Search Results (18)

Grassland biomass yield reflects a complex interaction of management intensity and environmental factors, yet quantifying the relative role of practices such as mowing and fertilization remains challenging. In this study, we introduce a multi-class machine learning framework to predict above-ground biomass on 150 permanent grassland plots across eight years (2009–2016) in Germany’s Biodiversity Exploratories and to evaluate the influence of key management variables. Following rigorous data cleaning, imputation of missing nitrogen values, feature standardization, and encoding of categorical practices, we trained CatBoost classifiers optimized via Bayesian hyperparameter search and mitigated class imbalance with ADASYN oversampling. We assessed model performance under binary, three-class, four-class, and five-class quantile-based categorizations, achieving test accuracies of 0.76, 0.57, 0.42, and 0.38, respectively. Across all schemes, mowing frequency and mineral nitrogen input emerged as the dominant predictors, while secondary variables such as drainage and conditioner use contributed as well. These results demonstrate that broad biomass categories can be forecast reliably from standardized management records, whereas finer distinctions necessitate additional environmental information or automated sensing to capture nonlinear effects and reduce reporting bias. This work shows both the potential and the limits of machine learning for informing sustainable grassland management and explainability thereof. Frequent mowing and higher mineral nitrogen inputs explained most of the predictable variation, enabling a 76% accurate separation of low and high biomass categories. Predictive accuracy fell below 60% for finer class resolutions, indicating that management records alone are insufficient for detailed yield forecasts without complementary environmental data. Full article

Observations of Very High Energy (VHE) gamma ray sources using the ground-based Imaging Atmospheric Cherenkov Telescopes (IACTs) play a pivotal role in understanding the non-thermal energetic phenomena and acceleration processes under extreme astrophysical conditions. However, detection of the VHE gamma ray signal from the astrophysical sources is very challenging, as these telescopes detect the photons indirectly by measuring the flash of Cherenkov light from the Extensive Air Showers (EAS) initiated by the cosmic gamma rays in the Earth’s atmosphere. This requires fast detection systems, along with advanced data acquisition and analysis techniques to measure the development of extensive air showers and the subsequent segregation of gamma ray events from the huge cosmic ray background, followed by the physics analysis of the signal. Here, we report the development of a python-based package for analyzing the data from the Major Atmospheric Cherenkov Experiment (MACE), which is operational at Hanle in India. The Python-based MACE data Analysis Package (PyMAP) analyzes data by using advanced methods and machine learning algorithms. Data recorded by the MACE telescope are passed through different utilities developed in the PyMAP to extract the gamma ray signal from a given source direction. We also propose a new image cleaning method called DIOS (Denoising Image of Shower) and compare its performance with the standard image cleaning method. The working performance of DIOS indicates an advantage over the standard method with an improvement of ≈$25\%$ in the sensitivity of MACE. Full article

In order to limit the ever-increasing consumption of new resources for material formulations, regulations and legislation require us to move from a linear to a circular economy and to find efficient ways to recycle, reuse and recover materials. Taking into account the principles of material circularity and waste reuse, this research study aims to produce thermoplastic composites using two types of industrial waste from neighbouring companies, namely waste polypropylene (wPP) from household production and carbon-fibre-reinforced epoxy composite scrap from a pultrusion company. The industrial scrap of the carbon-fibre-reinforced epoxy composites was either machined/ground to powder (pCFRC) and used directly as a reinforcement agent or subjected to a chemical digestion process to recover the carbon fibres (rCFs). Both pCFRC and rCF, at different weight ratios, were melt-blended with wPP. Prior to melt blending, both pCFRC and rCF were analysed for morphology by scanning electron microscopy (SEM). The pCFRC powder contains epoxy resin fragments with spherical to ellipsoidal shape and carbon fibre fragments. The rCFs are clean from the matrix, but they are slightly thicker and corrugated after the matrix digestion. Further, the morphologies of wPP/pCFRC and wPP/rCF were also investigated by SEM, while the thermal behaviour, i.e., transitions and changes in crystallinity, and thermal resistance were evaluated by differential scanning calorimetry (DSC) and thermogravimetric analysis (TGA), respectively. The strength of the interaction between the filler (i.e., pCFRC or rCF) and the wPP matrix and the processability of these composites were assessed by rheological studies. Finally, the mechanical properties of the systems were characterised by tensile tests, and as found, both pCFRC and rCF exert reinforcement effects, although better results were obtained using rCF. The wPP/pCFRC results are more heterogeneous than those of the wPP/rCF due to the presence of epoxy and carbon fibre fragments, and this heterogeneity could be considered responsible for the mechanical behaviour. Further, the presence of both pCFRC and rCF leads to a restriction of polymer chain mobility, which leads to an overall reduction in ductility. All the results obtained suggest that both pCFRC and rCF are good candidates as reinforcing fillers for wPP and that these complex systems could potentially be processed by injection or compression moulding. Full article

The new generation of available (i.e., PRISMA, ENMAP, DESIS) and future (i.e., ESA-CHIME, NASA-SBG) spaceborne hyperspectral missions provide unprecedented data for environmental and agricultural monitoring, such as crop trait assessment. This paper focuses on retrieving two crop traits, specifically Chlorophyll and Nitrogen content at the canopy level (CCC and CNC), starting from hyperspectral images acquired during the CHIME-RCS project, exploiting a self-supervised learning (SSL) technique. SSL is a machine learning paradigm that leverages unlabeled data to generate valuable representations for downstream tasks, bridging the gap between unsupervised and supervised learning. The proposed method comprises pre-training and fine-tuning procedures: in the first stage, a de-noising Convolutional Autoencoder is trained using pairs of noisy and clean CHIME-like images; the pre-trained Encoder network is utilized as-is or fine-tuned in the second stage. The paper demonstrates the applicability of this technique in hybrid approach methods that combine Radiative Transfer Modelling (RTM) and Machine Learning Regression Algorithm (MLRA) to set up a retrieval schema able to estimate crop traits from new generation space-born hyperspectral data. The results showcase excellent prediction accuracy for estimating CCC (R2 = 0.8318; RMSE = 0.2490) and CNC (R2 = 0.9186; RMSE = 0.7908) for maize crops from CHIME-like images without requiring further ground data calibration. Full article

Too often, the testing and evaluation of object detection, as well as the classification techniques for high-resolution remote sensing imagery, are confined to clean, discretely partitioned datasets, i.e., the closed-world model. In recent years, the performance on a number of benchmark datasets has exceeded 99% when evaluated using cross-validation techniques. However, real-world remote sensing data are truly big data, which often exceed billions of pixels. Therefore, one of the greatest challenges regarding the evaluation of machine learning models taken out of the clean laboratory setting and into the real world is the difficulty of measuring performance. It is necessary to evaluate these models on a grander scale, namely, tens of thousands of square kilometers, where it is intractable to the ground truth and the ever-changing anthropogenic surface of Earth. The ultimate goal of computer vision model development for automated analysis and broad area search and discovery is to augment and assist humans, specifically human–machine teaming for real-world tasks. In this research, various models have been trained using object classes from benchmark datasets such as UC Merced, PatternNet, RESISC-45, and MDSv2. We detail techniques to scan broad swaths of the Earth with deep convolutional neural networks. We present algorithms for localizing object detection results, as well as a methodology for the evaluation of the results of broad-area scans. Our research explores the challenges of transitioning these models out of the training–validation laboratory setting and into the real-world application domain. We show a scalable approach to leverage state-of-the-art deep convolutional neural networks for the search, detection, and annotation of objects within large swaths of imagery, with the ultimate goal of providing a methodology for evaluating object detection machine learning models in real-world scenarios. Full article

In this paper, we designed an automatic ground cleaning machine for the dedusting rooms of chicken houses to replace the manual daily cleaning of dust particles and fluff. The machine mainly comprised a power system, control system, frame and walking structure, ground cleaning system, and dedusting system. The automatic movement of the machine body in two vertical directions without turning, lifting, and lowering of the sweeper; the retraction and expansion of the sweeper support arm; the reciprocating movement of the sweeper relative to the machine body; and the timely separation of the dust particles and fluff from gas mixtures were achieved. Parameter optimization experiments on the machine were performed using a quadratic general rotary combination design considering the movement speed, rotation speed of the sweeper, and distance between the suction head nozzle and ground as experimental factors. The regression equations describing the relationship between the three experimental factors and the dust particle removal rate and fluff removal rate were obtained using Design-Expert 12 software, adequately reflecting the impact of the three experimental factors on the two experimental indexes. Further parameter optimization was conducted to obtain the optimized parameter combination at the same weight as the two experimental indexes: movement speed of 0.1 m/s, rotation speed of the sweeper of 198 r/min, and distance between the suction head nozzle and ground of 12 mm. The performance experiment on the machine was conducted using the optimized parameter combination, yielding a dust particle removal rate of 90.7% and fluff removal rate of 91.7%. The experimental results show that the machine exhibits good performance and stable operation, meeting the daily cleaning needs of large-, medium-, and small-scale rectangular dedusting rooms of chicken houses. Full article

As one of the core working parts of a combine harvester, the cleaning device directly affects the operation performance of the whole machine. This is especially the case on hilly and gently sloping terrain, which, due to the uneven ground, causes the combine harvester body to incline and material to accumulates on one side, resulting in a high cleaning loss rate. To solve this problem, a self-leveling cleaning screen device and a control system based on a fuzzy PID control algorithm are developed for a caterpillar harvester, enabling it to operate on gentle slopes of 10°. To verify the performance of the fuzzy PID algorithm applied to this system, simulation tests, response tests, comparison tests, and field tests were carried out. The indoor test results show that the system has a good tracking effect when the inclination amplitude does not exceed 10°. The maximum leveling error is −0.62°, the maximum leveling time is 1.85 s, and the maximum overshoot is 1.5°. The field test results show that when the tilt angle of the harvester body is within 10°, the system can stabilize the real-time leveling of the cleaning screen. Even with an increase in the tilt angle of the harvester, the cleaning loss of the harvester installed with the automatic leveling system can still be maintained at a low level. The cleaning loss rate of the harvester is 1.2% higher after leveling than during flat operation, which meets the accuracy requirements of the system design. Therefore, this system can be applied to grain combine harvesters and effectively reduce the cleaning loss caused by their operation. Full article

Conservation tillage technology can reduce wind erosion and soil erosion, improve soil fertility, avoid straw burning and relieve ecological pressure. It is an important measure to achieve sustainable agricultural development. In northeast China, there is a large amount of straw covering the ground after the corn machine harvest, which can easily lead to the blockage of the soil-touching parts during no-tillage seeding, affecting sowing quality and crop yield. In order to solve the above problems, the clearing and covering of a minimum-tillage planter for corn stubble was developed. The machine can complete multiple processes, such as seedbed preparation, seeding, fertilization, covering and suppression, straw covering, etc., in a single entity. This paper focuses on the design of the straw cleaning device and uses discrete element method software (EDEM 2018, Altair Engineering, Troy, MI, USA) to establish the straw cleaning device–straw–soil discrete element simulation model. The quadratic-regression orthogonal center-of-rotation combination test method is used to optimize the parameter combination of the machine, using the operating speed, the speed of the knife roller and the penetration depth of the knife as the test factors and using the rate of cleaning straw and the equivalent power consumption as the evaluation index. The results show that each factor has a significant influence on the performance evaluation indices, and the order of influence of each factor on the rate of cleaning straw is operation speed > penetration depth of knife > speed of knife roller, and the order of influence of each factor on the equivalent power consumption is penetration depth of knife > speed of knife roller > operation speed. The optimal combination of parameters is a 5.5–6.2 km/h operation speed, a 500 rpm speed of the knife roller, a 40 mm penetration depth of the knife, a straw-cleaning rate of more than 90% and an equivalent power consumption of less than 8 kW. This study provides technical and equipment support for the promotion of conservation tillage technology in Northeast China. Full article

Radon (Rn) is a biological threat to cells due to its radioactivity. It is capable of penetrating the human body and damaging cellular DNA, causing mutations and interfering with cellular dynamics. Human exposure to high concentrations of Rn should, therefore, be minimized. The concentration of radon in a room depends on numerous factors, such as room temperature, humidity level, existence of air currents, natural grounds of the buildings, building structure, etc. It is not always possible to change these factors. In this paper we propose a corrective measure for reducing indoor radon concentrations by introducing clean air into the room through forced ventilation. This cannot be maintained continuously because it generates excessive noise (and costs). Therefore, a system for predicting radon concentrations based on Machine Learning has been developed. Its output activates the fan control system when certain thresholds are reached. Full article

Estimation of rice plant height distribution plays a significant role in keeping the feed rate of rice combine harvesters stable. This is an effective way to guarantee the working stability of the whole machine, as a consequence, improving threshing and cleaning efficiency and reducing loss and damage rates. However, dense growth and leafy and bent branches of mature rice make it difficult to detect the lowest point of aggregated growing plants in three dimensional (3D) point cloud data. Therefore, the objective of this study was to put forward a method to estimate plant height distribution on the basis of a moving surface and 3D point cloud elevation. The statistical outlier removal (SOR) algorithm was used to reduce noise points far away from target point cloud body, and then moving surface fitting elevation was applied to achieve accurate classification of ground and crop point cloud data for plant height estimation. Experiments showed that, compared with the actual value, the average square root error (RMSE) of the estimation results was 8.29, the average absolute percentage error (MAPE) was 9.28%, and the average accuracy was 90%. The proposed method could accurately estimate the height of mature rice and is beneficial to calculating the feed rate in advance, which can provide a reference for further investigation in automatic and intelligent harvesting. Full article

Aiming at the problems of the poor passing capacity of machines and low cleaning rate of seed strip during wheat no-tillage sowing in annual double cropping areas of North China, a spiral discharge anti-blocking and row-sorting device (SDARD) was designed and is reported in this paper. After the straw was cut and chopped by the high-velocity rotating no-till anti-blocking knife group (NAKG), the straw was thrown into the spiral discharging mechanism (SDM) behind the NAKG. The chopped straw was discharged to the non-sowing area to reach the effect of seed strip cleaning through the interaction between the SDM and the row-sorting of straw mechanism (RSM). Based on a theoretical analysis for determining the parameters of crucial components, the quadratic rotation orthogonal combination test method was adopted, and the operating velocity of machines (OVM), the rotary velocity of the spiral shaft (RVSS), and the height of the holding hopper from the ground (HHHG) were selected as the test factors. The straw cleaning rate (SCR) was taken as the test index. The discrete element simulation test was carried out, the regression model of the SCR was established, and parameters optimization and field test were carried out. The results show that the significant order of the three influencing factors on the SCR was HHHG > OVM > RVSS. The optimal combination of operating parameters was that OVM was 5 km/h, RVSS was 80 r/min, and HHHG was 10 mm. Under the optimal parameter combination, the average SCR was 84.49%, which was 15.5% higher than the no-till planter without the device, and the passing capacity of machines was great, which met the agronomic requirements of no-tillage sowing of wheat in annual double cropping areas. This study could provide a reference for the design of no-tillage machines. Full article

Lakes are an important component of global water resources. Lake water bodies extraction based on satellite remote sensing mainly utilizes optical or radar data. However, due to the influence of water quality, ground features with low reflectivity, and smooth surface features, it is still challenging to accurately extract water bodies in complex geographic environments. In this work, we proposed a lake water bodies extraction method by fusing Sentinel-1/2 data. Firstly, the proposed method analyzed the difference of the spectral polarization features between water and non-water in complex geographical environment. Then, the spectral polarization and water index were fused to multidimensional features by feature stacking. Finally, support vector machines are used to classify. Six typical lakes (including urban, mountains, and polluted and clean lakes) in China were used to verify the mapping accuracy. The results showed that extracting lake water bodies by fusing Sentinel-1/2 data had a better performance than using optical or radar data solely, all types of lakes achieved better extraction results, the overall accuracy of lake water extraction is improved by 3%, and the error of commission and omission is controlled within 6%. Comparative experiments indicate that combine radar polarization information with spectral information is helpful to improve the accuracy of different types of lakes extraction in complex geographical environment. Full article

Advances in machine learning and computer vision, combined with increased access to unstructured data (e.g., images and text), have created an opportunity for automated extraction of building characteristics, cost-effectively, and at scale. These characteristics are relevant to a variety of urban and energy applications, yet are time consuming and costly to acquire with today’s manual methods. Several recent research studies have shown that in comparison to more traditional methods that are based on features engineering approach, an end-to-end learning approach based on deep learning algorithms significantly improved the accuracy of automatic building footprint extraction from remote sensing images. However, these studies used limited benchmark datasets that have been carefully curated and labeled. How the accuracy of these deep learning-based approach holds when using less curated training data has not received enough attention. The aim of this work is to leverage the openly available data to automatically generate a larger training dataset with more variability in term of regions and type of cities, which can be used to build more accurate deep learning models. In contrast to most benchmark datasets, the gathered data have not been manually curated. Thus, the training dataset is not perfectly clean in terms of remote sensing images exactly matching the ground truth building’s foot-print. A workflow that includes data pre-processing, deep learning semantic segmentation modeling, and results post-processing is introduced and applied to a dataset that include remote sensing images from 15 cities and five counties from various region of the USA, which include 8,607,677 buildings. The accuracy of the proposed approach was measured on an out of sample testing dataset corresponding to 364,000 buildings from three USA cities. The results favorably compared to those obtained from Microsoft’s recently released US building footprint dataset. Full article

With the implementation of the 2018–2020 Clean Air Action Plan (CAAP) the and impact from COVID-19 lockdowns in 2020, air pollution emissions in central and eastern China have decreased markedly. Here, by combining satellite remote sensing, re-analysis, and ground-based observational data, we established a machine learning (ML) model to analyze annual and seasonal changes in primary air pollutants in 2020 compared to 2018 and 2019 over central and eastern China. The root mean squared errors (RMSE) for the PM_2.5, PM₁₀, O₃, and CO validation dataset were 9.027 μg/m³, 20.312 μg/m³, 10.436 μg/m³, and 0.097 mg/m³, respectively. The geographical random forest (RF) model demonstrated good performance for four main air pollutants. Notably, PM_2.5, PM₁₀, and CO decreased by 44.1%, 43.2%, and 35.9% in February 2020, which was likely influenced by the COVID-19 lockdown and primarily lasted until May 2020. Furthermore, PM_2.5, PM₁₀, O₃, and CO decreased by 16.4%, 24.2%, 2.7%, and 19.8% in 2020 relative to the average values in 2018 and 2019. Moreover, the reduction in O₃ emissions was not universal, with a significant increase (~20–40%) observed in uncontaminated areas. Full article

Camelina is a low input crop than can be cultivated in rotation with cereals to provide vegetable oil suitable for bioenergy production, industrial applications and even as source of food for livestock. At large scale farming, camelina seeds are currently harvested using a combine harvester, equipped with a cereal header, but the literature still lacks the knowledge of the performance of the machine, the harvesting cost and the related loss of seeds. The present study aims to fulfill that gap by reporting the results obtained from an ad hoc harvest field test. Camelina seed yield was 0.95 Mg ha⁻¹ which accounted for the 18.60% of the total above ground biomass. Theoretical field capacity, effective field capacity and field efficiency were 3.38 ha h⁻¹, 3.17 ha h⁻¹ and 93.7% respectively, albeit the seed loss was 80.1 kg ha⁻¹ FM (7.82% w/w of the potential seed yield). The presence of material other than grain was rather high, 31.77% w/w, which implies a second step of cleaning to avoid undesired modification of the seed quality. Harvesting cost was estimated in 65.97 € ha⁻¹. Our findings provide evidence on the suitability to use a conventional combine harvester equipped with a cereal header for the harvesting of camelina seeds, although some improvements are required to reduce both seed loss and impurities. Full article