Search Results (28)

Remote sensing advancements have enhanced defoliation monitoring in forests, but distinguishing insect-specific damage from general canopy stress remains challenging due to overlapping spectral signatures. This study addresses this gap by analyzing multispectral reflectance changes in Eucalyptus dunnii caused by Gonipterus sp. n. 2 larval feeding and artificial defoliation (AD). A randomized complete block design with five replicates tested four treatments: No Damage, Medium (100 larvae/tree) and High (200 larvae/tree) larval inoculation, and AD (80% leaf removal). Twenty potted E. dunnii trees were monitored over 16 days using UAV-based multispectral 10-band imagery. Five multispectral flights were conducted during the experiment. The reduction in visible and near-infrared (NIR) reflectance likely reflects structural changes in canopy composition, namely an increased proportion of mature foliage. Both larval feeding and AD treatments decreased reflectance in these spectral regions, probably due to the removal of young leaves and exposure of older, darker leaves. This explanation is inferred from morphological observations; further biochemical measurements would be required to confirm the underlying mechanisms. Larval feeding and AD reduced chlorophyll-related vegetation indices (CVI, NDRE), decreased anthocyanin-related vegetation indices (mARI, ARI), and also caused a drop in relative carotene content (MTVI, CTRI/RE). The effects were strongest in the AD and peaked soon after the treatment, indicating that these pigment effects can mostly and also be attributed to the older leaves becoming more exposed. Statistically significant interactions between date and treatment were found for the pigment-sensitive indices, the Anthocyanin Reflectance Index (ARI) and the Chlorophyll Vegetation Index (CVI). They displayed opposite reflectance trends—CVI increased while ARI decreased—but followed a consistent pattern aligned with insect feeding. EVI values also exhibited a distinguishable pattern that matched this trend. Due to the inherent difficulty of studying insect feeding in natural settings, AD trials may serve as a practical proxy for assessing the impact of pest-induced damage on vegetation reflectance and physiological indices. Full article

Grassland productivity and precise monitoring of animal herbage intake are key requirements for sustainable grazing management in Mediterranean upland systems. This study aimed to evaluate the potential of uncrewed aerial vehicle (UAV)-based multispectral vegetation indices (VIs) combined with machine learning (ML) algorithms to estimate forage biomass, quality parameters and daily herbage dry matter intake (HDMI) of grazing ewes at the paddock scale. The experiment was conducted in a managed ryegrass–white clover meadow–pasture in southern Italy, where four plots were grazed sequentially by lactating Sarda ewes during spring–summer 2025. Ground measurements included pre- and post-grazing biomass inside and outside exclusion cages, botanical composition and forage quality. Concurrently, UAV multispectral imagery has been acquired, from which several VIs were computed. Pearson’s correlations were used to explore relationships between VIs and forage variables, and five ML algorithms. Indices such as MCARI2, MTVI2, MTVI, MSAVI and OSAVI showed the strongest associations with biomass and quality traits, while support vector machine and neural networks provided the best prediction accuracies, particularly for HDMI (R² up to 0.91). The integrated UAV–ML approach proved effective in simultaneously capturing spatial variability of pasture productivity and animal intake, supporting the development of operational precision grazing tools for heterogeneous Mediterranean grasslands. Full article

Leaf Area Index (LAI) is the total leaf area per unit of land surface area and is a crucial parameter for assessing vegetation growth and productivity. Machine learning regression algorithms are widely applied for LAI estimation. Due to spectral response variations among sensors and susceptibility of mangrove-derived variables to environmental noise suppression, obtaining sensitivity indices and optimal machine learning regression models is essential for retrieving mangrove LAI at the population scale. This study proposes a novel approach to processing and retrieving mangrove LAI data by integrating multispectral indices with machine learning methods. Box–Cox transformation and CatBoost-based feature selection were employed to obtain the optimal dataset. Random Forest (RF), Gradient Boosting Regression Trees (GBRT), and Categorical Boosting (CatBoost) algorithms were used to evaluate the accuracy of LAI retrieval from Unmanned Aerial Vehicle (UAV) and Gaofen-6 (GF-6) data. Results indicate that when LAI > 3, LAI does not immediately saturate as CVI, MTVI 2, and other indices increase, demonstrating higher sensitivity. UAV data outperformed GF-6 data in retrieving LAI for diverse mangrove populations; during model training, RF proved more suitable for small-sample datasets, while CatBoost effectively suppressed environmental noise. Both RF and CatBoost demonstrated higher robustness in estimating Avicennia marina (AM) (RF: R² = 0.704) and Aegiceras corniculatum (AC) (R² = 0.766), respectively. Spatial distribution analysis of LAI indicates that healthy AM and AC cover 85.36% and 96.67% of the area, respectively. Spartina alterniflora and aquaculture wastewater may be among the factors affecting the health of mangrove forests in the study area. LAI retrieval holds significant importance for mangrove health monitoring and risk early warning. Full article

Cotton (Gossypium hirsutum L.) is a primary global commercial crop, and accurate monitoring of its growth and yield prediction are essential for optimizing water management. This study integrates leaf area index (LAI) data derived from unmanned aerial system (UAS) imagery into the Decision Support System for Agrotechnology Transfer (DSSAT) model to improve cotton growth simulation and yield estimation. The results show that the normalized difference vegetation index (NDVI) exhibited higher estimation accuracy for the cotton LAI during the squaring stage (R² = 0.56, p < 0.05), whereas the modified triangle vegetation index (MTVI) and enhanced vegetation index (EVI) demonstrated higher and more stable accuracy in the flowering and boll-setting stages (R² = 0.64 and R² = 0.76, p < 0.05). After assimilating LAI data, the optimized DSSAT model accurately represented canopy development and yield variation under different irrigation levels. Compared with the DSSAT, the assimilated model reduced yield prediction error from 40–52% to 3.6–6.3% under 30%, 60%, and 90% irrigation. These findings demonstrate that integrating UAS-derived LAI data with the DSSAT substantially enhances model accuracy and robustness, providing an effective approach for precision irrigation and sustainable cotton management. Full article

Background/Objectives: Propionic acidemia (PA) and methylmalonic acidemia (MMA) affect methionine, threonine, valine (Val), and isoleucine (Ile) (MTVI) metabolism, leading to the production of highly neurotoxic organic acids. Treatment involves a diet restricted in natural proteins and supplemented with a protein substitute (PS) with traces of MTVI. The aim was to analyze natural protein and PS intake in relation to linear growth impairment in individuals with PA and MMA. Methods: We followed the PRISMA protocol. We considered articles published between 1970 and 2025. We determined the eligibility criteria for selecting articles and evaluated the quality. Results: Thirteen studies were selected: two case reports, eight longitudinal, three cohorts, and one cross-sectional. Articles demonstrated that natural protein intake decreases with age, consistent with previous reports, underscoring the need for PS supplementation to meet protein requirements. Subjects with PA and non-responsive MMA had greater restriction of natural proteins, and the majority required PS; a higher PS intake was negatively correlated with a higher height-for-age (H/A) z-score. When analyzing the ratio of protein to energy (P:E), a negative correlation was found between the intake of natural proteins and energy, and a positive correlation with H/A z-score (p-value < 0.05). Supplementation with PS increased leucine levels, causing an imbalance with MTVI amino acids. This imbalance led to the paradoxical need to supplement L-Val and L-Ile, both propiogenic amino acids. As a result, a decrease in the H/A z-score was observed, particularly in PA and non-responsive MMA. Responsive MMA tolerated more natural proteins, received a lower intake of PS, and had a better H/A z-score. Conclusions: Restriction of natural proteins and PS is associated with a lower H/A z-score, primarily in subjects with PA and non-responsive MMA. Full article

Aphids are significant pests of winter wheat, causing damage by feeding on plant sap and reducing crop yield and quality. This study evaluates the potential of hyperspectral remote sensing (350–2500 nm) and machine learning (ML) models for classifying healthy and aphid-infested wheat canopies. Field-based hyperspectral measurements were conducted at three growth stages—T1 (stem elongation–heading), T2 (flowering), and T3 (milky grain development)—with infestation levels categorized according to established economic thresholds (ET) for each growth stage. Spectral data were analyzed using Uniform Manifold Approximation and Projection (UMAP); vegetation indices; and ML classification models, including Logistic Regression (LR), k-Nearest Neighbors (KNNs), Support vector machines (SVMs), Random Forest (RF), and Light Gradient Boosting Machine (LGBM). The classification models achieved high performance, with F1-scores ranging from 0.88 to 0.99, and SVM and RF consistently outperforming other models across all input datasets. The best classification results were obtained at T2 with an F1-score of 0.98, while models trained on the full spectrum dataset showed the highest overall accuracy. Among vegetation indices, the Modified Triangular Vegetation Index, MTVI (r_pb = −0.77 to −0.82), and Triangular Vegetation Index, TVI (r_pb = −0.66 to −0.75), demonstrated the strongest correlations with canopy condition. These findings underscore the utility of canopy spectra and vegetation indices for detecting aphid infestations above ET levels, allowing for a clear classification of wheat fields into “treatment required” and “no treatment required” categories. This approach provides a precise and timely decision making tool for insecticide application, contributing to sustainable pest management by enabling targeted interventions, reducing unnecessary pesticide use, and supporting effective crop protection practices. Full article

Unmanned aerial vehicles (UAVs) provide images at decametric spatial resolutions. Their flexibility, efficiency, and low cost make it possible to apply UAV remote sensing to multisensor data acquisition. In this frame, the present study aims at employing RGB UAV images (at a 3 cm resolution) and multispectral images (at a 16 cm resolution) with related vegetation indices (VIs) for mapping surfaces according to their illumination. The aim is to map land cover in order to access temperature distribution and compare NDVI and MTVI2 dynamics as a function of their illuminance. The method, which is based on a linear discriminant analysis, is validated at different periods during the phenological cycle of the crops in place. A model based on a given date is evaluated, as well as the use of a generic model. The method provides a good capacity of separation between four classes: vegetation, no-vegetation, shade, and sun (average kappa of 0.93). The effects of agricultural practices on two adjacent plots of maize respectively submitted to conventional and conservation farming are assessed. The transition from shade to sun increases the brightness temperature by 2.4 °C and reduces the NDVI by 26% for non-vegetated surfaces. The conservation farming plot is found to be 1.9 °C warmer on the 11th of July 2019, with no significant difference between vegetation in the sun or shade. The results also indicate that the NDVI of non-vegetated areas is increased by the presence of crop residues on the conservation agriculture plot and by the effect of shade on the conventional plot which is different for MTVI2. Full article

Monitoring and mapping crop water stress and variability at a farm scale for cereals such as maize, one of the most common crops in developing countries with 200 million people around the world, is an important objective within precision agriculture. In this regard, unmanned aerial vehicle-obtained multispectral and thermal imagery has been adopted to estimate the crop water stress proxy (i.e., Crop Water Stress Index) in conjunction with algorithm machine learning techniques, namely, partial least squares (PLS), support vector machines (SVM), and random forest (RF), on a typical smallholder farm in southern Africa. This study addresses this objective by determining the change between foliar and ambient temperature (Tc-Ta) and vapor pressure deficit to determine the non-water stressed baseline for computing the maize Crop Water Stress Index. The findings revealed a significant relationship between vapor pressure deficit and Tc-Ta (R² = 0.84) during the vegetative stage between 10:00 and 14:00 (South Africa Standard Time). Also, the findings revealed that the best model for predicting the Crop Water Stress Index was obtained using the random forest algorithm (R² = 0.85, RMSE = 0.05, MAE = 0.04) using NDRE, MTCI, CCCI, GNDVI, TIR, Cl_Red Edge, MTVI2, Red, Blue, and Cl_Green as optimal variables, in order of importance. The results indicated that NIR, Red, Red Edge derivatives, and thermal band were some of the optimal predictor variables for the Crop Water Stress Index. Finally, using unmanned aerial vehicle data to predict maize crop water stress index on a southern African smallholder farm has shown encouraging results when evaluating its usefulness regarding the use of machine learning techniques. This underscores the urgent need for such technology to improve crop monitoring and water stress assessment, providing valuable insights for sustainable agricultural practices in food-insecure regions. Full article

Durum wheat (Triticum durum Desf.) is one of the most widely cultivated cereal species in the Mediterranean basin, supporting pasta, bread and other typical food productions. Considering its importance for the nutrition of a large population and production of high economic value, its supply is of strategic significance. Therefore, an early and accurate crop yield estimation may be fundamental to planning the purchase, storage, and sale of this commodity on a large scale. Multispectral (MS) remote sensing (RS) of crops using unpiloted aerial vehicles (UAVs) is a powerful tool to assess crop status and productivity with a high spatial–temporal resolution and accuracy level. The object of this study was to monitor the behaviour of thirty different durum wheat varieties commonly cultivated in Italy, taking into account their spectral response to different vegetation indices (VIs) and assessing the reliability of this information to estimate their yields by Pearson’s correlation and different machine learning (ML) approaches. VIs allowed us to separate the tested wheat varieties into different groups, especially when surveyed in April. Pearson’s correlations between VIs and grain yield were good (R² > 0.7) for a third of the varieties tested; the VIs that best correlated with grain yield were CVI, GNDVI, MTVI, MTVI2, NDRE, and SR RE. Implementing ML approaches with VIs data highlighted higher performance than Pearson’s correlations, with the best results observed by random forest (RF) and support vector machine (SVM) models. Full article

The increasing amount of rain produces a number of issues in Kerala, particularly in urban regions where the drainage system is frequently unable to handle a significant amount of water in such a short duration. Meanwhile, standard flood detection results are inaccurate for complex phenomena and cannot handle enormous quantities of data. In order to overcome those drawbacks and enhance the outcomes of conventional flood detection models, deep learning techniques are extensively used in flood control. Therefore, a novel deep hybrid model for flood prediction (DHMFP) with a combined Harris hawks shuffled shepherd optimization (CHHSSO)-based training algorithm is introduced for flood prediction. Initially, the input satellite image is preprocessed by the median filtering method. Then the preprocessed image is segmented using the cubic chaotic map weighted based k-means clustering algorithm. After that, based on the segmented image, features like difference vegetation index (DVI), normalized difference vegetation index (NDVI), modified transformed vegetation index (MTVI), green vegetation index (GVI), and soil adjusted vegetation index (SAVI) are extracted. The features are subjected to a hybrid model for predicting floods based on the extracted feature set. The hybrid model includes models like CNN (convolutional neural network) and deep ResNet classifiers. Also, to enhance the prediction performance, the CNN and deep ResNet models are fine-tuned by selecting the optimal weights by the combined Harris hawks shuffled shepherd optimization (CHHSSO) algorithm during the training process. This hybrid approach decreases the number of errors while improving the efficacy of deep neural networks with additional neural layers. From the result study, it clearly shows that the proposed work has obtained sensitivity (93.48%), specificity (98.29%), accuracy (94.98%), false negative rate (0.02%), and false positive rate (0.02%) on analysis. Furthermore, the proposed DHMFP–CHHSSO displays better performances in terms of sensitivity (0.932), specificity (0.977), accuracy (0.952), false negative rate (0.0858), and false positive rate (0.036), respectively. Full article

This work presents a remote sensing application to estimate the leaf area index (LAI) in two rice (Oryza sativa L.) varieties (IDIAP 52-05 and IDIAP FL 137-11), as a proxy for crop performance. In-field, homogeneous spectroradiometric measurements (350–1050 nm) were carried in two campaigns (June–November 2017 and January–March 2018), on a private farm, TESKO, located in Juan Hombrón, Coclé Province, Panama. The spectral fingerprint of IDIAP 52-05 plants was collected in four dates (47, 67, 82 and 116 days after sowing), according to known phenological stages of rice plant growth. Moreover, true LAI or green leaf area was measured from representative plants and compared to LAI calculated from normalized PlanetScope multi-spectral satellite images (selected according to dates close to the in-field collection). Two distinct estimation models were used to establish the relationships of measured LAI and two vegetational spectral indices (NDVI and MTVI2). The results show that the MTVI2 based model has a slightly higher predictive ability of true LAI ($R^2$ = 0.92, RMSE = 2.20), than the NDVI model. Furthermore, the satellite images collected were corrected and satellite LAI was contrasted with true LAI, achieving in average 18% for Model 2 for MTVI2, with the NDVI (Model 1) corrected model having a smaller error around 13%. This work provides an important advance in precision agriculture, specifically in the monitoring of total crop growth via LAI for rice crops in the Republic of Panama. Full article

The vegetation of the post-mining areas is subject to constant and significant changes. Reclamation works, carried out after the cessation of mineral extraction, contribute to the intensive development of new plant species. However, secondary deformations, occurring even many years after the end of exploitation, may cause the degradation of the vegetation cover. It is, therefore, an important issue to identify changes in flora conditions and to determine whether and to what extent past mining has a negative impact on the plant cover state. The objectives of this research have been as follows: (1) analysis of the flora condition in the post-mining area in the 1989–2019 period, (2) identification of sites with significant changes in vegetation state, and (3) modeling of the relationship between the identified changes in vegetation and former mining activities. The research was carried out in the area of the former opencast and underground lignite mine “Friendship of Nations—Babina Shaft,” which is located in the present-day Geopark (Western Poland), using Landsat TM/ETM+/OLI derived vegetation indices (NDVI, NDII, MTVI2) and GIS-based spatial regression. The results indicate a general improvement in flora condition, especially in the vicinity of post-mining waste heaps and former opencast excavations, with the exception of the northwestern part of the former mining field where the values of all of the analyzed vegetation indices have decreased. Also, four zones of statistically significant changes in the flora condition were identified. Finally, the developed GWR models demonstrate that former mining activities had a significant influence on changes in the plant cover state of the analyzed region. Full article

Warming is expected to intensify hydrological processes and reshape precipitation regimes, which is closely related to water availability for terrestrial ecosystems. Effects of the inter-annual precipitation changes on plant growth are widely concerned. However, it is not well-known how plant growth responds to intra-annual precipitation regime changes. Here, we compiled reanalysis climate data (ERA5) and four satellite-based vegetation indices, including the Normalized Difference Vegetation Index (NDVI), the Enhanced Vegetation Index (EVI), the Solar-induced Chlorophyll Fluorescence (SIF), and the Modified Triangular Vegetation Index (MTVI2), to evaluate the response of alpine grasslands (including alpine meadow and alpine steppe) to the change of precipitation regimes, especially to the intra-annual precipitation regimes on the Tibetan Plateau. We found monthly precipitation over the alpine steppe significantly increased in the growing season (May–September), but precipitation over the alpine meadow significantly increased only in the early growing season (May–June) (MJP) during the past four decades (1979–2019). The inter-annual plant growth (vegetation indices changes) on the alpine meadow was dominated by temperature, but it was driven by precipitation for the alpine steppe. On the intra-annual scale, the temperature sensitivity of the vegetation indices generally decreased but precipitation sensitivity increased during the growing season for both the alpine meadow and steppe. In response to the increase in MJP, we found the temperature sensitivity of the vegetation indices during the mid-growing season (July–August) (MGNDVI, MGEVI, MGSIF, and MGMTVI2) in the alpine meadow significantly increased (p < 0.01) while its precipitation sensitivity significantly decreased (p < 0.01). We infer that more MJP over the meadow may be the result of enhanced evapotranspiration, which is at the expense of soil moisture and even induces soil “drought” in the early growing season. This may be to elevate community water acquisition capacity through altering root mass allocation and community composition, consequently regulating the divergent climate sensitivities of vegetation growth in the mid-growing season. Our findings highlight that it is inadequate to regard precipitation as an indicator of water availability conditions for plant growth, which may limit our understanding of the response and acclimatization of plants to climate change. Full article

The adaptability and stability of new bread wheat cultivars that can be successfully grown in rainfed conditions are of paramount importance. Plant improvement can be boosted using effective high-throughput phenotyping tools in dry areas of the Mediterranean basin, where drought and heat stress are expected to increase yield instability. Remote sensing has been of growing interest in breeding programs since it is a cost-effective technology useful for assessing the canopy structure as well as the physiological traits of large genotype collections. The purpose of this study was to evaluate the use of a 4-band multispectral camera on-board an unmanned aerial vehicle (UAV) and ground-based RGB imagery to predict agronomic traits as well as quantify the best estimation of leaf area index (LAI) in rainfed conditions. A collection of 365 bread wheat genotypes, including 181 Mediterranean landraces and 184 modern cultivars, was evaluated during two consecutive growing seasons. Several vegetation indices (VI) derived from multispectral UAV and ground-based RGB images were calculated at different image acquisition dates of the crop cycle. The modified triangular vegetation index (MTVI2) proved to have a good accuracy to estimate LAI (R² = 0.61). Although the stepwise multiple regression analysis showed that grain yield and number of grains per square meter (NGm²) were the agronomic traits most suitable to be predicted, the R² were low due to field trials were conducted under rainfed conditions. Moreover, the prediction of agronomic traits was slightly better with ground-based RGB VI rather than with UAV multispectral VIs. NDVI and GNDVI, from multispectral images, were present in most of the prediction equations. Repeated measurements confirmed that the ability of VIs to predict yield depends on the range of phenotypic data. The current study highlights the potential use of VI and RGB images as an efficient tool for high-throughput phenotyping under rainfed Mediterranean conditions. Full article

Estimates of crop canopy chlorophyll content (CCC) can be used to monitor vegetation productivity, manage crop resources, and control disease and pests. However, making these estimates using conventional ground-based methods is time-consuming and resource-intensive when deployed over large areas. Although vegetation indices (VIs), derived from satellite sensor data, have been used to estimate CCC, they suffer from problems related to spectral saturation, soil background, and canopy structure. A new method was, therefore, proposed for combining the Medium Resolution Imaging Spectrometer (MERIS) terrestrial chlorophyll index (MTCI) and LAI-related vegetation indices (LAI-VIs) to increase the accuracy of CCC estimates for wheat and soybeans. The PROSAIL-D canopy reflectance model was used to simulate canopy spectra that were resampled to match the spectral response functions of the MERIS carried on the ENVISAT satellite. Combinations of the MTCI and LAI-VIs were then used to estimate CCC via univariate linear regression, binary linear regression and random forest regression. The accuracy using the field spectra and MERIS data was determined based on field CCC measurements. All the MTCI and LAI-VI combinations for the selected regression techniques resulted in more accurate estimates of CCC than the use of the MTCI alone (field spectra data for soybeans and wheat: R² = 0.62 and RMSE = 77.10 μg cm⁻²; MERIS satellite data for soybeans: R² = 0.24 and RMSE = 136.54 μg cm⁻²). The random forest regression resulted in better accuracy than the other two linear regression models. The combination resulting in the best accuracy was the MTCI and MTVI2 and random forest regression, with R² = 0.65 and RMSE = 37.76 μg cm⁻² (field spectra data) and R² = 0.78 and RMSE = 47.96 μg cm⁻² (MERIS satellite data). Combining the MTCI and a LAI-VI represents a further step towards improving the accuracy of estimation CCC based on multispectral satellite sensor data. Full article