Agricultural Environment Research Articles

Risk factors for antimicrobial usage and diseases in Dutch veal calf farms: a cross-sectional study

Antimicrobial use (AMU) is the main driver of antimicrobial resistance (AMR). In the Netherlands, the veal calf sector was among the largest consumers of antimicrobials in Defined Daily Doses Animal (DDDA) for the year of 2022. As preventive use in Dutch livestock farms is forbidden since 2011, most AMU is due to the herd health status which is affected by the farm environment in which the conditions for diseases to spread are created. The aim of this study was to determine which disease etiologies for group treatments are associated with AMU in rosé starter veal calves, and which modifiable technical risk factors on farm are associated with those diseases and with total AMU. Cross-sectional data were collected from 36 Dutch rosé starter veal calf farms in the Netherlands in 2021 using a digital survey. Linear regression analysis showed that the main indications for AMU were respiratory infections, for which mainly tetracyclines and macrolides were used. Partial least squares regression analysis (PLS) revealed 13 on-farm practices associated with the number of group treatments for respiratory diseases and 19 with total AMU. Overlapping variables in both PLS models were related to regrouping of calves, micro-climate conditions, water access and weaning strategies. Overall, these features focused on improving animal welfare and nutrition during production and enhancing a farm’s internal and external biosecurity. This study identified opportunities for reducing AMU in rosé starter veal calf farms, which thereby could contribute to limiting AMR emergence and spread.

Expert-based assessment of chemical and physical exposures, and organizational factors, in past agricultural jobs

BackgroundLimited data document the spectrum of exposures in the agricultural environment. We describe here the wide range of chemical and physical agents, and organizational factors, encountered in agricultural jobs held in the past in Canada and abroad. MethodsWe used data from a population-based case-control study of prostate cancer including 3,925 male participants residing in Montreal, Canada in 2005-2012. Lifetime occupational histories and detailed job descriptions were collected in-person. Industrial hygienists and an agronomist conducted semi-quantitative evaluations of exposure, including intensity and reliability, to some 300 chemical and physical agents in each job held. Analyses focused on the 156 agricultural jobs ever held in the study population. Clusters of agricultural co-exposures were derived. ResultsAgricultural jobs had taken place in 1946-2012, 53% ending in 1970 or after. Jobs were often (43%) held in Quebec, Canada; 22% in Italy, Portugal or Greece, and 10% in Haiti. Jobs entailed exposure to an average of 10 chemical agents (± 7) and most were characterized by long working hours, high physical activity levels, and did not provoke stress or anxiety. Few involved early morning shifts. Exposure to 78 agents was assigned with probable or definite certainty. The most common definite or probable carcinogens were ultraviolet radiation (92% of jobs), environmental tobacco smoke (39%), diesel engine exhaust (23%), wood dust (20%), lubricating oils and greases (20%) and lead (15%). Pesticide exposure (as a group) occurred in 31% of jobs. Fifty-four percent of jobs entailed exposure to ≥ 2 recognized carcinogens. Exposure clusters varied according to countries and type of agricultural activities (general, animal, crops, horticulture, vineyards, etc.). ConclusionsFindings highlight the heterogeneity of exposure patterns in past agricultural environments based on their setting and activities involved. Studies on health-related effects of farming should account for numerous potential exposures, beyond their typical focus on pesticides.

Computing for Agricultural and Urban Environments

Computing for Agricultural and Urban Environments

Multi-model fusion method for predicting CO2 concentration in greenhouse tomatoes

With the rapid development of greenhouse agriculture, accurate prediction of environmental parameters such as temperature, humidity, and carbon dioxide concentration is crucial for optimal crop growth. Traditional forecasting models struggle with the nonlinear and complex nature of greenhouse data, leading to challenges in model robustness. This study addresses these issues by proposing a multi-model fusion strategy for predicting CO2 concentration in greenhouse tomatoes. The proposed method integrates wavelet denoising (WT), variational mode decomposition (VMD), and long short-term memory networks (LSTM). This innovative nonlinear ensemble model effectively extracts key time series features and removes noise, while an introduced attention mechanism enhances the model’s focus on essential time steps, improving prediction accuracy. Experimental results demonstrate that the multi-model fusion approach significantly outperforms single models in terms of accuracy and stability, achieving mean absolute error (MAE) and root mean square error (RMSE) of 0.0117 and 0.0194, respectively. The proposed method offers significant advantages for CO2 prediction in greenhouse crops, providing a theoretical basis and technical support for optimizing and controlling greenhouse parameters. This contributes to the advancement of smart agriculture by offering an efficient environmental monitoring and prediction tool. Additionally, the study presents new ideas and technical solutions for addressing similar agricultural environment prediction challenges, optimizing greenhouse environment control strategies, and improving crop production efficiency.

Improving On-farm Energy Use Efficiency by Optimizing Machinery Operations and Management: A Review

AbstractThe energy use and emissions from direct fossil fuel combustion on-farms to power farm machinery was critically reviewed. Approximately, 15% of agricultural production costs on-farm are energy-related. A potential solution to more sustainable energy use is a shift toward biofuels from renewable resources. The reduction of greenhouse gas emissions through the substitution of diesel oil with biodiesel depends on the feedstock, the inter-esterification process, the storage period, and ambient conditions. In modern tractors, increased fuel use efficiency (or reduced fuel consumption) has been achieved by power/load matching and the use of variable transmission. Engine management systems that are capable of continuously communicating with the engine and transmission to make appropriate adjustments based on inputs received from the tractor allow for quick and precise responses to changing conditions. As a result, maximum efficiency and productivity can be obtained from the tractor operating similarly to the traditional ‘gear-up and throttle-back’ methods of a proficient operator. The future for autonomous tractors is promising, though not new. Electric-powered tractors are near to commercialization or are already commercially available. Hybrid electric driven tractors present some advantages in terms of increased energy use efficiency and functionalities. Increased efficiency can lead to a reduction in diesel fuel consumption and hence, a concurrent decrease in CO2 emission. Where the local electricity supply has a low-carbon emission factor, this can also result in significant emission reductions. Small light-weight robotic equipment can potentially perform functions currently undertaken by tractor-drawn and other heavy equipment with high-fuel consumption, provided field operating capacity was not compromised. However, the size and weight limitations inherent in current harvesting and transport technology mean that soil compaction will still be a problem with robotic units. The robotic operation of medium-scale equipment within a precision-controlled traffic farming environment should offer more feasible and energy-efficient alternatives.

Research on Path Planning of Agricultural UAV Based on Improved Deep Reinforcement Learning

Traditional manual or semi-mechanized pesticide spraying methods often suffer from issues such as redundant coverage and cumbersome operational steps, which fail to meet current pest and disease control requirements. Therefore, there is an urgent need to develop an efficient pest control technology system. This paper builds upon the Deep Q-Network algorithm by integrating the Bi-directional Long Short-Term Memory structure to propose the BL-DQN algorithm. Based on this, a path planning framework for pest and disease control using agricultural drones is designed. This framework comprises four modules: remote sensing image acquisition via the Google Earth platform, task area segmentation using a deep learning U-Net model, rasterized environmental map creation, and coverage path planning. The goal is to enhance the efficiency and safety of pesticide application by drones in complex agricultural environments. Through simulation experiments, the BL-DQN algorithm achieved a 41.68% improvement in coverage compared with the traditional DQN algorithm. The repeat coverage rate for BL-DQN was 5.56%, which is lower than the 9.78% achieved by the DQN algorithm and the 31.29% of the Depth-First Search (DFS) algorithm. Additionally, the number of steps required by BL-DQN was only 80.1% of that of the DFS algorithm. In terms of target point guidance, the BL-DQN algorithm also outperformed both DQN and DFS, demonstrating superior performance.

An attention-based deep network for plant disease classification

Plant disease classification using machine learning in a real agricultural field environment is a difficult task. Often, an automated plant disease diagnosis method might fail to capture and interpret discriminatory information due to small variations among leaf sub-categories. Yet, modern Convolutional Neural Networks (CNNs) have achieved decent success in discriminating various plant diseases using leave images. A few existing methods have applied additional pre-processing modules or sub-networks to tackle this challenge. Sometimes, the feature maps ignore partial information for holistic description by part-mining. A deep CNN that emphasizes integration of partial descriptiveness of leaf regions is proposed in this work. The efficacious attention mechanism is integrated with high-level feature map of a base CNN for enhancing feature representation. The proposed method focuses on important diseased areas in leaves, and employs an attention weighting scheme for utilizing useful neighborhood information. The proposed Attention-based network for Plant Disease Classification (APDC) method has achieved state-of-the-art performances on four public plant datasets containing visual/thermal images. The best top-1 accuracies attained by the proposed APDC are: PlantPathology 97.74%, PaddyCrop 99.62%, PaddyDoctor 99.65%, and PlantVillage 99.97%. These results justify the suitability of proposed method.

Cucumber Leaf Segmentation Based on Bilayer Convolutional Network

When monitoring crop growth using top-down images of the plant canopies, leaves in agricultural fields appear very dense and significantly overlap each other. Moreover, the image can be affected by external conditions such as background environment and light intensity, impacting the effectiveness of image segmentation. To address the challenge of segmenting dense and overlapping plant leaves under natural lighting conditions, this study employed a Bilayer Convolutional Network (BCNet) method for accurate leaf segmentation across various lighting environments. The major contributions of this study are as follows: (1) Utilized Fully Convolutional Object Detection (FCOS) for plant leaf detection, incorporating ResNet-50 with the Convolutional Block Attention Module (CBAM) and Feature Pyramid Network (FPN) to enhance Region of Interest (RoI) feature extraction from canopy top-view images. (2) Extracted the sub-region of the RoI based on the position of the detection box, using this region as input for the BCNet, ensuring precise segmentation. (3) Utilized instance segmentation of canopy top-view images using BCNet, improving segmentation accuracy. (4) Applied the Varifocal Loss Function to improve the classification loss function in FCOS, leading to better performance metrics. The experimental results on cucumber canopy top-view images captured in glass greenhouse and plastic greenhouse environments show that our method is highly effective. For cucumber leaves at different growth stages and under various lighting conditions, the Precision, Recall and Average Precision (AP) metrics for object recognition are 97%, 94% and 96.57%, respectively. For instance segmentation, the Precision, Recall and Average Precision (AP) metrics are 87%, 83% and 84.71%, respectively. Our algorithm outperforms commonly used deep learning algorithms such as Faster R-CNN, Mask R-CNN, YOLOv4 and PANet, showcasing its superior capability in complex agricultural settings. The results of this study demonstrate the potential of our method for accurate recognition and segmentation of highly overlapping leaves in diverse agricultural environments, significantly contributing to the application of deep learning algorithms in smart agriculture.

Development of Livestock-Associated Methicillin-Resistant Staphylococcus aureus (LA-MRSA) Loads in Pigs and Pig Stables During the Fattening Period

Livestock-associated methicillin-resistant Staphylococcus aureus (LA-MRSA) is widespread in European pig production and poses an occupational hazard to farm workers and their household members. Farm workers are exposed to LA-MRSA through direct contact with pigs and airborne transmission, enabling bacteria to be carried home in the nose or on the skin. Consequently, it is important to consider LA-MRSA in a one-health context, studying human exposure by examining LA-MRSA levels in pigs, air, and dust in the farm environment. In this investigation, LA-MRSA levels were quantified in nasal swab samples from the pigs, air samples, and dust at three different time points in a farm rearing pigs from approx. 30 kg until slaughter. Sampling was repeated across seven batches of pigs, resulting in the analysis of 504 nasal swab samples, alongside air and dust samples. LA-MRSA was cultured and quantified on MRSA2 agar plates. Findings revealed significant batch-to-batch variation and a significant 94.1% decrease in LA-MRSA levels during the rearing period. Despite this decline, all nasal swab samples tested positive, with the highest level reaching 353,000 cfu in a sample. Among the 42 air samples, LA-MRSA levels were low to moderate, with a maximum of 568 and an average of 63 cfu/m3. In the 28 dust samples collected during the second and third sampling periods, LA-MRSA counts were high, reaching up to 37,272 cfu/g, with an average of 17,185 cfu/g. The results suggest that while LA-MRSA levels in pigs decrease with age, reaching low levels before slaughter, the bacterium remains highly abundant in dust, posing an occupational hazard to farm workers.

Pseudo-Labeling and Time-Series Data Analysis Model for Device Status Diagnostics in Smart Agriculture

This study proposes an automated data-labeling model that combines a pseudo-labeling algorithm with waveform segmentation based on Long Short-Term Memory (LSTM) to effectively label time-series data in smart agriculture. This model aims to address the inefficiency of manual labeling for large-scale data generated by agricultural systems, enhancing the performance and scalability of predictive models. Our proposed method leverages key features of time-series data to automatically generate labels for new data, thereby improving model accuracy and streamlining data processing. By automating the labeling process, we reduce dependence on manual labeling, which is often labor-intensive and prone to errors in large datasets. This approach enables the efficient preparation of labeled data for applications such as anomaly detection, pattern recognition, and predictive modeling in smart agriculture. Experimental results demonstrate that the automated labeling model achieves 89% accuracy in agricultural environments and reduces data processing time by 30%. Future research will focus on extending the model’s applicability to diverse agricultural settings, enhancing generalization performance, and improving real-time processing capabilities, thereby advancing intelligent and sustainable smart agriculture systems.

Extended-Spectrum Beta-Lactamase-Producing and Multidrug-Resistant Escherichia coli and Klebsiella spp. from the Human–Animal–Environment Interface on Cattle Farms in Burkina Faso

Extended-spectrum beta-lactamase (ESBL)-producing and multidrug-resistant Enterobacterales pose a major threat to both human and animal health. This study assessed the prevalence of ESBL-producing Escherichia coli (ESBL-Ec) and Klebsiella spp. (ESBL-K) on cattle farms in Ouagadougou, Burkina Faso, using a One Health approach. From May 2021 to September 2022, cattle faeces, farmers’ stools, their drinking water and farm soil samples were collected from semi-intensive and traditional farms. An ESBL-selective medium was used to obtain resistant isolates, which were further characterised using biochemical tests. Antimicrobial susceptibility testing was performed using the Kirby–Bauer disc diffusion method. ESBL-Ec and/or ESBL-K were detected in 188 of 322 samples (58.0%). The prevalence of ESBL-Ec isolates was 42.2% (136/322) and that of ESBL-K isolates was 24.5% (79/322). Notably, 156 of the 188 ESBL isolates (83.0%) exhibited multidrug resistance. The highest resistance rates were observed against tetracycline and cotrimoxazole. Importantly, no isolates showed resistance to meropenem, which was used to test for carbapenem resistance. This study highlights the presence of ESBL-Ec and ESBL-K among the humans, animals and environment of the cattle farms. Good hygiene and biosafety practices are essential to limit the potential spread of multidrug-resistant bacteria between different interfaces on farms.

The Current Situation, Issues, and Improvement Measures of Foreign Direct Investment in China's Agricultural Economy

With the rapid development of economic globalization, the connections between countries have become increasingly close, and foreign direct investment has gradually become an important factor influencing the agricultural economic development of various countries. In order to achieve their own economic development, countries have actively sought to attract foreign investment. With China’s accession to the World Trade Organization and its active participation in global governance, attracting and utilizing foreign investment for domestic agriculture has become a strategic consensus for economic development amid intense global economic competition. This paper analyzes the history and current situation of foreign direct investment in Chinese agriculture, identifies the main issues faced by Chinese agriculture in utilizing foreign direct investment, and proposes corresponding solutions, including improving the foreign direct investment environment in Chinese agriculture and optimizing the structure of foreign direct investment sources.

A Lightweight Cotton Field Weed Detection Model Enhanced with EfficientNet and Attention Mechanisms

Cotton is a crucial crop in the global textile industry, with major production regions including China, India, and the United States. While smart agricultural mechanization technologies, such as automated irrigation and precision pesticide systems, have improved crop management, weeds remain a significant challenge. These weeds not only compete with cotton for nutrients but can also serve as hosts for diseases, affecting both cotton yield and quality. Existing weed detection models perform poorly in the complex environment of cotton fields, where the visual features of weeds and crops are similar and often overlap, resulting in low detection accuracy. Furthermore, real-time deployment on edge devices is difficult. To address these issues, this study proposes an improved lightweight weed detection model, YOLO-WL, based on the YOLOv8 architecture. The model leverages EfficientNet to reconstruct the backbone, reducing model complexity and enhancing detection speed. To compensate for any performance loss due to backbone simplification, CA (cross-attention) is introduced into the backbone, improving feature sensitivity. Finally, AFPN (Adaptive Feature Pyramid Network) and EMA (efficient multi-scale attention) mechanisms are integrated into the neck to further strengthen feature extraction and improve weed detection accuracy. At the same time, the model maintains a lightweight design suitable for deployment on edge devices. Experiments on the CottonWeedDet12 dataset show that the YOLO-WL model achieved an mAP of 92.30%, reduced the detection time per image by 75% to 1.9 ms, and decreased the number of parameters by 30.3%. After TensorRT optimization, the video inference time was reduced from 23.134 ms to 2.443 ms per frame, enabling real-time detection in practical agricultural environments.

Exposure of Enchytraeus crypticus to ciprofloxacin - A multi- and transgenerational study

The increased consumption of antibiotics and their partial metabolism by the human and animal body have led to the frequent identification of such compounds in various environmental matrices. Ciprofloxacin (CIP) is one of the most widely used antibiotics worldwide and it has low mobility and high sorption to soil, leading to the potential to accumulate and persist on organic fertilizers (manure and sewage sludge) and agricultural soils. Therefore, the presence of CIP in the agricultural environment has become an emerging concern as it may negatively affect soil organisms and soil quality, and contribute to the spread of antibiotic-resistant bacteria, thus threatening food security and public health. In this study, we aimed to evaluate the multigenerational and transgenerational effects of CIP on the reproduction of Enchytraeus crypticus to properly assess the long-term impacts of such exposure. Our results for the multigenerational test showed that the reproduction of the organisms was affected from the third to the last (sixth) generation, highlighting the importance of long-term evaluation. There was also evidence of tolerance to reproduction of E. crypticus, as there was no statistical difference between concentrations for all generations, including for among the last three generations themselves. For the transgenerational tests, almost all results showed no statistical difference from the controls, indicating that the effects of CIP may be reversible when the organisms are transferred to clean media. However, since the last generations in the transgenerational tests showed significant differences from the first of the multigenerational tests, the adverse effects may still persist to some extent in the subsequent generations. Thus, when evaluating the effects of CIP on soil media, its persistence and behavior in this matrix must be considered. We strongly recommend that further long-term exposure studies be conducted and considered in regulatory decision-making, especially for persistent compounds.

Microbial Contamination and Antibiotic Resistance in Fresh Produce and Agro-Ecosystems in South Asia—A Systematic Review

Fresh produce prone to microbial contamination is a potential reservoir for antimicrobial-resistant bacteria (ARB) and antimicrobial resistance genes (ARGs), posing challenges to food safety and public health. This systematic review aims to comprehensively assess the prevalence of bacterial pathogens and the incidence of ARB/ARGs in fresh produce and agro-ecosystems across South Asia. Twenty-two relevant studies published between 2012 and 2022 from three major scientific databases and the grey literature were identified. The results revealed a wide occurrence of microbial contamination in various types of fresh produce across South Asia, with a predominance of E. coli (16/22), Salmonella spp. (13/22), Staphylococcus spp. (5/22), and Klebsiella spp. (4/22). The agro-ecosystem serves as a complex interface for microbial interactions; studies have reported the prevalence of E. coli (1/4), Salmonella spp. (1/4) and Listeria monocytogenes (1/4) in farm environment samples. A concerning prevalence of ARB has been reported, with resistance to multiple classes of antibiotics. The presence of ARGs in fresh produce underscores the potential for gene transfer and the emergence of resistant pathogens. To conclude, our review provides insights into the requirements of enhanced surveillance, collaborative efforts, implementation of good agricultural practices, and public awareness for food safety and safeguarding public health in the region.

Global microplastics pollution: a bibliometric analysis and review on research trends and hotspots in agroecosystems.

The prevalence of microplastics (MPs) in agricultural ecosystems poses a notable threat to dynamics of soil ecosystems, crop productivity, and global food security. MPs enter agricultural ecosystems from various sources and have considerable impacts on the physiochemical properties soil, soil organisms and microbial communities, and plants. However, the intensity of these impacts can vary with the size, shape, types, and the concentrations of MPs in the soil. Besides, MPs can enter food chain through consummation of crops grown on MPs polluted soils. In this study, we conducted a bibliometric analysis of 1636 publications on the effects of MPs on agricultural ecosystems from 2012 to May 2024. The results revealed a substantial increase in publications over the years, and China, the USA, Germany, and India have emerged as leading countries in this field of research. Social network analysis identified emerging trends and research hotspots. The latest burst keywords were contaminants, biochar, polyethylene microplastics, biodegradable microplastics, antibiotic resistance genes, and quantification. Furthermore, we have summarized the effects of MPs on various components of agricultural ecosystems. By integrating findings from diverse disciplinary perspectives, this study provides a valuable insight into the current knowledge landscape, identifies research gaps, and proposes future research directions to effectively tackle the intricate challenges associated with MPs pollution in agricultural environments.

Kinetic study, byproducts characterization and photodegradation pathway of profoxydim in a biochar water soil system

The study focused on the photodegradation of profoxydim, a low-toxicity cyclohexanedione herbicide commonly used in rice crops, under simulated sunlight conditions. Profoxydim’s behavior in paddy field conditions is not well understood, and this research aimed to fill that gap, particularly examining the effect of commonly utilized organic amendments such as biochar (BC) on its degradation. Results indicated that profoxydim degrades rapidly, with a half-life of 2.4 ± 0.3 h in paddy water and 1.03 ± 0.1 h in paddy soil. However, when BC was introduced, the degradation slowed significantly, extending the half-lives to 3.1 ± 0.2 h in water and 3.07 ± 0.5 h in soil. The study identified five degradation products (DPs) using TOF mass accuracy measurements and MS/MS spectra fragmentation. Two of these DPs were found to be more stable than profoxydim itself. Additionally, the research proposed a novel photodegradation pathway, highlighting processes such as homolytic C-N bond cleavage, photoisomerization, and photoinduced oxidation. The study’s findings contribute new insights into the environmental fate of profoxydim, offering a deeper understanding of its transformation in rice paddy fields and aiding in the assessment of potential risks associated with its residues in agricultural environments.

Ignoring the food route underestimated human health risk from potentially toxic elements in agricultural environments of Ziyang, Shaanxi, China.

Staple food is a crucial exposure route for the human intake of potentially toxic elements (PTEs), but it has been neglected in previous human health risk (HHR) studies. Lack of attention to this issue will lead to an underestimation of HHR caused by PTEs. This study establishes a comprehensive regional identification method for health risk assessment (HRA), namely, soil-maize health risk assessment (SMHRA) and applies it to Ziyang, Shaanxi, which is a typical agricultural county. SMHRA considered the exposure pathway of staple food and utilized Monte Carlo simulation to enhance the accuracy of HRA for PTEs. Results indicated the PTE spatial heterogeneity in a soil-maize system. Introducing staple food exposure pathway would increase HHR values and probabilities 1.57-2.80 and 1.53-5.63 times than that when food route was not considered. Overall, the HHR caused by a single PTE was low, which relatively safe. The introduction of food pathway contributed to accurate estimate the HHR of As and Ni, and the risk probabilities ranged from 0.04% to 12.46%. Few areas had high levels of Ni, which pose health risks: approximately 1.8% for children and higher than 0.5% for adults. Both As and Ni had the highest contribution to HHR among all PTEs, with 33.84%-41.56% TNCR caused by As, and 54.73%-56.90% TCR created by Ni, respectively. For human health risk routes, the staple food exhibited the highest contribution to HHR among all exposure routes, with TNCR of 36.15%-56.73% and the TCR of 44.96%-64.28%. Our research imply that dietary intake of PETs must be considered in the human health risk assessment in agricultural environment, which offers the foundation for subsequent environmental risk prevention and control.

Chemometric assessment of bioaccumulation and contamination pathways for toxic metals in diet and environment: implications for chronic kidney disease of unknown etiology (CKDu) in Sri Lankan agricultural regions.

Prolonged consumption of foods containing toxic metals can elevate the risk of noncommunicable diseases, including chronic kidney disease of uncertain etiology (CKDu). Despite the increasing number of CKDu cases in Maradankulama and Mahakanadrawa Grama Niladhari Divisions (GN) in Sri Lanka, no prior studies have examined the accumulation of heavy metal(loid)s and their potential association with CKDu prevalence. Furthermore, there is an absence of comprehensive analyses using chemometric techniques such as PCA and hierarchical studies regarding CKDu and heavy metal contamination in Sri Lanka. This study aims to provide initial insights into the accumulation and potential pathways of toxic metals in staple foods within local diets and their subsequent presence in the agricultural environment of examined GNs. Cr, Cd, As, and Ni concentrations in analyzed foods were within permissible limits (MPLs), whereas Pb levels exceeded MPLs in rice (Oryza sativa), gotukola (Centella asiatica), lime (Citrus crenatifolia), and inland fish (Etroplus suratensis). High target hazard quotient (THQt) values in polished rice suggest possible health risks with prolonged intake. Hierarchical analysis suggested a common source of Pb accumulation. PCA and hierarchical clustering revealed the intricate connection between As and Cd, with their concurrent clustering in samples suggesting a potential common origin. This indicates that while individual concentrations comply with acceptable standards, the potential synergistic effects of Cd and As accumulation might pose elevated health risks. Further, the gut tissues of inland fish exhibited pronounced metal concentrations and significant (p < 0.05) positive correlations with toxic metals in the tank sediments suggesting a diet-based bioaccumulation pathway through sediments.

A new strategy based on multi-source remote sensing data for improving the accuracy of land use/cover change classification.

Land Use/Cover Change (LUCC) plays a crucial role in sustainable land management and regional planning. However, contemporary feature extraction approaches often prove inefficient at capturing critical data features, thereby complicating land cover categorization. In this research, we introduce a new feature extraction algorithm alongside a Segmented and Stratified Principal Component Analysis (SS-PCA) dimensionality reduction method based on correlation grouping. These methods are applied to UAV LiDAR and UAV HSI data collected from land use types (e.g., residential areas, agricultural lands) and specific species (e.g., tree species) in urban, agricultural, and natural environments to reflect the diversity of the study area and to demonstrate the ability of our methods to be applied in different classification scenarios. We utilize LiDAR and HSI data to extract 157 features, including intensity, height, Normalized Digital Surface Model (nDSM), spectral, texture, and index features, to identify the optimal feature subset. Subsequently, the best feature subset is inputted into a random forest classifier to classify the features. Our findings demonstrate that the SS-PCA method successfully enhances downscaled feature bands, reduces hyperspectral data noise, and improves classification accuracy (Overall Accuracy = 91.17%). Additionally, the CFW method effectively screens appropriate features, thereby increasing classification accuracy for LiDAR (Overall Accuracy = 78.10%), HIS (Overall Accuracy = 89.87%), and LiDAR + HIS (Overall Accuracy = 97.17%) data across various areas. Moreover, the integration of LiDAR and HSI data holds promise for significantly improving ground fine classification accuracy while mitigating issues such as the 'salt and pepper noise'. Furthermore, among individual features, the LiDAR intensity feature emerges as critical for enhancing classification accuracy, while among single-class features, the HSI feature proves most influential in improving classification accuracy.