and height, by methods using NDVI and CHM values derived from UAV images. This procedure was tested on the young Corylus avellana tree to manage a hazelnut orchard in the early years of cultivation. The study area is a hazelnut orchard (6.68 ha), located in Bernalda, Italy. The survey was conducted in a six-year-old irrigated hazelnut orchard of Tonda di Giffoni and Nocchione varieties using multispectral UAV. We determined the Projected Ground Area and, on the Corylus avellana canopy trough, the vigor index NDVI (Normalized Difference Vegetation Index) and the CHM (Canopy Height Model), which were used to define the canopy and to calculate the tree crown area. The projection of the canopy area to the ground measured with NDVI values > 0.30 and NDVI values > 0.35 and compared with CHM measurements showed a statistically significant linear regression, R2 = 0.69 and R2 = 0.70, respectively. The ultra-high-resolution imagery collected with the UAV system helped identify and define each tree crown individually from the background (bare soil and grass cover). Future developments are the construction of reliable relationships between the vigor index NDVI and the Leaf Area Index (LAI), as well as the evaluation of their spatial-temporal evolution.

Potential of Snapshot-Type Hyperspectral Imagery Using Support Vector Classifier for the Classification of Tomatoes Maturity

It is necessary to convert to automation in a tomato hydroponic greenhouse because of the aging of farmers, the reduction in agricultural workers as a proportion of the population, COVID-19, and so on. In particular, agricultural robots are attractive as one of the ways for automation conversion in a hydroponic greenhouse. However, to develop agricultural robots, crop monitoring techniques will be necessary. In this study, therefore, we aimed to develop a maturity classification model for tomatoes using both support vector classifier (SVC) and snapshot-type hyperspectral imaging (VIS: 460–600 nm (16 bands) and Red-NIR: 600–860 nm (15 bands)). The spectral data, a total of 258 tomatoes harvested in January and February 2022, was obtained from the tomatoes’ surfaces. Spectral data that has a relationship with the maturity stages of tomatoes was selected by correlation analysis. In addition, the four different spectral data were prepared, such as VIS data (16 bands), Red-NIR data (15 bands), combination data of VIS and Red-NIR (31 bands), and selected spectral data (6 bands). These data were trained by SVC, respectively, and we evaluated the performance of trained classification models. As a result, the SVC based on VIS data achieved a classification accuracy of 79% and an F1-score of 88% to classify the tomato maturity into six stages (Green, Breaker, Turning, Pink, Light-red, and Red). In addition, the developed model was tested in a hydroponic greenhouse and was able to classify the maturity stages with a classification accuracy of 75% and an F1-score of 86%.

Retrieval of Nitrogen Content in Apple Canopy Based on Unmanned Aerial Vehicle Hyperspectral Images Using a Modified Correlation Coefficient Method

The accurate retrieval of nitrogen content based on Unmanned Aerial Vehicle (UAV) hyperspectral images is limited due to uncertainties in determining the locations of nitrogen-sensitive wavelengths. This study developed a Modified Correlation Coefficient Method (MCCM) to select wavelengths sensitive to nitrogen content. The Normalized Difference Canopy Shadow Index (NDCSI) was applied to remove the shadows from UAV hyperspectral images, thus yielding the canopy spectral information. The MCCM was then used to screen the bands sensitive to nitrogen content and to construct spectral characteristic parameters. Finally, the optimal model for nitrogen content retrieval was established and selected. As a result, the screened sensitive wavelengths for nitrogen content selected were 470, 474, 490, 514, 582, 634, and 682 nm, respectively. Among the nitrogen content retrieval models, the best model was the Support Vector Machine (SVM) model. In the training set, this model outperformed the other models with an R2 of 0.733, RMSE of 6.00%, an nRMSE of 12.76%, and a MAE of 4.49%. Validated by the ground-measured nitrogen content, this model yielded good performance with an R2 of 0.671, an RMSE of 4.73%, an nRMSE of 14.83%, and a MAE of 3.98%. This study can provide a new method for vegetation nutrient content retrieval based on UAV hyperspectral data.

Application of RGB Images Obtained by UAV in Coffee Farming

The objective of this study was to evaluate the potential of the practical application of unmanned aerial vehicles and RGB vegetation indices (VIs) in the monitoring of a coffee crop. The study was conducted in an experimental coffee field over a 12-month period. An RGB digital camera coupled to a UAV was used. Nine VIs were evaluated in this study. These VIs were subjected to a Pearson correlation analysis with the leaf area index (LAI), and subsequently, the VIs with higher R2 values were selected. The LAI was estimated by plant height and crown diameter values obtained by imaging, which were correlated with these values measured in the field. Among the VIs evaluated, MPRI (0.31) and GLI (0.41) presented greater correlation with LAI; however, the correlation was weak. Thematic maps of VIs in the evaluated period showed variability present in the crop. The evolution of weeds in the planting rows was noticeable with both VIs, which can help managers to make the decision to start crop management, thus saving resources. The results show that the use of low-cost UAVs and RGB cameras has potential for monitoring the coffee production cycle, providing producers with information in a more accurate, quick and simple way.

Estimating Plant Nitrogen Concentration of Rice through Fusing Vegetation Indices and Color Moments Derived from UAV-RGB Images

Estimating plant nitrogen concentration (PNC) has been conducted using vegetation indices (VIs) from UAV-based imagery, but color features have been rarely considered as additional variables. In this study, the VIs and color moments (color feature) were calculated from UAV-based RGB images, then partial least square regression (PLSR) and random forest regression (RF) models were established to estimate PNC through fusing VIs and color moments. The results demonstrated that the fusion of VIs and color moments as inputs yielded higher accuracies of PNC estimation compared to VIs or color moments as input; the RF models based on the combination of VIs and color moments (R2 ranging from 0.69 to 0.91 and NRMSE ranging from 0.07 to 0.13) showed similar performances to the PLSR models (R2 ranging from 0.68 to 0.87 and NRMSE ranging from 0.10 to 0.29); Among the top five important variables in the RF models, there was at least one variable which belonged to the color moments in different datasets, indicating the significant contribution of color moments in improving PNC estimation accuracy. This revealed the great potential of combination of RGB-VIs and color moments for the estimation of rice PNC.

The Correlation between Soil Nutrient and Potato Quality in Loess Plateau of China Based on PLSR

Potato tuber quality is influenced by the interaction of soil nutrients. Hence, simple correlation analysis cannot accurately reflect the true relationship between soil nutrients and potato tuber quality. In this study, potato tuber quality and soil nutrient content were used as research materials in the Loess Plateau of China. The partial least square regression (PLSR) method was used to establish the regression equation between potato quality and soil nutrient. The major soil nutrient indexes influencing potato quality were screened out to provide theoretical basis for potato field management. The results showed that the major soil nutrient factors influencing the potato tuber quality in Loess Plateau were soil ammonium nitrogen, soil nitrate nitrogen, soil available phosphorus, pH, and soil available potassium. Soil pH value is the most important factor affecting potato starch, reducing sugar content, and soluble protein content. Soil nitrate nitrogen is one of the important factors affecting potato tuber soluble total sugar content, vitamin C, browning intensity, and polyphenol oxidase activity. Soil ammonium nitrogen was positively correlated with the total soluble sugar content of potato tubers, and negatively correlated with reducing sugar content, browning intensity, and polyphenol oxidase activity. However, soil available potassium has positive effects on potato starch and reducing sugar content, and negative effects on soluble protein and browning strength. Results of this study indicates that the major soil nutrient factors influencing potato tuber quality were soil nitrate nitrogen and soil pH value.