Search Results (205)

Sugarcane, a vital economic crop and renewable energy source, requires precise monitoring of the area in which it has been planted to ensure sugar industry security, optimize agricultural resource allocation, and allow the assessment of ecological benefits. Guangxi Zhuang Autonomous Region, leveraging its subtropical climate and abundant solar thermal resources, accounts for over 63% of China’s total sugarcane cultivation area. In this study, we constructed an enhanced RCAU-net model and developed a refined extraction framework that considers different growth stages to enable rapid identification of sugarcane planting areas. This study addresses key challenges in remote-sensing-based sugarcane extraction, namely, the difficulty of distinguishing spectrally similar objects, significant background interference, and insufficient multi-scale feature fusion. To significantly enhance the accuracy and robustness of sugarcane identification, an improved RCAU-net model based on the U-net architecture was designed. The model incorporates three key improvements: it replaces the original encoder with ResNet50 residual modules to enhance discrimination of similar crops; it integrates a Convolutional Block Attention Module (CBAM) to focus on critical features and effectively suppress background interference; and it employs an Atrous Spatial Pyramid Pooling (ASPP) module to bridge the encoder and decoder, thereby optimizing the extraction of multi-scale contextual information. A refined extraction framework that accounts for different growth stages was ultimately constructed to achieve rapid identification of sugarcane planting areas in Guangxi. The experimental results demonstrate that the RCAU-net model performed excellently, achieving an Overall Accuracy (OA) of 97.19%, a Mean Intersection over Union (mIoU) of 94.47%, a Precision of 97.31%, and an F1 Score of 97.16%. These results represent significant improvements of 7.20, 10.02, 6.82, and 7.28 percentage points in OA, mIoU, Precision, and F1 Score, respectively, relative to the original U-net. The model also achieved a Kappa coefficient of 0.9419 and a Recall rate of 96.99%. The incorporation of residual structures significantly reduced the misclassification of similar crops, while the CBAM and ASPP modules minimized holes within large continuous patches and false extractions of small patches, resulting in smoother boundaries for the extracted areas. This work provides reliable data support for the accurate calculation of sugarcane planting area and greatly enhances the decision-making value of remote sensing monitoring in modern agricultural management of sugarcane. Full article

In the context of global agricultural modernization, the early and accurate detection of sugarcane leaf diseases is critical for ensuring stable sugar production. However, existing deep learning models still face significant challenges in complex field environments, such as blurred lesion edges, scale variation, and limited generalization capability. To address these issues, this study constructs an efficient recognition model for sugarcane disease detection, named CaneFocus-Net, specifically designed for precise identification of sugarcane leaf diseases. Based on a single-stage detection architecture, the model introduces a lightweight cross-stage feature fusion module (CP) to optimize feature transfer efficiency. It also designs a module combining a channel-spatial adaptive calibration mechanism with multi-scale pooling aggregation to enhance the backbone network’s ability to extract multi-scale lesion features. Furthermore, by expanding the high-resolution shallow feature layer to enhance sensitivity toward small-sized targets and adopting a phased adaptive nonlinear optimization strategy, detection and localization accuracy along with convergence efficiency have been further improved. Test results on public datasets demonstrate that this method significantly enhances recognition performance for fuzzy lesions and multi-scale targets while maintaining high inference speed. Compared to the baseline model, precision, recall, and mean average precision (mAP50 and mAP50-95) improved by 1.9%, 4.6%, 1.5%, and 1.4%, respectively, demonstrating strong generalization capabilities and practical application potential. This provides reliable technical support for intelligent monitoring of sugarcane diseases in the field. Full article

Heavy metal contamination in water, predominantly from copper (Cu(II)) ions, poses substantial risks to human and environmental health. This study developed a novel, robust adsorbent known as a carboxylate cellulose nanocrystal–silica–graphene oxide hybrid composite porous monolith, which effectively removes Cu(II) from water in a rapid manner. Carboxylate cellulose nanocrystals with enhanced metal-binding properties were synthesized from cellulose extracted from sugarcane bagasse, a significant agricultural byproduct. The porous monolith was synthesized through the combination of carboxylate cellulose nanocrystals, tetraethyl orthosilicate (TEOS), and graphene oxide, utilizing a sol–gel method. The efficacy of the synthesis was confirmed using Fourier-Transform Infra-red (FTIR), X-ray diffraction (XRD), thermogravimetric analysis (TGA), scanning electron microscope (SEM), and Brunauer–Emmett–Teller (BET) analyses. The material exhibited a highly porous mesoporous structure with a surface area of 512 m²/g, signifying a significant enhancement. Batch adsorption experiments under optimal conditions (pH = 5.5, contact time = 240 min, initial concentration = 200 mg/L) demonstrated a high experimental adsorption capacity of 172 mg/g for Cu(II). The adsorption process was best described by the Langmuir isotherm model, which yielded a theoretical maximum capacity (q_m) of 172 mg/g, and the pseudo-second-order kinetic model, confirming monolayer coverage and chemisorption as the rate-limiting step. Thermodynamic analyses demonstrate that the process is both spontaneous and exothermic. The porous monolith demonstrates the capability for multiple uses, maintaining over 70% efficiency after five cycles. The findings indicate that the carboxylate cellulose nanocrystal–silica–graphene oxide hybrid composite porous monolith is an efficient and robust method for the remediation of copper-contaminated water. Full article

This paper presents a data-driven flexible hybrid architecture which explores the use of a Specific Energy Consumption (SEC) index for predicting the Remaining Useful Lifetime (RUL) of spare mechanical parts of an industrial electric machine. The architecture carries out a hybrid process between a physics-based and data-driven deterioration model, and a similarity model based on a recursive database continuously enriched with real data on current used electrical power and the flow of raw material. The architecture enriches the production database with both synthetic and real data through continuous improvement based on the extraction of features from new incoming real data. This recursive process of database construction is carried out to improve the robustness, accuracy, and precision of estimations. The integration of the architecture aims to enhance predictive maintenance. As an example to illustrate the architecture, the case of an industrial shredder machine is analyzed from real data. The proposed architecture successfully predicts the RUL of sugarcane shredder spare parts from the recursive database and a defined threshold condition. The RUL prognosis converges toward a representative trajectory of the database after a given early time with respect to the total useful life. Full article

Detecting surface cracks on sugarcane is a critical step in ensuring product quality control, with detection precision directly impacting raw material screening efficiency and economic benefits in the sugar industry. Traditional methods face three core challenges: (1) complex background interference complicates texture feature extraction; (2) variable crack scales limit models’ cross-scale feature generalization capabilities; and (3) high computational complexity hinders deployment on edge devices. To address these issues, this study proposes a lightweight sugarcane surface crack detection model, SCS-YOLO (Surface Cracks on Sugarcane-YOLO), based on the YOLOv10 architecture. This model incorporates three key technical innovations. First, the designed RFAC2f module (Receptive-Field Attentive CSP Bottleneck with Dual Convolution) significantly enhances feature representation capabilities in complex backgrounds through dynamic receptive field modeling and multi-branch feature processing/fusion mechanisms. Second, the proposed DSA module (Dynamic SimAM Attention) achieves adaptive spatial optimization of cross-layer crack features by integrating dynamic weight allocation strategies with parameter-free spatial attention mechanisms. Finally, the DyHead detection head employs a dynamic feature optimization mechanism to reduce parameter count and computational complexity. Experiments demonstrate that on the Sugarcane Crack Dataset v3.1, compared to the baseline model YOLOv10, our model achieves mAP50:95 to 71.8% (up 2.1%). Simultaneously, it achieves significant reductions in parameter count (down 19.67%) and computational load (down 11.76%), while boosting FPS to 122 to meet real-time detection requirements. Considering the multiple dimensions of precision indicators, complexity indicators, and FPS comprehensively, the SCS—YOLO detection framework proposed in this study provides a feasible technical reference for the intelligent detection of sugarcane quality in the raw materials of the sugar industry. Full article

In this study, the effects of composting phase and bulking agent on macronutrient extraction and the chemical, physicochemical, and biological properties of 20 compost teas from poultry waste composting mixtures were evaluated. Phosphorus (P) extraction was more efficient during stabilization after the thermophilic phase; however, water-soluble P declined as composting progressed. K was more amenable to extraction, with yields ranging from 30% to 70%, followed by N (2% to 12%) and P (1% to 7%). Compost tea quality was clearly affected by both the bulking agent and the composting stage. Bulking agents that accelerate the process, such as cotton waste (CW) and Napier grass (NG), contributed to nutrient mineralization, increasing availability in the compost tea but also raising salt contents responsible for phytotoxicity. In contrast, tree trimmings (TT), sawdust (S), and sugarcane bagasse (SCB) showed better results, striking a balance between nutrient availability and salt content. The period between the thermophilic phase and cooling was the most suitable for extraction, providing the greatest contribution of water-soluble nutrients. This study highlights the influence of bulking agents and composting phases on nutrient extraction and phytotoxicity of compost teas and provides new insights into the role of electrical conductivity as a threshold indicator for safe agricultural application. Full article

Sugarcane wax-derived policosanol (POL) is well recognized for its multifaceted biological activities, particularly in dyslipidemia management, whereas sugar cane extract powder (SEP), prepared from whole sugar juice blended with supplementary components, has not been thoroughly investigated for its biological activities and potential toxicities. Herein, the comparative dietary effect of four distinct SEPs (SEP-1 to SEP-4) and Cuban sugarcane wax extracted POL were examined to prevent the pathological events in high-cholesterol diet (HCD)-induced hyperlipidemic zebrafish. Among the SEPs, a 14-week intake of SEP-2 emerged with the least zebrafish survival probability (0.75, log-rank: χ² = 14.1, p = 0.015), while the POL supplemented group showed the utmost survival probability. A significant change in body weight and morphometric parameters was observed in the SEP-2 supplemented group compared to the HCD group, while non-significant changes had appeared in POL, SEP-1, SEP-3, and SEP-4 supplemented groups. The HCD elevated total cholesterol (TC) and triglyceride (TG) levels were significantly minimized by the supplementation of POL, SEP-1, and SEP-2. However, an augmented HDL-C level was only noticed in POL-supplemented zebrafish. Likewise, only the POL-supplemented group showed a reduction in blood glucose, malondialdehyde (MDA), AST, and ALT levels, and an elevation in sulfhydryl content, paraoxonase (PON), and ferric ion reduction (FRA) activity. Also, plasma from the POL-supplemented group showed the highest antioxidant activity and protected zebrafish embryos from carboxymethyllysine (CML)-induced toxicity and developmental deformities. POL effectively mitigated HCD-triggered hepatic neutrophil infiltration, steatosis, and the production of interleukin (IL)-6 and inhibited cellular senescence in the kidney and minimized the ROS generation and apoptosis in the brain. Additionally, POL substantially elevated spermatozoa count in the testis and safeguarded ovaries from HCD-generated ROS and senescence. The SEP products (SEP-1, SEP-3, and SEP-4) showed almost non-significant protective effect; however, SEP-2 exhibited an additive effect on the adversity posed by HCD in various organs and biochemical parameters. The multivariate examination, employing principal component analysis (PCA) and hierarchical cluster analysis (HCA), demonstrates the positive impact of POL on the HCD-induced pathological events in zebrafish, which are notably diverse, with the effect mediated by SEPs. The comparative study concludes that POL has a functional superiority over SEPs in mitigating adverse events in hyperlipidemic zebrafish. Full article

In this study, the mechanical and structural properties of biocomposites fabricated using transgenic sugarcane bagasse overexpressing sucrose synthesis were investigated. The bagasse fibers were extracted from the transgenic and non-transgenic (NT) sugarcane stalk, then treated with alkalization and carbonization, and their chemical composition was analyzed. The treated fibers were reinforced to produce biocomposites, and their mechanical and structural properties were evaluated by measuring tensile strength, elongation at break, modulus of elasticity and scanning electron microscopy. The cellulose content ranged from 40.6–44.2% in transgenic sugarcane and was higher than in NT sugarcane, with the highest content observed in transgenic SPS3. However, the cellulose and hemicellulose contents were reduced, and the lignin content was significantly increased after carbonization treatment. Alkalization treatment significantly increased the tensile strength, with the highest value of 30.46 MPa obtained at 9% NaOH concentration in a biocomposite fabricated from transgenic SPS3 bagasse fibers. However, carbonization of the SPS3 bagasse fibers lowered tensile strength and slightly increased modulus of elasticity in the biocomposite. Morphological analyses showed roughened fiber surfaces after alkalization and the formation of voids in the carbonized composites. These results indicate the potential of the transgenic sugarcane bagasse fibers with high cellulose content as a renewable reinforcement material for biocomposites. Full article

The sugarcane crop is of great economic relevance to Brazil, and the precise productivity estimation is a major challenge in production. Therefore, the aim of this study was to estimate the productivity of sugarcane cultivars in different regions, using multispectral sensors embedded in RPAs and biometric variables sampled in the field. The study was conducted in two experimental areas, located in the municipalities of Itirapina-SP and Iracemápolis-SP, with 16 cultivars in a randomized block design. The images were acquired using the multispectral sensor MicaSense Altum, allowing the extraction of spectral bands and vegetation indices. In parallel, biometric variables were collected at 149 and 295 days after planting (DAP). The machine learning models Random Forest (RF) and Extreme Gradient Boosting (XGBoost) were calibrated using different sets of variables, and, despite the similar performance, it was decided to use the model derived from XGBoost in the analyses, since it deals more effectively with overfitting. The results indicated a good performance of the model (R² = 0.83 and 0.66; RMSE = 18.7 t ha⁻¹ and 25.3 t ha⁻¹; MAE = 15.7 and 20.2; RPIQ = 3.22 and 2.61) for the validations K-fold and Leave-one-out cross-validation (LOOCV). The correlations between biometric variables, spectral bands, and vegetation indices varied according to crop development stage. The leaf insertion angle presented a strong correlation with near-infrared (NIR) (r = 0.76) and the indices ExG and VARI (r = 0.70 and r = 0.69, respectively). The present work demonstrated that the integration between multispectral and biometric data represents a promising approach for estimating sugarcane productivity. Full article

The contamination of food and beverages with heavy metals, such as Cd, presents significant health risks, underscoring the need for reliable and sensitive analytical methods. This study introduces the development of a rapid, cost-effective, and environmentally friendly method for Cd determination in cachaça, a traditional Brazilian sugarcane spirit. Magnetic nanoparticles (Fe₃O₄) functionalized with tetraethyl orthosilicate are synthesized and employed as adsorbents in a dispersive magnetic solid-phase extraction procedure. The extracted Cd is quantified using flame atomic absorption spectrometry. A full factorial experimental design is used to optimize key parameters, including the sorbent mass, adsorption time, desorption time, and acid concentration. The method demonstrates excellent analytical performance, with a linear calibration range (R² = 0.99), detection limit of 0.0046 mg L⁻¹, and quantification limit of 0.0200 mg L⁻¹. Moreover, validation results show high precision (coefficient of variation < 9.10%) and accuracy (recovery rates between 92.00% and 120.00%). When analyzing commercial cachaça samples, cadmium was detected in all five specimens. Notably, in one sample the cadmium concentration exceeded Brazil’s maximum permissible limit of 0.0200 mg kg⁻¹, underscoring the importance of this work for ensuring food safety. The proposed method offers a sensitive, reproducible, and sustainable approach for analysis of potentially toxic trace metals in alcoholic beverages, reinforcing its potential for routine monitoring and regulatory compliance. Full article

Traditional nutritional diagnosis suffers from inefficiency, high cost, and damage when predicting the nitrogen, phosphorus, and potassium content of sugarcane leaves. Non-destructive nutritional diagnosis of sugarcane leaves based on traditional machine learning and deep learning suffers from poor generalization and lower accuracy. To address these issues, this study proposes a novel convolutional neural network called WT-ResNet. This model incorporates wavelet transform into the residual network structure, enabling effective feature extraction from sugarcane leaf images and facilitating the regression prediction of nitrogen, phosphorus, and potassium content in the leaves. By employing a cascade of decomposition and reconstruction, the wavelet transform extracts multi-scale features, which allows for the capture of different frequency components in images. Through the use of shortcut connections, residual structures facilitate the learning of identity mappings within the model. The results show that by analyzing sugarcane leaf images, our model achieves R² values of 0.9420 for nitrogen content prediction, 0.9084 for phosphorus content prediction, and 0.8235 for potassium content prediction. The accuracy rate for nitrogen prediction reaches 88.24% within a 0.5 tolerance, 58.82% for phosphorus prediction within a 0.1 tolerance, and 70.59% for potassium prediction within a 0.5 tolerance. Compared to other algorithms, WT-ResNet demonstrates higher accuracy. This study aims to provide algorithms for non-destructive sugarcane nutritional diagnosis and technical support for precise sugarcane fertilization. Full article

Early-season sugarcane identification plays a pivotal role in precision agriculture, enabling timely yield forecasting and informed policy-making. Compared to post-season crop identification, early-season identification faces unique challenges, including incomplete temporal observations and spectral ambiguity among crop types in early seasons. Previous studies have not systematically investigated the capability of optical and synthetic aperture radar (SAR) data for early-season sugarcane identification, which may result in suboptimal accuracy and delayed identification timelines. Both the timing for reliable identification (≥90% accuracy) and the earliest achievable timepoint matching post-season level remain undetermined, and which features are effective in the early-season identification is still unknown. To address these questions, this study integrated Sentinel-1 and Sentinel-2 data, extracted 10 spectral indices and 8 SAR features, and employed a random forest classifier for early-season sugarcane identification by means of progressive temporal analysis. It was found that LSWI (Land Surface Water Index) performed best among 18 individual features. Through the feature set accumulation, the seven-dimensional feature set (LSWI, IRECI (Inverted Red-Edge Chlorophyll Index), EVI (Enhanced Vegetation Index), PSSRa (Pigment Specific Simple Ratio a), NDVI (Normalized Difference Vegetation Index), VH backscatter coefficient, and REIP (Red-Edge Inflection Point Index)) achieved the earliest attainment of 90% accuracy by 30 June (early-elongation stage), with peak accuracy (92.80% F1-score) comparable to post-season accuracy reached by 19 August (mid-elongation stage). The early-season sugarcane maps demonstrated high agreement with post-season maps. The 30 June map achieved 88.01% field-level and 90.22% area-level consistency, while the 19 August map reached 91.58% and 93.11%, respectively. The results demonstrate that sugarcane can be reliably identified with accuracy comparable to post-season mapping as early as six months prior to harvest through the integration of optical and SAR data. This study develops a robust approach for early-season sugarcane identification, which could fundamentally enhance precision agriculture operations through timely crop status assessment. Full article

Early and accurate detection of plant diseases is critical for ensuring global food security and agricultural resilience. Ratoon stunting disease (RSD), caused by the bacterium Leifsonia xyli subsp. xyli (Lxx), is among the most economically significant diseases of sugarcane worldwide. Its cryptic nature—characterized by an absence of visible symptoms—renders timely diagnosis particularly difficult, contributing to substantial undetected yield losses across major sugar-producing regions. Here, we report the development of a potential-induced electrochemical (EC) nanobiosensor platform for the rapid, low-cost, and field-deployable detection of Lxx DNA directly from crude sugarcane sap. This method eliminates the need for conventional nucleic acid extraction and thermal cycling by integrating the following: (i) a boiling lysis-based DNA release from xylem sap; (ii) sequence-specific magnetic bead-based purification of Lxx DNA using immobilized capture probes; and (iii) label-free electrochemical detection using a potential-driven DNA adsorption sensing platform. The biosensor shows exceptional analytical performance, achieving a detection limit of 10 cells/µL with a broad dynamic range spanning from 10⁵ to 1 copy/µL (r = 0.99) and high reproducibility (SD < 5%, n = 3). Field validation using genetically diverse sugarcane cultivars from an inoculated trial demonstrated a strong correlation between biosensor signals and known disease resistance ratings. Quantitative results from the EC biosensor also showed a robust correlation with qPCR data (r = 0.84, n = 10, p < 0.001), confirming diagnostic accuracy. This first-in-class EC nanobiosensor for RSD represents a major technological advance over existing methods by offering a cost-effective, equipment-free, and scalable solution suitable for on-site deployment by non-specialist users. Beyond sugarcane, the modular nature of this detection platform opens up opportunities for multiplexed detection of plant pathogens, making it a transformative tool for early disease surveillance, precision agriculture, and biosecurity monitoring. This work lays the foundation for the development of a universal point-of-care platform for managing plant and crop diseases, supporting sustainable agriculture and global food resilience in the face of climate and pathogen threats. Full article

The rapid growth of plant-based biodegradable tableware, driven by plastic restrictions, necessitates rigorous safety assessments of potential chemical contaminants like per- and polyfluoroalkyl substances (PFASs). This study comprehensively evaluated PFAS contamination risks in commercial sugarcane pulp tableware, focusing on the residues of five target PFASs (PFOA, PFOS, PFNA, PFHxA, PFPeA) and their migration behavior under simulated use and takeout conditions. An analysis of 22 samples revealed elevated levels of total fluorine (TF: 33.7–163.6 mg/kg) exceeding the EU limit (50 mg/kg) in 31% of products. While sporadic PFOA residues surpassed the EU single compound limit (0.025 mg/kg) in 9% of samples (16.1–25.5 μg/kg), the levels of extractable organic fluorine (EOF: 4.9–17.4 mg/kg) and the low EOF/TF ratio (3.19–10.4%) indicated inorganic fluorides as the primary TF source. Critically, the migration of all target PFASs into food simulants (water, 4% acetic acid, 50% ethanol, 95% ethanol) under standardized use conditions was minimal (PFOA: 0.52–0.70 μg/kg; PFPeA: 0.54–0.63 μg/kg; others < LOQ). Even under aggressive simulated takeout scenarios (50 °C oscillation for 12 h + 12 h storage at 25 °C), PFOA migration reached only 0.99 ± 0.01 μg/kg in 95% ethanol. All migrated levels were substantially (>15-fold) below typical safety thresholds (e.g., 0.01 mg/kg). These findings demonstrate that, despite concerning residue levels in some products pointing to manufacturing contamination sources, migration during typical and even extended use scenarios poses negligible immediate consumer risk. This study underscores the need for stricter quality control targeting PFOA and inorganic fluoride inputs in sugarcane pulp tableware production. Full article

Sugarcane is mostly planted in rows, and the accurate identification of crop rows is important for the autonomous navigation of agricultural machines. Especially in the elongation period of sugarcane, accurate row identification helps in weed control and the removal of ineffective tillers in the field. However, sugarcane leaves and stalks intertwine and overlap at this stage. They can form a complex occlusion structure, which poses a greater challenge to target detection. To address this challenge, this paper proposes an improved target detection method, SugarRow-YOLO, based on the YOLOv11n model. The method aims to achieve accurate sugarcane identification and provide basic support for subsequent sugarcane row detection. This model introduces the WTConv convolutional modules to expand the sensory field and improve computational efficiency, adopts the iRMB inverted residual block attention mechanism to enhance the modeling capability of crop spatial structure, and uses the UIOU loss function to effectively mitigate the misdetection and omission problem in the region of dense and overlapping targets. The experimental results show that SugarRow-YOLO performs well in the sugarcane target detection task, with a precision of 83%, recall of 87.8%, and mAP₅₀ and mAP_50-95 of 90.2% and 69.2%. In addition to addressing the problem of large variability in row spacing and plant spacing of sugarcane, this paper introduces the DBSCAN clustering algorithm and combines it with a smooth spline curve to fit the crop rows in order to realize the accurate extraction of crop rows. This method achieved 96.6% in the task, with high precision in sugarcane target detection and demonstrates excellent accuracy in sugarcane row fitting, offering robust technical support for the automation and intelligent advancement of agricultural operations. Full article