Search Results (109)

Agriculture’s sustainable growth necessitates the application of advanced science and technology to ensure the sensible use of resources and improve the agricultural economy’s long-term stability. In this study, apple trees were employed as research objects throughout the spring (NSS) and autumn shoot stop-growing stage (ASS), and the data source was canopy hyperspectral data of fruit trees collected using ASD near-earth sensors, which was then combined with multiple sensitive wavelength screening algorithms and machine learning models to create an efficient and accurate apple yield estimation system. This is critical for guiding fruit farmers’ production, maintaining market supply and demand balances, fostering stable agricultural economy development, and providing a scientific basis and technical support for agricultural sustainability. Firstly, the fruit tree canopy hyperspectral data and apple tree yield data were collected, and the Savitsky–Golay convolution smoothing method (SG) was used to preprocess the canopy hyperspectral data. Secondly, six algorithms—Competitive Adaptive Re-weighting Sampling (CARS), Genetic Algorithm (GA), Successive Projections Algorithm (SPA), Uninformative Variable Elimination Algorithm (UVE), Variable Iteration Spatial Shrinking Algorithm (VISSA), and Variable Combination Population Algorithm (VCPA)—were employed to screen for the sensitive wavelengths related to apple tree yield, then preferring three methods for two-by-two combinations to determine the optimal algorithm combinations. Finally, using the best algorithm combinations, we built the apple yield linear model partial least squares regression (PLSR) and three machine learning models, Random Forest (RF), Cubist, and XGBoost, to screen for the best estimation model. The results demonstrated that ASS was the best fertility period for estimating yield; the validation set of the model constructed using each algorithm in ASS had a higher R² of 0.05–0.51 and a lower RMSE of 0.21–5.33 than those in NSS. The three algorithms preferred were CARS, GA, and VISSA. After combining the three algorithms in two combinations, the best combination of VISSA-CARS was found. The RF model established based on the best VISSA-CARS combination algorithm is the best model for apple yield estimation, with a validation set R² = 0.78 and RMSE = 6.03. The findings of this study may provide a new concept for accurately and quickly estimating apple yield, allowing fruit growers to improve production efficiency and promote agricultural sustainability. Full article

Metabolic pathway modeling, essential for understanding organism metabolism, is pivotal in predicting genetic mutation effects, drug design, and biofuel development. Enhancing these modeling techniques is crucial for achieving greater prediction accuracy and reliability. However, the limited experimental data or the complexity of the pathway makes it challenging for researchers to predict phenotypes. Deep learning (DL) is known to perform better than other Machine Learning (ML) approaches if the right conditions are met (i.e., a large database and good choice of parameters). Here, we use a knowledge-based model to massively generate synthetic data and extend a small initial dataset of experimental values. The main objective is to assess if DL can perform at least as well as other ML approaches in flux prediction, using 68,950 instances. Two processing methods are used to generate DL models: cross-validation and repeated holdout evaluation. DL models predict the metabolic fluxes with high precision and slightly outperform the best-known ML approach (the Cubist model) with a lower RMSE (≤0.01) in both cases. They also outperform the PLS model (RMSE ≥ 30). This study is the first to use DL to predict the overall flux of a metabolic pathway only from variations of enzyme concentrations. Full article

Accurate crop yield prediction is crucial for optimizing cultivation practices and informing breeding decisions. Integrating UAV-acquired multispectral datasets with advanced machine learning methodologies has markedly refined the accuracy of crop yield forecasting. This study aimed to construct a robust and versatile yield prediction model for multi-genotyped oat varieties by investigating 14 modeling scenarios that combine multispectral data from four key growth stages. An ensemble learning framework, StackReg, was constructed by stacking four base algorithms—ridge regression (RR), support vector machines (SVM), Cubist, and extreme gradient boosting (XGBoost)—to predict oat yield. The results show that, for single growth stages, base models achieved R² values within the interval of 0.02 to 0.60 and RMSEs ranging from 391.50 to 620.49 kg/ha. By comparison, the StackReg improved performance, with R² values extending from 0.25 to 0.61 and RMSEs narrowing to 385.33 and 542.02 kg/ha. In dual-stage and multi-stage settings, the StackReg consistently surpassed the base models, reaching R² values of up to 0.65 and RMSE values as low as 371.77 kg/ha. These findings underscored the potential of combining UAV-derived multispectral imagery with ensemble learning for high-throughput phenotyping and yield forecasting, advancing precision agriculture in oat cultivation. Full article

The need to maintain soil health and produce more food worldwide has increased, and soil analysis is essential for its management. Although spectroscopy has emerged as an important tool, it is important to focus primarily on predictive modeling procedures rather than specific protocols. This article aims to contribute to a routine work sequence in a hybrid laboratory that seeks to provide the best data for its users. In this study, 18,730 soil samples from the state of Paraná, Brazil, were analyzed using three different laboratories, sensors and geometries for data acquisition. Thirty soil properties were analyzed, some using different chemical methodologies for comparison purposes. After a spectral reading, two literary protocols were applied, and the final prediction results were observed. We applied cubist models, which were the best for our population. The combination of different spectral analysis systems, with a standardized protocol using LB for the ISS detection of discrepant samples, was shown to significantly improve the accuracy of predictions for 21 of the 30 soil properties analyzed, highlighting the importance of choosing the extraction methodology and improving data quality, which have a significant impact on laboratory analyses, reaffirming spectroscopy as an essential tool for the efficient and sustainable management of soil resources. Full article

Soil organic carbon (SOC) sequestration in cropland is not only instrumental in combating climate change, but it also significantly enhances soil fertility. It is imperative to precisely and accurately quantify the SOC sequestration potential and assess the relative significance of various multiple explanatory factors in a timely manner. We studied 555 soil samples from the cropland topsoil (0–15 cm) across the black soil region in Northeast China between the years 2021 and 2022, and we identified 16 significant impact factors using one-way ANOVA and Pearson correlation coefficient analysis. In addition, the Random Forest (RF) model outperformed the Cubist model in predicting the spatial distribution of SOC contents. The predicted ranges of SOC contents span from 5.24 to 43.93 g/kg, with the average SOC content using the RF model standing at 17.24 g/kg in Northeast China. Stepwise regression and structural equation modeling revealed climate and topography as key factors affecting SOC distribution. The SOC density in the study area varied from 0.51 to 9.11 kg/m², averaging 3.30 kg/m², with a total SOC stock of 1226.64 Tg. The SOC sequestration potential in the study area was estimated at 3057.65 Tg by the categorical maximum method, with a remaining sequestration capacity of 1831.01 Tg. The study area has great potential for SOC sequestration. We hope to transform the theoretical value of SOC sequestration potential into actual SOC sequestration capacity by promoting sustainable agriculture and additional strategies. Our findings provide insights into the global soil conditions, SOC storage capacities, and effective SOC management strategies. Full article

Estimating tropical cyclone (TC) intensity is crucial for disaster reduction and risk management. This study aims to estimate TC intensity using machine learning (ML) models. We utilized eight ML models to predict TC intensity, incorporating factors such as TC location, central pressure, distance to land, landfall in the next six hours, storm speed, storm direction, date, and number from the International Best Track Archive for Climate Stewardship Version 4 (IBTrACS V4). The dataset was divided into four sub-datasets based on the El Niño–Southern Oscillation (ENSO) phases (Neutral, El Niño, and La Niña). Our results highlight that central pressure has the greatest effect on TC intensity estimation, with a maximum root mean square error (RMSE) of 1.289 knots (equivalent to 0.663 m/s). Cubist and Random Forest (RF) models consistently outperformed others, with Cubist showing superior performance in both training and testing datasets. The highest bias was observed in SVM models. Temporal analysis revealed the highest mean error in January and November, and the lowest in February. Errors during the Warm phase of ENSO were notably higher, especially in the South China Sea. Central pressure was identified as the most influential factor for TC intensity estimation, with further exploration of environmental features recommended for model robustness. Full article

Bamboo forests, as some of the integral components of forest ecosystems, have emerged as focal points in forestry research due to their rapid growth and substantial carbon sequestration capacities. In this paper, satellite-borne lidar data from GEDI and ICESat-2/ATLAS are utilized as the main information sources, with Landsat 9 and DEM data as covariates, combined with 51 pieces of ground-measured data. Using random forest regression (RFR), boosted regression tree (BRT), k-nearest neighbor (KNN), Cubist, extreme gradient boosting (XGBoost), and Stacking-ridge regression (RR) machine learning methods, an aboveground carbon (AGC) storage model was constructed at a regional scale. The model evaluation indices were the coefficient of determination (R²), root mean square error (RMSE), and overall estimation accuracy (P). The results showed that (1) The best-fit semivariogram models for c_dem, f_dem, f_ndvi, p_dem, and a_ndvi were Gaussian models, while those for h1_b7, h2_b7, h3_b7, and h4_b7 were spherical models; (2) According to Pearson correlation analysis, the AGC of Dendrocalamus giganteus showed an extremely significant correlation (p < 0.01) with c_dem and p_dem from GEDI, and also showed an extremely significant correlation with a_ndvi, h1_b7, h2_b7, h3_b7, and h4_b7 from ICESat-2/ATLAS; moreover, AGC showed a significant correlation (0.01 < p < 0.05) with f_dem and f_ndvi from GEDI; (3) The estimation accuracy of the GEDI model was superior to that of the ICESat-2/ATLAS model; additionally, the estimation accuracy of the Stacking-RR model, which integrates GEDI and ICESat-2/ATLAS (R² = 0.92, RMSE = 5.73 Mg/ha, p = 86.19%), was better than that of any single model (XGBoost, RFR, BRT, KNN, Cubist); (4) Based on the Stacking-RR model, the estimated AGC of Dendrocalamus giganteus within the study area was 1.02 × 10⁷ Mg. The average AGC was 43.61 Mg/ha, with a maximum value of 76.43 Mg/ha and a minimum value of 15.52 Mg/ha. This achievement can serve as a reference for estimating other bamboo species using GEDI and ICESat-2/ATLAS remote sensing technologies and provide decision support for the scientific operation and management of Dendrocalamus giganteus. Full article

Soil visible and near–infrared reflectance spectroscopy is an effective tool for the rapid estimation of soil organic carbon (SOC). The development of spectroscopic technology has increased the application of spectral libraries for SOC research. However, the direct application of spectral libraries for SOC prediction remains challenging due to the high variability in soil types and soil–forming factors. This study aims to address this challenge by improving SOC prediction accuracy through spectral classification. We utilized the European Land Use and Cover Area frame Survey (LUCAS) large–scale spectral library and employed a geographically weighted principal component analysis (GWPCA) combined with a fuzzy c–means (FCM) clustering algorithm to classify the spectra. Subsequently, we used partial least squares regression (PLSR) and the Cubist model for SOC prediction. Additionally, we classified the soil data by land cover types and compared the classification prediction results with those obtained from spectral classification. The results showed that (1) the GWPCA–FCM–Cubist model yielded the best predictions, with an average accuracy of R² = 0.83 and RPIQ = 2.95, representing improvements of 10.33% and 18.00% in R² and RPIQ, respectively, compared to unclassified full sample modeling. (2) The accuracy of spectral classification modeling based on GWPCA–FCM was significantly superior to that of land cover type classification modeling. Specifically, there was a 7.64% and 14.22% improvement in R² and RPIQ, respectively, under PLSR, and a 13.36% and 29.10% improvement in R² and RPIQ, respectively, under Cubist. (3) Overall, the prediction accuracy of Cubist models was better than that of PLSR models. These findings indicate that the application of GWPCA and FCM clustering in conjunction with the Cubist modeling technique can significantly enhance the prediction accuracy of SOC from large–scale spectral libraries. Full article

Rapid and accurate mapping of soil properties in farmlands is crucial for guiding agricultural production and maintaining food security. Traditional methods using spectral features from remote sensing prove valuable for estimating soil properties, but are restricted to short periods of bare soil occurrence within agricultural settings. Addressing the challenge of predicting soil properties under crop cover, this study proposed an improved soil modeling framework that integrates dynamic crop growth information with machine learning techniques. The methodology’s robustness was tested on six key soil properties in an agricultural region of China, including soil organic carbon (SOC), total nitrogen (TN), total phosphorus (TP), dissolved organic carbon (DOC), dissolved organic nitrogen (DON), and pH. Four experimental scenarios were established to assess the impact of crop growth information, represented by the normalized difference vegetation index (NDVI) and phenological parameters. Specifically, Scenario I utilized only natural factors (terrain and climate data); Scenario II added phenological parameters based on Scenario I; Scenario III incorporated time-series NDVI based on Scenario I; and Scenario IV combined all variables (traditional natural factors and crop growth information). These were evaluated using three advanced machine learning models: random forest (RF), Cubist, and Extreme Gradient Boosting (XGBoost). Results demonstrated that incorporating phenological parameters and time-series NDVI significantly improved model accuracy, enhancing predictions by up to 36% over models using only natural factors. Moreover, although both are crop growth factors, the contribution of the time-series NDVI variable to model accuracy surpassed that of the phenological variable for most soil properties. Relative importance analysis suggested that the crop growth information, derived from time-series NDVI and phenology data, collectively explained 14–45% of the spatial variation in soil properties. This study highlights the significant benefits of integrating remote sensing-based crop growth factors into soil property inversion under crop-covered conditions, providing valuable insights for digital soil mapping. Full article

Soil moisture is the most direct evaluation index for agricultural drought. It is not only directly affected by meteorological conditions such as precipitation and temperature but is also indirectly influenced by environmental factors such as climate zone, surface vegetation type, soil type, elevation, and irrigation conditions. These influencing factors have a complex, nonlinear relationship with soil moisture. It is difficult to accurately describe this non-linear relationship using a single indicator constructed from meteorological data, remote sensing data, and other data. It is also difficult to fully consider environmental factors using a single drought index on a large scale. Machine learning (ML) models provide new technology for nonlinear problems such as soil moisture retrieval. Based on the multi-source drought indexes calculated by meteorological, remote sensing, and land surface model data, and environmental factors, and using the Cubist algorithm based on a classification decision tree (CART), a comprehensive agricultural drought monitoring model at 10 cm, 20 cm, and 50 cm depth in Gansu Province is established. The influence of environmental factors and meteorological factors on the accuracy of the comprehensive model is discussed, and the accuracy of the comprehensive model is evaluated. The results show that the comprehensive model has a significant improvement in accuracy compared to the single variable model, which is a decrease of about 26% and 28% in RMSE and MAPE, respectively, compared to the best MCI model. Environmental factors such as season, DEM, and climate zone, especially the DEM, play a crucial role in improving the accuracy of the integrated model. These three environmental factors can comprehensively reduce the average RMSE of the comprehensive model by about 25%. Compared to environmental factors, meteorological factors have a slightly weaker effect on improving the accuracy of comprehensive models, which is a decrease of about 6.5% in RMSE. The fitting accuracy of the comprehensive model in humid and semi-humid areas, as well as semi-arid and semi-humid areas, is significantly higher than that in arid and semi-arid areas. These research results have important guiding significance for improving the accuracy of agricultural drought monitoring in Gansu Province. Full article

This study proposes a novel approach to estimate canopy density in Picea Schrenkiana var. Tianschanica forest sub-compartments by integrating optical and radar satellite data. This effort is aimed at enhancing methodologies for forest resource surveys and monitoring, particularly vital for the sustainable development of semi-arid mountainous areas with fragile ecological environments. The study area is the West Tianshan Mountain Nature Reserve in Xinjiang, which is characterized by its unique dominant tree species, Picea Schrenkiana. A total of 411 characteristic factors were extracted from Gaofen-2 (GF-2) sub-meter optical satellite imagery, Gaofen-3 (GF-3) multi-polarization synthetic aperture radar satellite imagery, and digital elevation model (DEM) data. Consequently, 17 characteristic parameters were selected based on their correlation with canopy density data to construct an estimation model. Three distinct models were developed, including a multiple stepwise regression model (a linear approach), a Back Propagation (BP) neural network model (a neural network-based method), and a Cubist model (a decision tree-based technique). The results indicate that combining optical and radar image characteristics significantly enhances accuracy, with an Average Absolute Percentage Precision (AAPP) value improvement in estimation accuracy from 76.50% (with optical image) and 78.50% (with radar image) to 78.66% (with both). Of the three models, the BP neural network model achieved the highest overall accuracy (79.19%). At the sub-component scale, the BP neural network model demonstrated superior accuracy in low canopy density estimation (75.37%), whereas the Cubist model, leveraging radar image characteristics, excelled in medium density estimations (87.46%). Notably, the integrated Cubist model combining optical and radar data achieved the highest accuracy for high canopy density estimation (89.17%). This study highlights the effectiveness of integrating optical and radar data for precise canopy density assessment, contributing significantly to ecological resource monitoring methodologies and environmental assessments. Full article

Winter wheat is an important grain that plays a crucial role in agricultural production and ensuring food security. Its yield directly impacts the stability and security of the global food supply. The accurate monitoring of grain yield is imperative for precise agricultural management. This study aimed to enhance winter wheat yield predictions with UAV remote sensing and investigate its predictive capability across diverse environments. In this study, RGB and multispectral (MS) data were collected on 6 May 2020 and 10 May 2022 during the grain filling stage of winter wheat. Using the Pearson correlation coefficient method, we identified 34 MS features strongly correlated with yield. Additionally, we identified 24 texture features constructed from three bands of RGB images and a plant height feature, making a total of 59 features. We used seven machine learning algorithms (Cubist, Gaussian process (GP), Gradient Boosting Machine (GBM), Generalized Linear Model (GLM), K-Nearest Neighbors algorithm (KNN), Support Vector Machine (SVM), Random Forest (RF)) and applied recursive feature elimination (RFE) to nine feature types. These included single-sensor features, fused sensor features, single-year data, and fused year data. This process yielded diverse feature combinations, leading to the creation of seven distinct yield prediction models. These individual machine learning models were then amalgamated to formulate a Bayesian Model Averaging (BMA) model. The findings revealed that the Cubist model, based on the 2020 and 2022 dataset, achieved the highest R² at 0.715. Notably, models incorporating both RGB and MS features outperformed those relying solely on either RGB or MS features. The BMA model surpassed individual machine learning models, exhibiting the highest accuracy (R² = 0.725, RMSE = 0.814 t·ha⁻¹, MSE = 0.663 t·ha⁻¹). Additionally, models were developed using one year’s data for training and another year’s data for validation. Cubist and GLM stood out among the seven individual models, delivering strong predictive performance. The BMA model, combining these models, achieved the highest R² of 0.673. This highlights the BMA model’s ability to generalize for multi-year data prediction. Full article

Peas are one of the most important cultivated legumes worldwide, for which early yield estimations are helpful for agricultural planning. The unmanned aerial vehicles (UAVs) have become widely used for crop yield estimations, owing to their operational convenience. In this study, three types of sensor data (red green blue [RGB], multispectral [MS], and a fusion of RGB and MS) across five growth stages were applied to estimate pea yield using ensemble learning (EL) and four base learners (Cubist, elastic net [EN], K nearest neighbor [KNN], and random forest [RF]). The results showed the following: (1) the use of fusion data effectively improved the estimation accuracy in all five growth stages compared to the estimations obtained using a single sensor; (2) the mid filling growth stage provided the highest estimation accuracy, with coefficients of determination (R²) reaching up to 0.81, 0.8, 0.58, and 0.77 for the Cubist, EN, KNN, and RF algorithms, respectively; (3) the EL algorithm achieved the best performance in estimating pea yield than base learners; and (4) the different models were satisfactory and applicable for both investigated pea types. These results indicated that the combination of dual-sensor data (RGB + MS) from UAVs and appropriate algorithms can be used to obtain sufficiently accurate pea yield estimations, which could provide valuable insights for agricultural remote sensing research. Full article

Helical piles are advantageous alternatives in constructions subjected to high tractions in their foundations, like transmission towers. Installation torque is a key parameter to define installation equipment and the final depth of the helical pile. This work applies machine learning (ML) techniques to predict helical pile installation torque based on information from 707 installation reports, including Standard Penetration Test (SPT) data. It uses this information to build three datasets to train and test eight machine-learning techniques. Decision tree (DT) was the worst technique for comparing performances, and cubist (CUB) was the best. Pile length was the most important variable, while soil type had little relevance for predictions. Predictions become more accurate for torque values greater than 8 kNm. Results show that CUB predictions are within $\left[0.71,1.59\right]$ times the real value with a 95% confidence. Thus, CUB successfully predicted the pile length using SPT data in a case study. One can conclude that the proposed methodology has the potential to aid in the helical pile design and the equipment specification for installation. Full article

Pablo Picasso’s Landscape with Billboards (1912) evinces a deep and complex relationship with emergent trademark and related intellectual property law in France. Among the three trademarked logos featured prominently in the work is that for Bouillon Kub. Critics, caricaturists, and the Cubists themselves toyed with the visual and textual rhymes between Cubism and Bouillon Kub. But only Picasso in his Landscape with Billboards engaged deeply with the nascent trademark and design protection laws exploited more forcefully by Bouillon Kub than nearly any other brand. This essay is a small part of a larger chapter on Picasso, Cubism, and the semiotics of trademark, which, in turn, is a part of the book project Art™: A History of Modern Art, Authenticity, and Trademarks. Full article