Search Results (58)

This paper proposed a two-level hybrid stacking model for the classification of crops—wheat, soybean, and barley—based on multispectral orthomosaics obtained from uncrewed aerial vehicles. The proposed method unites gradient boosting algorithms (LightGBM, XGBoost, CatBoost) and tree ensembles (RandomForest, ExtraTrees, Attention-MLP deep neural network), whose predictions fuse at the meta-level using ExtraTreesClassifier. Spectral channels, along with a wide range of vegetation indices and their statistical characteristics, are used to construct the feature space. Experiments on an open dataset showed that the proposed model achieves high classification accuracy (Accuracy ≈ 95%, macro-F1 ≈ 0.95) and significantly outperforms individual algorithms across all key metrics. An analysis of the seasonal dynamics of vegetation indices confirmed the feasibility of monitoring phenological phases and early detection of stress factors. Furthermore, spatial segmentation of orthomosaics achieved approximately 99% accuracy in constructing crop maps, making the developed approach a promising tool for precision farming. The study’s results showed the high potential of hybrid ensembles for scaling to other crops and regions, as well as for integrating them into digital agricultural information systems. Full article

We develop a year-round, field-scale framework to retrieve soil moisture and map irrigation in an arid irrigation district where crop phenology and canopy dynamics undermine static, single-season approaches. However, the currently popular TVDI application is limited during non-growing seasons. To address this gap, we introduce a season-stratified TVDI scheme—based on the LST–EVI feature space with phenology-specific dry/wet edges—coupled with a non-growing-season inversion that fuses Sentinel-1 SAR and Landsat features and compares multiple regressors (PLSR, RF, XGBoost, and CNN). The study leverages 2023–2024 multi-sensor image time series for the Yichang sub-district of the Hetao Irrigation District (China), together with in situ topsoil moisture, meteorological records, a local cropping calendar, and district statistics for validation. Methodologically, EVI is preferred over NDVI to mitigate saturation under dense canopies; season-specific edge fitting stabilizes TVDI, while cross-validated regressors yield robust soil-moisture retrievals outside the growing period, with the CNN achieving the highest accuracy (test R² ≈ 0.56–0.61), outperforming PLSR/RF/XGBoost by approximately 12–38%. The integrated mapping reveals complementary seasonal irrigation patterns: spring irrigates about 40–45% of farmland (e.g., 43.39% on 20 May 2024), summer peaks around 70% (e.g., 71.42% on 16 August 2024), and autumn stabilizes near 20–25% (e.g., 24.55% on 23 November 2024), with marked spatial contrasts between intensively irrigated southwest blocks and drier northeastern zones. We conclude that season-stratified edges and multi-source inversions together enable reproducible, year-round irrigation detection at field scale. These results provide operational evidence to refine irrigation scheduling and water allocation, and support drought-risk management and precision water governance in arid irrigation districts. Full article

Yield prediction in wheat cultivated under semi-arid climatic conditions is gaining increasing importance for sustainable production strategies and decision support systems. In this study, a time-series-based modeling approach was implemented using sensor-based data (SPAD, NSPAD, NDVI, INSEY, and plant height measurements collected at four different Zadoks growth stages (ZD24, ZD30, ZD31, and ZD32). Five different machine learning algorithms (Random Forest, Gradient Boosting, AdaBoost, LightGBM, and XGBoost) were tested individually for each stage, and the model performances were evaluated using statistical metrics such as R²%, RMSE t/ha, and MAE t/ha. Modeling results revealed that the ZD31 stage (first node detectable) was identified as the most successful phase for prediction accuracy, with the XGBoost model achieving the highest R²% score (81.0). In the same model, RMSE and MAE values were calculated as 0.49 and 0.37, respectively. The LightGBM model also showed remarkable performance during the ZD30 stage, achieving an R²% of 78.0, an RMSE of 0.52, and an MAE of 0.40. The SHAP (SHapley Additive exPlanations) method used to interpret feature importance revealed that the NDVI and INSEY indices contributed the most significant values to prediction accuracy for yield. This study demonstrates that phenology-sensitive yield prediction approaches offer high potential for sensor-based digital applications. Furthermore, the integration of timing, model selection, and explainability provided valuable insights for the development of advanced decision support systems. Full article

Early-season crop mapping plays a critical role in yield estimation, agricultural management, and policy-making. However, most existing methods assign a uniform earliest identification time across provincial or broader extents, overlooking spatial heterogeneity in crop phenology and environmental conditions. This often results in delayed detection or reduced mapping accuracy. To address this issue, we proposed a zonal-based early-season mapping framework for winter wheat by integrating phenological and environmental factors. Aggregation zones across Shandong Province were delineated using Principal Component Analysis (PCA) based on factors such as start of season, end of season, temperature, slope, and others. On this basis, early-season winter wheat identification was conducted for each zone individually. Training samples were generated using the Time-Weighted Dynamic Time Warping (TWDTW) method. Time-series datasets derived from Sentinel-1/2 imagery (2021–2022) were processed on the Google Earth Engine (GEE) platform, followed by feature selection and classification using the Random Forest (RF) algorithm. Results indicated that Shandong Province was divided into four zones (A–D), with Zone D (southwestern Shandong) achieving the earliest mapping by early December with an overall accuracy (OA) of 97.0%. Other zones reached optimal timing between late December and late January, all with OA above 95%. The zonal strategy improved OA by 3.6% compared to the non-zonal approach, demonstrated a high correlation with official municipal-level statistics (R² = 0.97), and surpassed the ChinaWheat10 and ChinaWheatMap10 datasets in terms of crop differentiation and boundary delineation. Historical validation using 2017–2018 data from Liaocheng City, a prefecture-level city in Shandong Province, achieved an OA of 0.98 and an F₁ score of 0.96, further confirming the temporal robustness of the proposed approach. This zonal strategy significantly enhances the accuracy and timeliness of early-season winter wheat mapping at a large scale. Full article

Water use efficiency (WUE) connects two key processes in terrestrial ecosystems: the carbon and water cycles. Thus, it is important to evaluate temporal and spatial changes in WUE over a prolonged period. The spatiotemporal variation characteristics of the WUE in the Mongolian Plateau from 1982 to 2018 were analyzed based on the net primary productivity (NPP), evapotranspiration (ET), temperature, precipitation, and soil moisture. In this study, we used remote sensing data and various statistical methods to evaluate the spatiotemporal patterns of water use efficiency and their potential influencing factors on the Mongolian Plateau from 1982 to 2018. In total, 27.02% of the region witnessed a significant decline in the annual WUE over the 37 years. Two abnormal surges in the WUE_Season (April–October) were detected, from 1997 to 1998 and from 2007 to 2009. The trend in the annual WUE in some broadleaf forest areas in the middle and northeast of the Mongolian Plateau reversed from the original decreasing trend to an increasing trend. WUE has shown strong resilience in previous analytical studies, whereas the WUE in the artificial vegetation area in the middle of the Mongolian Plateau showed weak resilience. WUE had a significant positive correlation with precipitation, soil moisture, and the drought severity index (DSI) but a weak correlation with temperature. WUE had strong resistance to abnormal water disturbances; however, its resistance to the effects of temperature and DSI anomalies was weak. The degree of interpretation of vegetation changes for WUE was higher than that for meteorological factors, and WUE showed weak resistance to normalized difference vegetation index (NDVI) disturbances. Delaying the start of the vegetation growing season had an increasing effect on WUE, and the interaction between phenological and meteorological vegetation factors had a non-linear enhancing effect on WUE. Human activities have contributed significantly to the increase in WUE in the eastern, central, and southern regions of the Mongolian Plateau. These results provide a reference for the study of the carbon–water cycle in the Mongolian Plateau. Full article

Remote sensing data play a crucial role in capturing and evaluating eutrophication, providing a comprehensive view of spatial and temporal variations in water quality parameters. Chlorophyll-a concentration time series analysis aids in understanding the current trophic state of coastal waters and tracking changes over time, enabling the evaluation of water bodies’ trophic status. This research presents a novel and replicable methodology able to derive accurate phenological patterns using remote sensing data. The methodology proposed uses the two-decade MODIS-Aqua surface reflectance dataset, analyzing data from 30-point stations and calculating chlorophyll-a concentrations from NASA’s Ocean Color algorithm. Then, a correction process is implemented through a robust, simple statistical analysis by applying LOESS smoothing to detect and remove outliers from the extensive dataset. Different scenarios are reviewed and compared with field data to calibrate the proposed methodology accurately. The results demonstrate the methodology’s capacity to produce consistent chlorophyll-a time series and to present phenological patterns that can effectively identify key indicators and trends, resulting in valuable insights into the coastal body’s trophic state. The case study of the Ambracian Gulf is characterized as hypertrophic since algal bloom during August reaches up to 5 mg/m³, while the replicate case study of Aitoliko shows algal bloom reaching up to 2.5 mg/m³. Finally, the proposed methodology successfully identifies the positive chlorophyll-a climate tendencies of the two selected Greek water bodies. This study highlights the value of integrating statistical methods with remote sensing data for accurate, long-term monitoring of water quality in aquatic ecosystems. Full article

Dirofilaria immitis and Angiostrongylus vasorum are major parasitic nematodes of dogs. Many environmental and phenological changes have recently modified their geographic patterns in many countries; thus, this study has updated the distribution of D. immitis and A. vasorum in dog populations of selected regions of Central and Southern Italy. Also, collateral data on other endoparasites affecting the study population have been collected. Blood and fecal samples collected from 2000 dogs were tested using Knott’s test and copromicroscopy (i.e., Baermann’s and fecal flotation tests), respectively. Binomial logistic regression was performed to evaluate statistically significant associations between positivity for D. immitis and/or A. vasorum and potential risk factors. Overall, 35 (1.7%) and 62 (3.1%) dogs were positive for microfilariae of D. immitis and first stage larvae (L1) of A. vasorum, respectively, while 3 (0.1%) were co-infected by both nematodes. Microfilariae of Dirofilaria repens were found in 148 (7.4%) dogs, while at the flotation, eggs of Ancylostomatidae, Trichuris vulpis, and ascarids were found in the feces of 323 (16.5%), 249 (12.4%), and 172 (8.6%), dogs, respectively. Overall, 217 (10.8%) and 44 (2.2%) dogs were positive for eggs of Capillaria aerophila and Capillaria boehmi. The presence of cardiorespiratory clinical signs or non-specific signs, history of travel, and an age of >4 years old were significantly associated with positivity for D. immitis, while A. vasorum was significantly recorded in dogs with cardiorespiratory signs, or with a history of mollusk ingestion or permanent outdoor housing. These results confirm that D. immitis is enzootic in the investigated regions of Central and Southern Italy, even where it was rare/undetected until recently. Indeed, although some dogs positive for D. immitis had a history of travel in enzootic areas, the majority of them were never moved, indicating that they acquired the parasite in the region where they live. Additionally, A. vasorum is stably enzootic in the study areas, as also are other extraintestinal nematodes (i.e., D. repens and C. aerophila) that are more frequently detected today than in the past. A high level of vigilance and routine parasitological screening are necessary, considering the high prevalence of intestinal parasites in owned dogs that are also co-infected by respiratory parasites. The implementation of chemoprevention against D. immitis in dogs living in the examined area should be encouraged. Full article

Catch crops are intermediate crops sown between two main crop cycles. Their adoption into the cropping system has increased considerably in the last years due to its numerous benefits, in particular its potential in carbon fixation and preventing nitrogen leaching during winter. The growth period of catch crops in Germany is often marked by dense cloud cover, which limits land surface monitoring through optical remote sensing. In such conditions, synthetic aperture radar (SAR) emerges as a viable option. Despite the known advantages of SAR, the understanding of temporal behavior of radar parameters in relation to catch crops remains largely unexplored. Hence, in this study, we exploited the dense time series of Sentinel-1 data within the Copernicus Space Component to study the temporal characteristics of catch crops over a test site in the center of Germany. Radar parameters such as VV, VH, VH/VV backscatter, dpRVI (dual-pol Radar Vegetation Index) and VV coherence were extracted, and temporal profiles were interpreted for catch crops and preceding main crops along with in situ, temperature, and precipitation data. Additionally, we examined the temporal profiles of winter main crops (winter oilseed rape and winter cereals), that are grown parallel to the catch crop growing cycle. Based on the analyzed temporal patterns, we defined 22 descriptive features from VV, VH, VH/VV and dpRVI, which are specific to catch crop identification. Then, we conducted a Kruskal–Wallis test on the extracted parameters, both crop-wise and group-wise, to assess the significance of statistical differences among different catch crop groups. Our results reveal that there exists a unique temporal pattern for catch crops compared to main crops, and each of these extracted parameters possess a different sensitivity to catch crops. Parameters VV and VH are sensitive to phenological stages and crop structure. On the other hand, VH/VV and dpRVI were found to be highly sensitive to crop biomass. Coherence can be used to detect the sowing and harvest events. The preceding main crop analysis reveals that winter wheat and winter barley are the two dominant main crops grown before catch crops. Moreover, winter main crops (winter oilseed rape, winter cereals) cultivated during the catch crop cycle can be distinguished by exploiting the observed sowing window differences. The extracted descriptive features provide information about sowing, harvest, vigor, biomass, and early/late die-off nature specific to catch crop types. In the Kruskal–Wallis test, the observed high H-statistic and low p-value in several predictors indicates significant variability at 0.001 level. Furthermore, Dunn’s post hoc test among catch crop group pairs highlights the substantial differences between cold-sensitive and legume groups (p < 0.001). Full article

A range of remote sensing platforms provide high spatial and temporal resolution insights which are useful for monitoring vegetation growth. Very few studies have focused on fruit orchards, largely due to the inherent complexity of their structure. Fruit trees are mixed with inter-rows that can be grassed or non-grassed, and there are no standard protocols for ground measurements suitable for the range of crops. The assessment of biophysical variables (BVs) for fruit orchards from optical satellites remains a significant challenge. The objectives of this study are as follows: (1) to address the challenges of extracting and better interpreting biophysical variables from optical data by proposing new ground measurements protocols tailored to various orchards with differing inter-row management practices, (2) to quantify the impact of the inter-row at the Sentinel pixel scale, and (3) to evaluate the potential of Sentinel 2 data on BVs for orchard development monitoring and the detection of key phenological stages, such as the flowering and fruit set stages. Several orchards in two pedo-climatic zones in southeast France were monitored for three years: four apricot and nectarine orchards under different management systems and nine cherry orchards with differing tree densities and inter-row surfaces. We provide the first comparison of three established ground-based methods of assessing BVs in orchards: (1) hemispherical photographs, (2) a ceptometer, and (3) the Viticanopy smartphone app. The major phenological stages, from budburst to fruit growth, were also determined by in situ annotations on the same fields monitored using Viticanopy. In parallel, Sentinel 2 images from the two study sites were processed using a Biophysical Variable Neural Network (BVNET) model to extract the main BVs, including the leaf area index (LAI), fraction of absorbed photosynthetically active radiation (FAPAR), and fraction of green vegetation cover (FCOVER). The temporal dynamics of the normalised FAPAR were analysed, enabling the detection of the fruit set stage. A new aggregative model was applied to data from hemispherical photographs taken under trees and within inter-rows, enabling us to quantify the impact of the inter-row at the Sentinel 2 pixel scale. The resulting value compared to BVs computed from Sentinel 2 gave statistically significant correlations (0.57 for FCOVER and 0.45 for FAPAR, with respective RMSE values of 0.12 and 0.11). Viticanopy appears promising for assessing the PAI (plant area index) and FCOVER for orchards with grassed inter-rows, showing significant correlations with the Sentinel 2 LAI (R² of 0.72, RMSE 0.41) and FCOVER (R² 0.66 and RMSE 0.08). Overall, our results suggest that Sentinel 2 imagery can support orchard monitoring via indicators of development and inter-row management, offering data that are useful to quantify production and enhance resource management. Full article

Optimizing the prediction of maize (Zea mays L.) yields in smallholder farming systems enhances crop management and thus contributes to reducing hunger and achieving one of the Sustainable Development Goals (SDG 2—zero hunger). This research investigated the capability of unmanned aerial vehicle (UAV)-derived data and machine learning algorithms to estimate maize yield and evaluate its spatiotemporal variability through the phenological cycle of the crop in Bronkhorstspruit, South Africa, where UAV data collection took over four dates (pre-flowering, flowering, grain filling, and maturity). The five spectral bands (red, green, blue, near-infrared, and red-edge) of the UAV data, vegetation indices, and grey-level co-occurrence matrix textural features were computed from the bands. Feature selection relied on the correlation between these features and the measured maize yield to estimate maize yield at each growth period. Crop yield prediction was then conducted using our machine learning (ML) regression models, including Random Forest, Gradient Boosting (GradBoost), Categorical Boosting, and Extreme Gradient Boosting. The GradBoost regression showed the best overall model accuracy with R² ranging from 0.05 to 0.67 and root mean square error from 1.93 to 2.9 t/ha. The yield variability across the growing season indicated that overall higher yield values were predicted in the grain-filling and mature growth stages for both maize fields. An analysis of variance using Welch’s test indicated statistically significant differences in maize yields from the pre-flowering to mature growing stages of the crop (p-value < 0.01). These findings show the utility of UAV data and advanced modelling in detecting yield variations across space and time within smallholder farming environments. Assessing the spatiotemporal variability of maize yields in such environments accurately and timely improves decision-making, essential for ensuring sustainable crop production. Full article

The applicability of remote sensing enables the prediction of nutritional value, phytosanitary conditions, and productivity of crops in a non-destructive manner, with greater efficiency than conventional techniques. By identifying problems early and providing specific management recommendations in bean cultivation, farmers can reduce crop losses, provide more accurate and adequate diagnoses, and increase the efficiency of agricultural resources. The aim was to analyze the efficiency of vegetation indices using remote sensing techniques from UAV multispectral images and Sentinel-2A/MSI to evaluate the spectral response of common bean (Phaseolus vulgaris L.) cultivation in different phenological stages (V4 = 32 DAS; R5 = 47 DAS; R6 = 60 DAS; R8 = 74 DAS; and R9 = 89 DAS, in 99 days after sowing—DAS) with the application of doses of magnesium (0, 250, 500, and 1000 g ha⁻¹). The field characteristics analyzed were mainly chlorophyll content, productivity, and plant height in an experimental area by central pivot in the midwest region of Brazil. Data from UAV vegetation indices served as variables for the treatments implemented in the field and were statistically correlated with the crop’s biophysical parameters. The spectral response of the bean crop was also detected through spectral indices (NDVI, NDMI_GAO, and NDWI_GAO) from Sentinel-2A/MSI, with spectral resolutions of 10 and 20 m. The quantitative values of NDVI from UAV and Sentinel-2A/MSI were evaluated by multivariate statistical analysis, such as principal components (PC), and cophenetic correlation coefficient (CCC), in the different phenological stages. The NDVI and MCARI vegetation indices stood out for productivity prediction, with r = 0.82 and RMSE of 330 and 329 kg ha⁻¹, respectively. The TGI had the best performance in terms of plant height (r = 0.73 and RMSE = 7.4 cm). The best index for detecting the relative chlorophyll SPAD content was MCARI (r = 0.81; R² = 0.66 and RMSE = 10.14 SPAD), followed by NDVI (r = 0.81; R² = 0.65 and RMSE = 10.19 SPAD). The phenological stage with the highest accuracy in estimating productive variables was R9 (Physiological maturation). GNDVI in stages R6 and R9 and VARI in stage R9 were significant at 5% for magnesium doses, with quadratic regression adjustments and a maximum point at 500 g ha⁻¹. Vegetation indices based on multispectral bands of Sentinel-2A/MSI exhibited a spectral dynamic capable of aiding in the management of bean crops throughout their cycle. PCA (PC1 = 48.83% and PC2 = 39.25%) of the satellite multiple regression model from UAV vs. Sentinel-2A/MSI presented a good coefficient of determination (R² = 0.667) and low RMSE = 0.12. UAV data for the NDVI showed that the Sentinel-2A/MSI samples were more homogeneous, while the UAV samples detected a more heterogeneous quantitative pattern, depending on the development of the crop and the application of doses of magnesium. Results shown denote the potential of using geotechnologies, especially the spectral response of vegetation indices in monitoring common bean crops. Although UAV and Sentinel-2A/MSI technologies are effective in evaluating standards of the common bean crop cycle, more studies are needed to better understand the relationship between field variables and spectral responses. Full article

In Northeast China, transplanted rice cultivation has been adopted to extend the rice growing season and boost yields, responding to the limitations of the cumulative temperature zone and high food demand. However, direct-seeded rice offers advantages in water conservation and labour efficiency. The precise and timely monitoring of the distribution of different rice planting types is key to ensuring food security and promoting sustainable regional development. This study explores the feasibility of mapping various rice planting types using only early-stage satellite data from the rice growing season. We focused on Daxing Farm in Fujin City, Jiamusi City, Heilongjiang Province, for cropland plot extraction using Planet satellite imagery. Utilizing Sentinel-2 satellite imagery, we analysed the differences in rice’s modified normalized difference water index (MNDWI) during specific phenological periods. A multitemporal Gaussian mixture model (GMM) was developed, integrated with the maximum expectation algorithm, to produce binarized classification outcomes. These results were employed to detect surface changes and map the corresponding rice cultivation types. The probability of various rice cultivation types within arable plots was quantified, yielding a plot-level rice-cultivation-type mapping product. The mapping achieved an overall accuracy of 91.46% in classifying rice planting types, with a Kappa coefficient of 0.89. The area extraction based on arable land parcels showed a higher R² by 0.1109 compared to pixel-based area extraction and a lower RMSE by 0.468, indicating more accurate results aligned with real statistics and surveys, thus validating our study’s method. This approach, not requiring labelled samples or many predefined parameters, offers a new method for rapid and feasible mapping, especially suitable for direct-seeded rice areas in Northeast China. It fills the gap in mapping rice distribution for different planting types, supporting water management in rice fields and policies for planting-method changes. Full article

Rust is one of the main diseases affecting wheat crops in Spain, causing significant yield and quality losses. Research on its identification and quantification in the air is a worldwide priority due to the importance of this crop as a source of food and feed. The objective of this study is to determine the temporal variation of airborne spores of Puccinia and their relationship with meteorological variables and the phenological development of a wheat crop in Northwestern Spain during two growing seasons. The study was conducted in A Limia, Ourense, located in Northwestern Spain, during the wheat growing seasons of 2021 and 2022. The Lanzoni VPPS 2010 spore trap was used to collect airborne spores, which were identified using optical microscopy. The wheat growing season was less than 95 days during both years, and wheat rust spores were detected during all phenological stages of the crop. Concentrations were higher than 100 spores/m³ from the booting stage to senescence, mainly in 2021. Statistical analyses showed that temperature was the meteorological variable that most influenced Puccinia concentrations in the air in both years. The modification of a prediction model proposed by other authors for wheat rust, which takes into account mean temperature (10–25 °C), dew point temperature (<5 °C), and nighttime temperature (10–20 °C), allowed us to tentatively predict the increase in Puccinia concentrations in the year 2022 when these conditions occurred for four or five consecutive days. This research is the first in Spain to report the presence of rust-causing Puccinia spores in the air during all phenological stages of the wheat crop and provides useful information for designing management strategies, considering temperature values. Full article

Rice is a globally significant staple food crop. Therefore, it is crucial to have adequate tools for monitoring changes in the extent of rice paddy cultivation. Such a system would require a sustainable and operational workflow that employs open-source medium to high spatial and temporal resolution satellite imagery and efficient classification techniques. This study used similar phenological data from Sentinel-2 (S2) optical and Sentinel-1 (S1) Synthetic Aperture Radar (SAR) satellite imagery to identify paddy rice distribution with deep learning (DL) techniques. Using Google Earth Engine (GEE) and U-Net Convolutional Neural Networks (CNN) segmentation, a workflow that accurately delineates smallholder paddy rice fields using multi-temporal S1 SAR and S2 optical imagery was investigated. The study′s accuracy assessment results showed that the optimal dataset for paddy rice mapping was a fusion of S2 multispectral bands (visible and near infra-red (VNIR), red edge (RE) and short-wave infrared (SWIR)), and S1-SAR dual polarization bands (VH and VV) captured within the crop growing season (i.e., vegetative, reproductive, and ripening). Compared to the random forest (RF) classification, the DL model (i.e., ResU-Net) had an overall accuracy of 94% (three percent higher than the RF prediction). The ResU-Net paddy rice prediction had an F1-Score of 0.92 compared to 0.84 for the RF classification generated using 500 trees in the model. Using the optimal U-Net classified paddy rice maps for the dates analyzed (i.e., 2016–2020), a change detection analysis over two epochs (2016 to 2018 and 2018 to 2020) provided a better understanding of the spatial–temporal dynamics of paddy rice agriculture in the study area. The results indicated that 377,895 and 8551 hectares of paddy rice fields were converted to other land-use over the first (2016–2018) and second (2018–2020) epochs. These statistics provided valuable insight into the paddy rice field distribution changes across the selected districts analyzed. The proposed DL framework has the potential to be upscaled and transferred to other regions. The results indicated that the approach could accurately identify paddy rice fields locally, improve decision making, and support food security in the region. Full article

Soils contaminated by waste motor oil (WMO) affect their fertility, so it is necessary to recover them by means of an efficient and safe bioremediation technique for agricultural production. The objectives were: (a) to biostimulate the soil impacted by WMO by applying crude fungal extract (CFE) and Cicer arietinum as a green manure (GM), and (b) phytoremediation using Sorghum vulgare with Rhizophagus irregularis and/or Rhizobium etli to reduce the WMO below the maximum value according to NOM-138 SEMARNAT/SS or the naturally detected one. Soil impacted by WMO was biostimulated with CFE and GM and then phytoremediated by S. vulgare with R. irregularis and R. etli. The initial and final concentrations of WMO were analyzed. The phenology of S. vulgare and colonization of S. vulgaris roots by R. irregularis were measured. The results were statistically analyzed by ANOVA/Tukey’s HSD test. The WMO in soil that was biostimulated with CFE and GM, after 60 days, was reduced from 34,500 to 2066 ppm, and the mineralization of hydrocarbons from 12 to 27 carbons was detected. Subsequently, phytoremediation with S. vulgare and R. irregularis reduced the WMO to 86.9 ppm after 120 days, which is a concentration that guarantees the restoration of soil fertility for safe agricultural production for human and animal consumption. Full article