Explore the vast range of titles available.

Crop yield estimation is important for formulating informed regional and national food trade policies. The introduction of remote sensing in agricultural monitoring makes accurate estimation of regional crop yields possible. However, remote sensing images and crop distribution maps with coarse spatial resolution usually cause inaccuracy in yield estimation due to the existence of mixed pixels. This study aimed to estimate the annual yields of maize and sunflower in Hetao Irrigation District in North China using 30 m spatial resolution HJ-1A/1B CCD images and high accuracy multi-year crop distribution maps. The Normalized Difference Vegetation Index (NDVI) time series obtained from HJ-1A/1B CCD images was fitted with an asymmetric logistic curve to calculate daily NDVI and phenological characteristics. Eight random forest (RF) models using different predictors were developed for maize and sunflower yield estimation, respectively, where predictors of each model were a combination of NDVI series and/or phenological characteristics. We calibrated all RF models with measured crop yields at sampling points in two years (2014 and 2015), and validated the RF models with statistical yields of four counties in six years. Results showed that the optimal model for maize yield estimation was the model using NDVI series from the 120th to the 210th day in a year with 10 days’ interval as predictors, while that for sunflower was the model using the combination of three NDVI characteristics, three phenological characteristics, and two curve parameters as predictors. The selected RF models could estimate multi-year regional crop yields accurately, with the average values of root-mean-square error and the relative error of 0.75 t/ha and 6.1% for maize, and 0.40 t/ha and 10.1% for sunflower, respectively. Moreover, the yields of maize and sunflower can be estimated fairly well with NDVI series 50 days before crop harvest, which implicated the possibility of crop yield forecast before harvest.

Background The cardioprotective effect of remote ischemic preconditioning (RIPC) in cardiovascular surgery is controversial. This study investigated whether RIPC combined with remote ischemic postconditioning (RIPostC) reduces myocardial injury to donor hearts in patients undergoing heart transplantation. Methods One hundred and twenty patients scheduled for orthotopic heart transplantation were enrolled and randomly assigned to an RIPC+RIPostC group ( n  = 60) or a control (n = 60) group. In the RIPC+RIPostC group, after anesthesia induction, four cycles of 5-min of ischemia and 5-min of reperfusion were applied to the right upper limb by a cuff inflated to 200 mmHg (RIPC) and 20 min after aortic declamping (RIPostC). Serum cardiac troponin I (cTnI) levels were determined preoperatively and at 3, 6, 12, and 24 h after aortic declamping. Postoperative clinical outcomes were recorded. The primary endpoint was a comparison of serum cTnI levels at 6 h after aortic declamping. Results Compared with the preoperative baseline, in both groups, serum cTnI levels peaked at 6 h after aortic declamping. Compared with the control group, RIPC+RIPostC significantly reduced serum cTnI levels at 6 h after aortic declamping (38.87 ± 31.81 vs 69.30 ± 34.13 ng/ml, P  = 0.02). There were no significant differences in in-hospital morbidity and mortality between the two groups. Conclusion In patients undergoing orthotopic heart transplantation, RIPC combined with RIPostC reduced myocardial injury at 6 h after aortic declamping, while we found no evidence of this function provided by RIPC+RIPostC could improve clinical outcomes. Trial registration Trial Registration Number: chictr.org.cn . no. ChiCTR-INR-16010234 (prospectively registered). The initial registration date was 9/1/2017.

Marine environment monitoring has attracted more and more attention due to the growing concern about climate change. During the past couple of decades, advanced information and communication technologies have been applied to the development of various marine environment monitoring systems. Among others, the Internet of Things (IoT) has been playing an important role in this area. This paper presents a review of the application of the Internet of Things in the field of marine environment monitoring. New technologies including advanced Big Data analytics and their applications in this area are briefly reviewed. It also discusses key research challenges and opportunities in this area, including the potential application of IoT and Big Data in marine environment protection.

At the heart of the smartLAB, says project leader Sascha Beutel, who is at the Institute of Technical Chemistry of Leibniz University in Hanover, is a laboratory information system to which all lab components will be connected and controlled, from 'intelligent', self-cleaning lab benches to smart safety goggles that can project chemical safety information and augmente d-re ality displays. Sensors can monitor temperature, humidity, and carbon dioxide and oxygen levels, as well as vibration, light intensity and mass air flow.

A technological revolution in farming led by advances in robotics and sensing technologies looks set to disrupt modern practice.

This study aimed to evaluate land use/land cover changes (1987–2017), prediction (2032–2047), and identify the drivers of Majang Forest Biosphere Reserves. Landsat image (TM, ETM+, and OLI-TIRS) and socioeconomy data were used for the LU/LC analysis and its drivers of change. The supervised classification was also employed to classify LU/LC. The CA-Markov model was used to predict future LU/LC change using IDRISI software. Data were collected from 240 households from eight kebeles in two districts to identify LU/LC change drivers. Five LU/LC classes were identified: forestland, farmland, grassland, settlement, and waterbody. Farmland and settlement increased by 17.4% and 3.4%, respectively; while, forestland and grassland were reduced by 77.8% and 1.4%, respectively, from 1987 to 2017. The predicted results indicated that farmland and settlement increased by 26.3% and 6.4%, respectively, while forestland and grassland decreased by 66.5% and 0.8%, respectively, from 2032 to 2047. Eventually, agricultural expansion, population growth, shifting cultivation, fuel wood extraction, and fire risk were identified as the main drivers of LU/LC change. Generally, substantial LU/LC changes were observed and will continue in the future. Hence, land use plan should be proposed to sustain resource of Majang Forest Biosphere Reserves, and local communities’ livelihood improvement strategies are required to halt land conversion.

Automatic animal detection has become a critical capability in ecology, conservation, agriculture, and public safety, driven by the rapid growth of visual data collected through camera traps, UAVs, and remote sensors. The necessity of this study arises from the increasing demand to understand and apply these underlying detection techniques in practical domains such as animal husbandry, farming, and livestock management, where timely and accurate animal identification directly impacts productivity, welfare, and safety. Traditional convolutional neural networks (CNNs) have demonstrated strong accuracy in static or controlled environments but often face limitations in computational cost and inference speed. In contrast, the You Only Look Once (YOLO) family of one-stage detectors has revolutionized animal detection by achieving real-time performance while maintaining competitive accuracy across challenging geospatial environments. This review provides a chronological synthesis of detection approaches, tracing the evolution from handcrafted features and two-stage CNN-based models to modern YOLO architectures and transformer-enhanced frameworks. A detailed comparative analysis is presented, highlighting trade-offs in accuracy, speed, robustness, and deployment feasibility across diverse datasets, including camera trap imagery, UAV-based surveys, and satellite observations. Persistent challenges such as small-object detection, class imbalance, and limited cross-geographical generalization are discussed alongside enhancement strategies, including attention mechanisms, few-shot learning, and domain adaptation. Furthermore, practical deployment considerations are explored, with emphasis on edge computing platforms such as Jetson Nano, Coral TPU, and UAV-embedded systems. This review adopts a systematic methodology following PRISMA guidelines, covering studies published between 2015 and 2025, from which 142 were included after screening. Comparative findings show that on camera-trap datasets, transformer-augmented YOLO variants achieve up to 94% mAP under controlled illumination, while lightweight YOLOv7-SE and YOLOv8 architectures offer superior real-time performance ( 60 FPS) on UAV-based imagery. However, large-scale deployment remains constrained by edge-device memory limits and cross-domain generalization challenges.

Understanding drought from multiple perspectives is critical due to its complex interactions with crop production, especially in India. However, most studies only provide singular view of drought and lack the integration with specific crop phenology. In this study, four time series of monthly meteorological, hydrological, soil moisture, and vegetation droughts from 1981 to 2013 were reconstructed for the first time. The wheat growth season (from October to April) was particularly analyzed. In this study, not only the most severe and widespread droughts were identified, but their spatial-temporal distributions were also analyzed alone and concurrently. The relationship and evolutionary process among these four types of droughts were also quantified. The role that the Green Revolution played in drought evolution was also studied. Additionally, the trends of drought duration, frequency, extent, and severity were obtained. Finally, the relationship between crop yield anomalies and all four kinds of drought during the wheat growing season was established. These results provide the knowledge of the most influential drought type, conjunction, spatial-temporal distributions and variations for wheat production in India. This study demonstrates a novel approach to study drought from multiple views and integrate it with crop growth, thus providing valuable guidance for local drought mitigation.

The test 30 years ago of what remote sensing could tell us about our own planet shows the value of looking with unbiased eyes at what we think we already know. The test 30 years ago of what remote sensing could tell us about our own planet shows the value of looking with unbiased eyes at what we think we already know.

Trees outside forests (TOFs) are an important natural resource that contributes substantially to national biomass and carbon stocks and to the livelihood of people in many regions. Over the last decades, decision makers have become increasingly aware of the importance of TOF, and as a consequence, this tree resource is nowadays often considered in forest monitoring systems. Our review shows that in many cases, TOF are included in national forest inventories, applying traditional methodologies with relatively sparse networks of field sample plots. Only in some countries, such as India, the design of the inventories has considered the special features of how TOFs occur in the landscape. Several research studies utilising remote sensing for monitoring TOF have been conducted lately, but very few studies include comparative studies to optimise sampling strategies for TOF. Our review indicates that methods combining remote sensing and field surveys appear to be very promising, especially when remote sensing techniques that assess both the horizontal and vertical structures of tree resources are applied. For example, two-phase sampling strategies with laser scanning in the first phase and a field survey in the second phase appear to be effective for assessing TOF resources. However, TOFs often exhibit different characteristics than forest trees. Thus, to improve TOF monitoring, there is often a need to develop models, e.g. for biomass assessment, that are specifically adapted to this tree resource. Alternatively, field-based remote sensing methods that provide structural information about individual trees, notably terrestrial laser scanning, could be further developed for TOF monitoring applications. This also would have a potential to reduce the problem of accessing TOF during field surveys, which is a problem, for example, in countries where TOF are present on intensively utilised private grounds like gardens and agricultural fields.