Search Results (103)

In recent years, global agriculture has encountered several challenges exacerbated by the effects of changes in climate, such as extreme water shortages for irrigation and heat waves. Water-deficit stress adversely affects the morpho-physiology of numerous crops, including soybean (Glycine max L.), which is considered as promising crop in Bangladesh. Seaweed extract (SWE) has the potential to improve crop yield and alleviate the adverse effects of water-deficit stress. Remote and proximal sensing are also extensively utilized in estimating morpho-physiological traits owing to their cost-efficiency and non-destructive characteristics. The study was carried out to evaluate soybean morpho-physiological traits under the application of water extracts of Gracilaria tenuistipitata var. liui (red seaweed) with two varying irrigation water conditions (100% of total crop water requirement (TCWR) and 70% of TCWR). Principal component analysis (PCA) revealed that among the four treatments, the 70% irrigation + 5% (v/v) SWE and the 100% irrigation treatments overlapped, indicating that the application of SWE effectively mitigated water-deficit stress in soybeans. This result demonstrates that the foliar application of 5% SWE enabled soybeans to achieve morpho-physiological performance comparable to that of fully irrigated plants while reducing irrigation water use by 30%. Based on Pearson’s correlation matrix, a simple linear regression model was used to ascertain the relationship between unmanned aerial vehicle (UAV)-derived vegetation indices and the field-measured physiological characteristics of soybean. The Normalized Difference Red Edge (NDRE) strongly correlated with stomatal conductance (R² = 0.76), photosystem II efficiency (R² = 0.78), maximum fluorescence (R² = 0.64), and apparent transpiration rate (R² = 0.69). The Soil Adjusted Vegetation Index (SAVI) had the highest correlation with leaf relative water content (R² = 0.87), the Blue Normalized Difference Vegetation Index (bNDVI) with steady-state fluorescence (R² = 0.56) and vapor pressure deficit (R² = 0.74), and the Green Normalized Difference Vegetation Index (gNDVI) with chlorophyll content (R² = 0.73). Our results demonstrate how UAV and physiological data can be integrated to improve precision soybean farming and support sustainable soybean production under water-deficit stress. Full article

Cultivating crops in the hot, arid conditions of the Arabian Peninsula often requires irrigation with brackish water, which exposes plants to salinity and heat stress. Timely, cost-effective monitoring of plant health can significantly enhance crop management. In this context, remote sensing techniques offer promising alternatives. This study evaluates several low-cost, ground-level remote sensing methods and compares them with benchmark analytical techniques for assessing salt stress in two economically important woody species, moringa and pomegranate. The species were irrigated under three salinity levels: low (2 dS m⁻¹), medium (5 dS m⁻¹), and high (10 dS m⁻¹). Remote sensing tools included RGB, multispectral, and thermal cameras mounted on selfie sticks for canopy imaging, as well as portable leaf pigment and chlorophyll fluorescence meters. Analytical benchmarks included sodium (Na) accumulation, carbon isotope composition (δ¹³C), and nitrogen (N) concentration in leaf dry matter. As salinity increased from low to medium, canopy temperatures, vegetation indices, and δ¹³C values rose. However, increasing salinity from medium to high levels led to a rise in Na accumulation without further significant changes in other remote sensing and analytical parameters. In moringa and across the three salinity levels, the Normalized Difference Red Edge (NDRE) and leaf chlorophyll content on an area basis showed significant correlations with δ¹³C (r = 0.758, p < 0.001; r = 0.423, p < 0.05) and N (r = 0.482, p < 0.01; r = 0.520, p < 0.01). In pomegranate, the Normalized Difference Vegetation Index (NDVI) and chlorophyll were strongly correlated with δ¹³C (r = 0.633, p < 0.01 and r = 0.767, p < 0.001) and N (r = 0.832, p < 0.001 and r = 0.770, p < 0.001). Remote sensing was particularly effective at detecting plant responses between low and medium salinity, with stronger correlations observed in pomegranate. Full article

The prediction of soil moisture conditions using multispectral data from unmanned aerial vehicles (UAVs) has advantages over ground measurements in terms of costs and monitoring range. However, the prediction accuracy for moisture conditions using spectral data alone is low. In this study, relationships between water deficits and phenotypic characteristics in oats were evaluated and used to develop a UAV multispectral-based water prediction model. The vegetation indices NDRE (Normalized Difference Red Edge), CIG (Chlorophyll Index), and MCARI (Modified Chlorophyll Absorption in Reflectance Index) were highly correlated with oat yield. Based on a multipath analysis in the structural equation modeling framework, irrigation (p < 0.01), leaf area index (LAI) (p < 0.001), and SPAD (p < 0.001) had direct positive effects on NDRE. Three distinct machine learning approaches—linear regression (LR), random forest (RF), and artificial neural network (ANN) were employed to establish predictive models between the Normalized Difference Red Edge Index (NDRE) and soil water content (SWC). The linear regression model showed moderate correlation (R² = 0.533). Machine learning approaches demonstrated markedly superior performance (RF: R² = 0.828; ANN: R² = 0.810). Nonlinear machine learning algorithms (RF and ANN) significantly outperform conventional linear regression in estimating SWC from spectral vegetation indices. Full article

Soybean cultivation plays a crucial role in the global food system, providing raw materials for both the food and feed industries. To enhance cultivation efficiency, plant biostimulants are used to improve metabolism and stimulate growth. A key aspect of modern cultivation is the ability to rapidly and non-invasively assess crop status. One such method involves the use of drones equipped with multispectral cameras. This paper presents the results of an experimental study on soybean cultivation involving a natural biostimulant in the form of Epilobium angustifolium extract (commonly known as fireweed) and a commercial seaweed-based biostimulant, Kelpak. The research was conducted at an experimental farm in eastern Poland. The effectiveness of the preparations was evaluated using a drone-mounted multispectral camera. Changes in the values of selected spectral indices were analyzed: the Normalized Difference Red Edge Index (NDRE), the Leaf Chlorophyll Index (LCI), and the Optimized Soil-Adjusted Vegetation Index (OSAVI). The study included a control group treated with pure water. Mathematical and statistical analyses of the mean values and standard deviations of the indices were conducted. The results demonstrated that multispectral scanning allows for the detection of significant differences between the effects of the E. angustifolium extract, the seaweed-based biostimulant, and the water control. These findings confirm the utility of this method for assessing the effectiveness of biostimulant applications in soybean cultivation. Full article

Estimating wheat traits based on spectral reflectance measurements and machine learning remains challenging due to the large datasets required for model training and testing. To overcome this limitation, a simulated dataset was generated using the radiative transfer model (RTM) PROSAIL and inverted based on an artificial neural network (ANN). Field experiments were conducted in Eastern Austria to measure spectral reflectance and destructively sample plants to measure the wheat traits plant area index (PAI), nitrogen yield (NY), canopy water content (CWC), and above-ground dry matter (AGDM). Four ANN-based RTM inversion models were setup, which varied in their spectral resolution, hyperspectral or multispectral, and the inclusion or exclusion of background soil spectra correction. The models were also compared to a simple vegetation index approach using Normalized Difference Vegetation Index (NDVI) and Normalized Difference Red-Edge (NDRE). The RTM inversion model with hyperspectral input data and background soil spectra correction was the best among all tested models for estimating wheat traits during the vegetative developmental stages (PAI: R² = 0.930, RRMSE = 17.9%; NY: R² = 0.908, RRMSE = 14.4%; CWC: R² = 0.967, RRMSE = 17.0%) as well as throughout the whole growing season (PAI: R² = 0.845, RRMSE = 27.7%; CWC: R² = 0.884, RRMSE = 20.0%; AGDM: R² = 0.960, RRMSE = 13.7%). Many models presented in this study provided suitable estimations of the relevant wheat traits PAI, NY, CWC, and AGDM for application in agronomy, breeding, and crop sciences in general. Full article

Climate change is threatening the sustainability of crop yields due to an increasing frequency of extreme weather conditions, requiring timely agricultural monitoring. Remote sensing facilitates consistent and continuous monitoring of field crops. This study aimed to estimate alfalfa crop height through satellite images and machine learning methods within the Google Earth Engine (GEE) Python API. Ground measurements for this study were collected over three years in four Canadian provinces. We utilized Sentinel-2 data to obtain satellite imagery corresponding to the same timeframe and location as the ground measurements. Three machine learning algorithms were employed to estimate plant height from satellite images: random forest (RF), support vector regression (SVR), and extreme gradient boosting (XGB). The efficacy of these algorithms has been assessed and compared. Several widely used vegetation indices, for instance normalized difference vegetation index (NDVI), enhanced vegetation index (EVI), and normalized difference red-edge (NDRE), were selected and assessed in this study. RF feature importance was utilized to determine the ranking of features from most to least significant. Several feature selection strategies were utilized and compared with the situation where all features are used. We demonstrated that RF and XGB surpassed SVR when assessing test data performance. Our findings showed that XGB and RF could predict alfalfa crop height with an R² of 0.79 and a mean absolute error (MAE) of around 4 cm Our findings indicated that SVR exhibited the lowest accuracy among the three algorithms tested, with R² of 0.69 and an MAE of 4.63 cm. The analysis of important features showed that normalized difference red edge (NDRE) and normalized difference water index (NDWI) were the most important variables in determining alfalfa crop height. The results of this study also demonstrated that using RF and feature selection strategies, alfalfa crop height can be estimated with comparably high accuracy. Given that the models were fully trained and developed in Python (v. 3.10), they can be readily implemented in a decision support system and deliver near real-time estimations of alfalfa crop height for farmers throughout Canada. Full article

Spectral indices such as NDRE (Normalized Difference Red Edge Index), CCCI (Canopy Chlorophyll Content Index), and IRECI (Inverted Red Edge Chlorophyll Index), derived from the Red Edge band of MSI/Sentinel-2A (B05, B06, B07), are critical for coffee monitoring. To align the Red Edge band (20 m resolution) with the NIR band (10 m resolution), the nearest neighbor, bilinear, cubic and Lanczos resampling methods were used, available in the Terra package in the R software(4.4.0). This study evaluates these methods using two original B05 images from 24 November 2023, and 21 September 2023, covering the “Ouro Verde” (15 ha) and “Canto do Rio” (45 ha) farms in Bahia, Brazil. A total of 500 random points were analyzed using PSF, linear models, and cross-validation with R², MAE, and RMSE. PSF analysis confirmed data integrity, and the cubic method demonstrated the best performance (R² = 0.996, MAE = 0.008 and RMSE = 0.012 in the “Ouro Verde” Farm and R² = 0.995, MAE = 0.007 and RMSE = 0.011 in the “Canto do Rio” Farm). The results highlight the importance of selecting appropriate resampling methods for precise remote sensing in coffee cultivation, ensuring accurate digital processing aligned with study objectives. Full article

Accurate cover crop biomass estimation is critical for evaluating their ecological benefits. Traditional methods, like destructive sampling, are labor-intensive and time-consuming. This study investigates the application of unmanned aerial vehicle (UAV)-mounted multispectral sensors to estimate biomass in oats, Austrian winter peas (AWP), turnips, and a combination of all three crops across six experimental plots. Five spectral images were collected at two growth stages, analyzing band reflectance, nine vegetation indices, and canopy height models (CHMs) for biomass estimation. Results indicated that most vegetation indices were effective during mid-growth stages but showed reduced accuracy later. Stepwise multiple linear regression revealed that combining the normalized difference red-edge (NDRE) index and CHM provided the best biomass model before termination (R² = 0.84). For bitemporal images, green reflectance, CHM, and the ratio of near-infrared (NIR) to red achieved the best performance (R² = 0.85). Cover crop species also influenced the model performance. Oats were best modeled using the enhanced vegetation index (EVI) (R² = 0.86), AWP with red-edge reflectance (R² = 0.71), turnips with NIR, GNDVI, and CHM (R² = 0.95), and mixed species with NIR and blue band reflectance (R² = 0.93). These findings demonstrate the potential of high-resolution multispectral imaging for efficient biomass assessment in precision agriculture. Full article

With advances in optical satellite remote sensing, urban flood mapping (UFM) leveraging water’s distinct spectral characteristics for water identification is preferred and has gained more attention. PlanetScope’s daily 3 m resolution imagery enables detailed and time-sensitive flood monitoring. Unlike machine learning, which requires extensive training data, thresholding methods offer a faster and more adaptable solution for binary classification. Three global (Yen’s, Otsu’s, Isodata) and three local (Niblack, Sauvola, Gonzalez) thresholding methods, with their parameters optimized for each case study, were assessed in this study. Additionally, a hybrid approach was proposed and evaluated. In this approach, local thresholds are computed for each pixel, using the respective local thresholding method. Then, a global threshold is derived by calculating the simple arithmetic mean of all these local thresholds. This global threshold is subsequently applied across the entire image to perform a binary classification, distinguishing flooded from non-flooded areas. To enhance water detection, we also evaluated 26 remote sensing indices. Each was computed using two formulations—the normalized difference and the ratio—where at least one of the eight PlanetScope bands was NIR or RedEdge to enhance water detection. We tested this methodology on three flooding events with different water coverage scenarios in Brazil (34% water coverage), the USA (11%), and Australia (21%). The model performance was validated using the Matthews correlation coefficient (MCC) and the Fowlkes–Mallows index (FMI). The results demonstrated that combining PlanetScope imagery with carefully selected remote sensing indices and thresholding techniques enhances efficient UFM. The hybrid methods outperformed the others by capturing local variations while maintaining global consistency, with the MCC and the FMI exceeding 0.9. The indices incorporating NIR and RedEdge, particularly NDRE, achieved the highest accuracy. However, each flood event was best classified by a different combination of method and index, indicating that it is important to carefully select the appropriate remote sensing indices and thresholding techniques for each specific case. This framework provides a fast, effective solution for UFM, adaptable to diverse urban environments and flood conditions. Full article

High soil moisture due to frequent excessive precipitation can lead to reductions in maize grain yields and increased nitrogen (N) loss. The traditional methods of computing N status in crops are destructive and time-consuming, especially in waterlogged fields. Therefore, in this study, we used unmanned aerial vehicle (UAV) remote sensing to evaluate the status of maize under different N rates and excessive soil moisture conditions. The experiment was performed using a split plot design with four replications, with soil moisture conditions as main plots and different N rates as sub-plots. The artificial intelligence SciPy (version 1.5.2) optimization algorithm and spherical function were used to estimate the economically optimum N rate under the different treatments. The computed EONR for CRS 2022 was 157 kg N ha⁻¹ for both treatments, with the maximum net return to N of USD 1203 ha⁻¹. In 2023, the analysis suggested a lower maximum attainable yield in excessive water conditions, with EONR pushed up to 197 kg N ha⁻¹ as compared to 185 kg N ha⁻¹ in the control treatment, resulting in a lower maximum net return to N of USD 884 ha⁻¹ as compared to USD 1019 ha⁻¹ in the control treatment. This study reveals a slight reduction of the fraction of NDRE at EONR to maximum NDRE under excessive water conditions, highlighting the need for addressing such abiotic stress circumstances when arriving at an N rate recommendation based on an N-rich strip concept. This study confirms the importance of sensing technology for N monitoring in maize, particularly in supporting decision making in nutrient management under adverse weather conditions. Full article

Wheat is a key staple crop globally, essential for food security and sustainable agricultural development. The results of this study highlight how innovative monitoring techniques, such as UAV-based multispectral imaging, can significantly improve agricultural practices by providing precise, real-time data on crop growth. This study utilized unmanned aerial vehicle (UAV)-based remote sensing technology at the wheat experimental field of the Hebei Academy of Agriculture and Forestry Sciences to capture the dynamic growth characteristics of wheat using multispectral data, aiming to explore efficient and precise monitoring and management strategies for wheat. A UAV equipped with multispectral sensors was employed to collect high-resolution imagery at five critical growth stages of wheat: tillering, jointing, booting, flowering, and ripening. The data covered four key spectral bands: green (560 nm), red (650 nm), red-edge (730 nm), and near-infrared (840 nm). Combined with ground-truth measurements, such as chlorophyll content and plant height, 21 vegetation indices were analyzed for their nonlinear relationships with wheat growth parameters. Statistical analyses, including Pearson’s correlation and stepwise regression, were used to identify the most effective indices for monitoring wheat growth. The Normalized Difference Red-Edge Index (NDRE) and the Triangular Vegetation Index (TVI) were selected based on their superior performance in predicting wheat growth parameters, as demonstrated by their high correlation coefficients and predictive accuracy. A random forest model was developed to comprehensively evaluate the application potential of multispectral data in wheat growth monitoring. The results demonstrated that the NDRE and TVI indices were the most effective indices for monitoring wheat growth. The random forest model exhibited superior predictive accuracy, with a mean squared error (MSE) significantly lower than that of traditional regression models, particularly during the flowering and ripening stages, where the prediction error for plant height was less than 1.01 cm. Furthermore, dynamic analyses of UAV imagery effectively identified abnormal field areas, such as regions experiencing water stress or disease, providing a scientific basis for precision agricultural interventions. This study highlights the potential of UAV-based remote sensing technology in monitoring wheat growth, addressing the research gap in systematic full-cycle analysis of wheat. It also offers a novel technological pathway for optimizing agricultural resource management and improving crop yields. These findings are expected to advance intelligent agricultural production and accelerate the implementation of precision agriculture. Full article

Fusarium head blight (FHB) is a serious fungal disease of wheat and other small cereal grains, significantly reducing grain yield and producing mycotoxins that affect food safety. There is a need for disease detection technologies to determine the right time to apply fungicides, as FHB infection begins before visible symptoms appear. Using multispectral remote sensing by an unmanned aircraft system (UAS), wheat plants were observed under field conditions infested with FHB and simultaneously protected with fungicides sprayed with four different types of nozzles, as well as corresponding control plots infested with FHB only. The results showed that the levels of deoxynivalenol (DON) differed significantly between the five treatments, indicating that the control had the highest DON concentration as no fungicide treatment was applied. This study revealed that the assessment of the normalized difference vegetation index (NDVI) after FHB infection could be useful for predicting DON accumulation in wheat, as a significant negative correlation between DON and NDVI values was measured 24 days after anthesis. The decreasing NDVI values at the end of the growth cycle were expected due to senescence and yellowing of the wheat spikes and leaves. Therefore, significant differences in the NDVI were observed between three measurement points on the 13th, 24th, and 45th day after anthesis. Additionally, the green normalized difference vegetation index (GNDVI) and normalized difference red-edge index (NDRE) were in significant positive correlation with the NDVI at 24th day after anthesis. The use of appropriate measurement points for the vegetation indices can offer the decisive advantage of enabling the evaluation of very large breeding trials or farmers’ fields where the timing of fungicide application is particularly important. Full article

Rice is one of the most extensively cultivated food crops in Northeast China. Estimating pre-harvest rice yield is important for accurately formulating field management strategies and swiftly assessing overall rice production. This can be achieved using a pixel-scale model, which estimates crop yield based on information from each pixel. Previous studies predominantly employed remote sensing indices, climatic data, and yield statistics of rice across either single or all growth periods for yield estimation. These approaches are limited by a delay in yield estimation and are insufficient in the exploration of time-series feature variables at the pixel scale. This study presents the development of a novel deep-learning framework designed for the early estimation of rice yield in Qian Gorlos County, Northeast China. The framework utilizes a long short-term memory neural network integrated with an attention mechanism (ALSTM). In this framework, the heading stage–milk ripening stage is the time window for early yield estimation, and the vegetation indices Normalized Difference Red Edge (NDRE), Green Chlorophyll Vegetation Index (GCVI), and Normalized Difference Water Index (NDWI) from the rice transplanting to the milk ripening stage are time-series feature variables. The yield estimation precision is R² = 0.88, RMSE = 341.82 kg/ha, MAE = 280.29 kg/ha, outperforming LASSO (R² = 0.71, RMSE = 567.10 kg/ha, MAE = 487.38 kg/ha), RF (R² = 0.79, RMSE = 506.70 kg/ha, MAE = 418.90 kg/ha), and LSTM (R² = 0.83, RMSE = 451.11 kg/ha, MAE = 326.31 kg/ha). The ALSTM introduced in this paper demonstrates its robustness after being generalized to the 2022 growing season. It can forecast the current-year rice yield two months prior to harvest, providing a valuable reference for developing field management strategies to enhance rice productivity. Full article

Agricultural land classification plays a pivotal role in food security and ecological sustainability, yet achieving accurate large-scale mapping remains challenging. This study presents methodological innovations through a multi-level feature enhancement framework that transcends traditional time series analysis. Using Shandong Province, northern China’s agricultural heartland, as a case study, we first established a foundation with time series red-edge vegetation indices (REVI) from Sentinel-2 imagery, uniquely combining the normalized difference red edge index (NDRE₇₀₅) and plant senescence reflectance index (PSRI). Moving beyond conventional time series analysis, we innovatively amplified key temporal characteristics through newly designed spatial feature parameters (SFPs) and phenological feature parameters (PFPs). This strategic enhancement of critical temporal points significantly improved classification performance by capturing subtle spatial patterns and phenological transitions that are often overlooked in traditional approaches. The study yielded three significant findings: (1) The synergistic application of NDRE₇₀₅ and PSRI significantly outperformed single-index approaches, demonstrating the effectiveness of our dual-index strategy; (2) The integration of SFPs and PFPs with time series REVI markedly enhanced feature discrimination at crucial growth stages, with PFPs showing superior capability in distinguishing agricultural land types through amplified phenological signatures; (3) Our optimal classification scheme (FC6), leveraging both enhanced spatial and phenological features, achieved remarkable accuracy (93.21%) with a Kappa coefficient of 0.9159, representing improvements of 4.83% and 0.0538, respectively, over the baseline approach. This comprehensive framework successfully mapped 120,996 km² of agricultural land, differentiating winter wheat–summer maize rotation areas (39.44%), single-season crop fields (36.16%), orchards (14.49%), and facility vegetable fields (9.91%). Our approach advances the field by introducing a robust, scalable methodology that not only utilizes the full potential of time series data but also strategically enhances critical temporal features for improved classification accuracy, particularly valuable for regions with complex farming systems and diverse crop patterns. Full article

Innovative methods for estimating commercial-scale switchgrass yields and feedstock quality are essential to optimize harvest logistics and biorefinery efficiency for sustainable aviation fuel production. This study utilized vegetation indices (VIs) derived from multispectral images to predict biomass yield and lignocellulose concentrations of advanced bioenergy-type switchgrass cultivars (“Liberty” and “Independence”) under two N rates (28 and 56 kg N ha⁻¹). Field-scale plots were arranged in a randomized complete block design (RCBD) and replicated three times at Urbana, IL. Multispectral images captured during the 2021–2023 growing seasons were used to extract VIs. The results show that linear and exponential models outperformed partial least square and random forest models, with mid-August imagery providing the best predictions for biomass, cellulose, and hemicellulose. The green normalized difference vegetation index (GNDVI) was the best univariate predictor for biomass yield (R² = 0.86), while a multivariate combination of the GNDVI and normalized difference red-edge index (NDRE) enhanced prediction accuracy (R² = 0.88). Cellulose was best predicted using the NDRE (R² = 0.53), whereas hemicellulose prediction was most effective with a multivariate model combining the GNDVI, NDRE, NDVI, and green ratio vegetation index (GRVI) (R² = 0.44). These findings demonstrate the potential of UAV-based VIs for the in-season estimation of biomass yield and cellulose concentration. Full article