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In cultivation, early harvest offers farmers an opportunity to increase production while decreasing the chances of lower crop production rates, ensuring that the economy remains balanced. The significant reason is to predict the disease in plants and distinguish the type of syndrome with the help of segmentation and random forest optimization classification. In this investigation, the accurate prior phase of crop imagery has been collected from different datasets like cropscience, yesmodes and nelsonwisc . In the current study, the real-time earlier state of crop images has been gathered from numerous data sources similar to crop_science, yes_modes, nelson_wisc dataset.

In this research work, random forest machine learning-based persuasive plants healthcare computing is provided. If proper ecological care is not applied to early harvesting, it can cause diseases in plants, decrease the cropping rate and less production. Until now different methods have been developed for crop analysis at an earlier stage, but it is necessary to implement methods to advanced techniques. So, the detection of plant diseases with the help of threshold segmentation and random forest classification has been involved in this investigation. This implemented design is verified on Python 3.7.8 software for simulation analysis.

In this work, different methods are developed for crops at an earlier stage, but more methods are needed to implement methods with prior stage crop harvesting. Because of this, a disease-finding system has been implemented. The methodologies like “Threshold segmentation” and RFO classifier lends 97.8% identification precision with 99.3% real optimistic rate, and 59.823 peak signal-to-noise (PSNR), 0.99894 structure similarity index (SSIM), 0.00812 machine squared error (MSE) values are attained.

The implemented machine learning design is outperformance methodology, and they are proving good application detection rate.

Nowadays, food problems are likely to arise because of the increasing global population and decreasing arable land. Therefore, it is necessary to increase the yield of agricultural products. Pesticides can be used to improve agricultural land products. This study aims to make the spraying of cherry trees more effective and efficient with the designed artificial intelligence (AI)-based agricultural unmanned aerial vehicle (UAV).

Two approaches have been adopted for the AI-based detection of cherry trees: In approach 1, YOLOv5, YOLOv7 and YOLOv8 models are trained with 70, 100 and 150 epochs. In Approach 2, a new method is proposed to improve the performance metrics obtained in Approach 1. Gaussian, wavelet transform (WT) and Histogram Equalization (HE) preprocessing techniques were applied to the generated data set in Approach 2. The best-performing models in Approach 1 and Approach 2 were used in the real-time test application with the developed agricultural UAV.

In Approach 1, the best F1 score was 98% in 100 epochs with the YOLOv5s model. In Approach 2, the best F1 score and mAP values were obtained as 98.6% and 98.9% in 150 epochs, with the YOLOv5m model with an improvement of 0.6% in the F1 score. In real-time tests, the AI-based spraying drone system detected and sprayed cherry trees with an accuracy of 66% in Approach 1 and 77% in Approach 2. It was revealed that the use of pesticides could be reduced by 53% and the energy consumption of the spraying system by 47%.

An original data set was created by designing an agricultural drone to detect and spray cherry trees using AI. YOLOv5, YOLOv7 and YOLOv8 models were used to detect and classify cherry trees. The results of the performance metrics of the models are compared. In Approach 2, a method including HE, Gaussian and WT is proposed, and the performance metrics are improved. The effect of the proposed method in a real-time experimental application is thoroughly analyzed.

Nanosensors have a wide range of applications because of their high sensitivity, selectivity and specificity. In the past decade, extensive and pervasive research related to nanosensors has led to significant progress in diverse fields, such as biomedicine, environmental monitoring and industrial process control. This led to better and more efficient detection and monitoring of physical and chemical properties at better resolution, opening new horizons in the development of novel technologies and applications for improved human health, environment protection, enhanced industrial processes, etc.

In this paper, the authors discuss the application of citation network analysis in the field of nanosensor research and development. Cluster analysis was carried out using papers published in the field of nanomaterial-based sensor research, and an in-depth analysis was carried out to identify significant clusters. The purpose of this study is to provide researchers to identify a pathway to the emerging areas in the field of nanosensor research. The authors have illustrated the knowledge base, knowledge domain and knowledge progression of nanosensor research using the citation analysis based on 3,636 Science Citation Index papers published during the period 2011 to 2021.

Among these papers, the bibliographic study identified 809 significant research publications, 11 clusters, 556 research sector keywords, 1,296 main authors, 139 referenced authors, 63 nations, 206 organizations and 42 journals. The authors have identified single quantum dot (QD)-based nanosensor for biological applications, carbon dot-based nanosensors, self-powered triboelectric nanogenerator-based nanosensor and genetically encoded nanosensor as the significant research hotspots that came to the fore in recent years. The future trend in nanosensor research might focus on the development of efficient and cost-effective designs for the detection of numerous environmental pollutants and biological molecules using mesostructured materials and QDs. It is also possible to optimize the detection methods using theoretical models, and generalized gradient approximation has great scope in sensor development.

The future trend in nanosensor research might focus on the development of efficient and cost-effective designs for the detection of numerous environmental pollutants and biological molecules using mesostructured materials and QDs. It is also possible to optimize the detection methods using theoretical models, and generalized gradient approximation has great scope in sensor development.

This is a novel bibliometric analysis in the area of “nanomaterial based sensor,” which is carried out in CiteSpace software.

The United Nations (UN) adopted Sustainable Development Goals (SDGs); agenda 2030 focuses on Climate Action (goal 13), targeting climate adaptability, as well as resilience, awareness and improving policy mechanisms on climate change. In order to enhance climate adaptability, climate-smart agricultural practices (CSAP) is a necessary step. CSAP is a sustainable agriculture approach with a strong focus on climate dimensions. The three pillars of climate-smart agriculture (CSA) are ‘Adaptation’: adapting to climate change; ‘Resilience’: building resilience against it and ‘Remove’: reducing carbon emissions. The new world economy uses Industry 4.0 technologies for sustainable advancement, including blockchain technology, big data analytics, artificial intelligence (AI), augmented and virtual reality, industrial Internet of Things (IoT) and services. Hence, technology plays a significant role in climate sustainable agriculture practices. This chapter shall consider three technologies consisting of IoT, AI and blockchain technology which contribute to CSAP in pre-harvesting (monitoring climate as well as fertility status, soil testing, etc.), harvesting (tilling, fertilisation, seed operations, etc.) and post-harvesting (predicting weather factors, seed varieties, etc.) periods of agriculture. All these three technologies work like the human nervous system; IoT helps in converting various information regarding demography, climate change, local agricultural needs, etc. into world data; AI works like a brain in combination with IoT, helps predict the use of climate-smart technology and blockchain, the memory part of the nervous system which deals with supply-side and ensures traceability as well as transparency for consumers as well as farmers. Hence, this chapter shall contribute to the importance of these three technologies in adopting CSAP in three stages of agriculture.

Building information modelling (BIM) is a digital representation of the physical and functional characteristics of a building. Its use offers a range of benefits in terms of achieving the efficient design, construction, operation and maintenance of buildings. Applying BIM at the outset of a new build project should be relatively easy. However, it is often problematic to apply BIM techniques to an existing building, for example, as part of a refurbishment project or as a tool supporting the facilities management strategy, because of inadequacies in the previous management of the dataset that characterises the facility in question. These inadequacies may include information on as built geometry and materials of construction. By the application of automated retrospective data gathering for use in BIM, such problems should be largely overcome and significant benefits in terms of efficiency gains and cost savings should be achieved.

Laser scanning can be used to collect geometrical and spatial information in the form of a 3D point cloud, and this technique is already used. However, as a point cloud representation does not contain any semantic information or geometrical context, such point cloud data must refer to external sources of data, such as building specification and construction materials, to be in used in BIM.

Hyperspectral imaging techniques can be applied to provide both spectral and spatial information of scenes as a set of high-resolution images. Integrating of a 3D point cloud into hyperspectral images would enable accurate identification and classification of surface materials and would also convert the 3D representation to BIM.

This integrated approach has been applied in other areas, for example, in crop management. The transfer of this approach to facilities management and construction would improve the efficiency and automation of the data transition from building pathology to BIM. In this study, the technological feasibility and advantages of the integration of laser scanning and hyperspectral imaging (the latter not having previously been used in the construction context in its own right) is discussed, and an example of the use of a new integration technique is presented, applied for the first time in the context of buildings.

Heritage is the latent part of a sustainable built environment. Conservation and preservation of heritage is one of the United Nations' (UN) sustainable development goals. Many social and natural factors seriously threaten heritage structures by deteriorating and damaging the original. Therefore, regular visual inspection of heritage structures is necessary for their conservation and preservation. Conventional inspection practice relies on manual inspection, which takes more time and human resources. The inspection system seeks an innovative approach that should be cheaper, faster, safer and less prone to human error than manual inspection. Therefore, this study aims to develop an automatic system of visual inspection for the built heritage.

The artificial intelligence-based automatic defect detection system is developed using the faster R-CNN (faster region-based convolutional neural network) model of object detection to build an automatic visual inspection system. From the English and Dutch cemeteries of Surat (India), images of heritage structures were captured by digital camera to prepare the image data set. This image data set was used for training, validation and testing to develop the automatic defect detection model. While validating this model, its optimum detection accuracy is recorded as 91.58% to detect three types of defects: “spalling,” “exposed bricks” and “cracks.”

This study develops the model of automatic web-based visual inspection systems for the heritage structures using the faster R-CNN. Then it demonstrates detection of defects of spalling, exposed bricks and cracks existing in the heritage structures. Comparison of conventional (manual) and developed automatic inspection systems reveals that the developed automatic system requires less time and staff. Therefore, the routine inspection can be faster, cheaper, safer and more accurate than the conventional inspection method.

The study presented here can improve inspecting the built heritages by reducing inspection time and cost, eliminating chances of human errors and accidents and having accurate and consistent information. This study attempts to ensure the sustainability of the built heritage.

For ensuring the sustainability of built heritage, this study presents the artificial intelligence-based methodology for the development of an automatic visual inspection system. The automatic web-based visual inspection system for the built heritage has not been reported in previous studies so far.

Extraction of leaf region from the plant leaf images is a prerequisite process for species recognition, disease detection and classification and so on, which are required for crop management. Several approaches were developed to implement the process of leaf region segmentation from the background. However, most of the methods were applied to the images taken under laboratory setups or plain background, but the application of leaf segmentation methods is vital to be used on real-time cultivation field images that contain complex backgrounds. So far, the efficient method that automatically segments leaf region from the complex background exclusively for black gram plant leaf images has not been developed.

Extracting leaf regions from the complex background is cumbersome, and the proposed PLRSNet (Plant Leaf Region Segmentation Net) is one of the solutions to this problem. In this paper, a customized deep network is designed and applied to extract leaf regions from the images taken from cultivation fields.

The proposed PLRSNet compared with the state-of-the-art methods and the experimental results evident that proposed PLRSNet yields 96.9% of Similarity Index/Dice, 94.2% of Jaccard/IoU, 98.55% of Correct Detection Ratio, Total Segmentation Error of 0.059 and Average Surface Distance of 3.037, representing a significant improvement over existing methods particularly taking into account of cultivation field images.

In this work, a customized deep learning network is designed for segmenting plant leaf region under complex background and named it as a PLRSNet.

The purpose of this study is to evaluate food supply chain stakeholders’ intention to use Industry 5.0 (I5.0) drones for cleaner production in food supply chains.

The authors used a quantitative research design and collected data using an online survey administered to a sample of 264 food supply chain stakeholders in Nigeria. The partial least square structural equation model was conducted to assess the research’s hypothesised relationships.

The authors provide empirical evidence to support the contributions of I5.0 drones for cleaner production. The findings showed that food supply chain stakeholders are more concerned with the use of I5.0 drones in specific operations, such as reducing plant diseases, which invariably enhances cleaner production. However, there is less inclination to drone adoption if the aim was pollution reduction, predicting seasonal output and addressing workers’ health and safety challenges. The findings outline the need for awareness to promote the use of drones for addressing workers’ hazard challenges and knowledge transfer on the potentials of I5.0 in emerging economies.

To the best of the authors’ knowledge, this study is the first to address I5.0 drones’ adoption using a sustainability model. The authors contribute to existing literature by extending the sustainability model to identify the contributions of drone use in promoting cleaner production through addressing specific system operations. This study addresses the gap by augmenting a sustainability model, suggesting that technology adoption for sustainability is motivated by curbing challenges categorised as drivers and mediators.

This study aims to study the distribution characteristics of antibiotic resistance in direct-eating food and analysis of Citrobacter freundii genome and pathogenicity. Residual antibiotics and antibiotic resistance genes (ARGs) in the environment severely threaten human health and the ecological environment. The diseases caused by foodborne pathogenic bacteria are increasing daily, and the enhancement of antibiotic resistance of pathogenic bacteria poses many difficulties in the treatment of disease.

In this study, six fresh fruits and vegetable samples were selected for isolation and identification of culturable bacteria and analysis of antibiotic resistance. The whole genome of Citrobacter freundii isolated from cucumber was sequenced and analyzed by Oxford Nanopore sequencing.

The results show that 270 strains of bacteria were identified in 6 samples. From 12 samples of direct food, 2 kinds of probiotics and 10 kinds of opportunistic pathogens were screened. The proportion of Citrobacter freundii screened from cucumber was significantly higher than that from other samples, and it showed resistance to a variety of antibiotics. Whole genome sequencing showed that Citrobacter freundii was composed of a circular chromosome containing signal peptides, transmembrane proteins and transporters that could induce antibiotic efflux, indicating that Citrobacter freundii had strong adaptability to the environment. The detection of genes encoding carbohydrate active enzymes is more beneficial to the growth and reproduction of Citrobacter freundii in crops. A total of 29 kinds of ARGs were detected in Citrobacter freundii, mainly conferring resistance to fluoroquinolones, aminoglycosides, carbapenem, cephalosporins and macrolides. The main mechanisms are the change in antibiotic targets and efflux pumps, the change in cell permeability and the inactivation of antibiotics and the detection of virulence factors and ARGs, further indicating the serious risk to human health.

The detection of genomic islands and prophages increases the risk of horizontal transfer of virulence factors and ARGs, which spreads the drug resistance of bacteria and pathogenic bacteria more widely.

All those effluent streams having compromised characteristics pose negative effects on the environment either directly or indirectly. Health care facilities and hospitals also generate a large amount of effluent like other industries containing harmful and toxic pharmaceutical residual compounds due to uncontrolled use of drugs, besides others. The occurrence of antibiotic in the environment is of utmost concern due to development of resistant genes. These get mixed up with ground and surface water due to lack of proper treatment of hospital wastewater. The effect of pharmaceutical compounds on human society and ecosystem as a whole is quite obvious. There are no strict laws regarding discharge of hospital effluent in many countries. Contrary to this, the authors do not have appropriate treatment facilities and solution to solve day by day increasing complexity of this problem. Moreover, water discharged from different health facilities having variable concentration often gets mixed with municipal sewage, thus remains partially untreated even after passing from conventional treatment plants. The purpose of this paper is to highlight the occurrences and fate of such harmful compounds, need of proper effluent management system as well as conventionally adopted treatment technologies nowadays all around the globe. This mini-review would introduce the subject, the need of the study, the motivation for the study, aim, objectives of the research and methodology to be adopted for such a study.

Hospital effluents consisting of pathogens, fecal coliforms, Escherichia coli, etc, including phenols, detergents, toxic elements like cyanide and heavy metals such as copper (Cu), iron (Fe), gadolinium (Gd), nickel (Ni), platinum (Pt), among others are commonly detected nowadays. These unwanted compounds along with emerging pollutants are generally not being regulated before getting discharged caused and spread of diseases. Various chemical and biological characteristics of hospital effluents are assessed keeping in the view the threat posed to ecosystem. Several research studies have been done and few are ongoing to explore the different characteristics and compositions of these effluent streams in comparison so as to suggest the suitable conventional treatment techniques and ways to manage the problem. Several antibiotic groups such as ciprofloxacin, ofloxacin, sulfa pyridine, trimethoprim, metronidazole and their metabolites are reported in higher concentration in hospital effluent. The aquatic system also receives a high concentration of pharmaceutical residues more than 14,000 μg/L from treatment plants also and other surface water or even drinking water in Indian cities. Many rivers in southern parts of India receives treated water have detected high concentration drugs and its metabolites. As far as global constraints that need to be discussed, there are only selected pharmaceuticals compounds generally analyzed, issue regarding management and detection based on method of sampling, frequency of analysis and observation, spatial as well as temporal concentration of these concerned micropollutants, accuracy in detecting these compounds, reliability of results and predictions, prioritization and the method of treatment in use for such type of wastewater stream. The complexity of management and treatment as well need to be addressed with following issues at priority: composition and characterization of effluent, compatible and efficient treatment technology that needs to be adopted and the environment risk posed by them. The problem of drugs and its residues was not seen to be reported in latter part of 20th century, but it might be reported locally in some part of globe. This paper covers some aspect about the disposal and regulatory standard around the world toward hospital effluent discharge, its managements and treatment technologies that are adopted and best suitable nowadays various industries and monitoring the efficiencies of existing treatment systems. This mini-review would introduce the subject, the need, the motivation and objectives of the study and methodology can be adopted for such a study.

The compiled review gives a complete view about the types of antibiotics used in different health care facilities, their residue formation, occurrences in different ecosystems, types of regulations or laws available in different counties related to disposal, different type of treatment technologies, innovative combined treatment schemes and future action needed to tackle such type of effluent after its generation. The thesis also highlights the use of certain innovative materials use for the treatment like nanoparticles. It also discusses about the residues impact on the human health as well as their bioaccumulative nature. If the authors relate the past to the current scenario of pharmaceutical compounds (PhACs) in the environment, the authors will certainly notice that many diseases are nowadays not curable by simple previously prescribed Ab. Many research projects have been done in European countries that have shown the risk of such residues like Pills, Sibell, Poseidon, No pills, Neptune, Knappe, Endetech, etc. In the previous section, it was mentioned that there are no stringent laws for hospital wastewater and in many countries, they are mixed with domestic wastewater. Many difficulties are there with this research due to complex analysis, detection of targeted Ab, affecting waterbodies rate of flow, nature of treatment varies with season to season. The way nature is being degraded and harmful effect are being imposed, it is important to take immediate and decisive steps in this area. Wastewater treatment plants (WWTPs) serves as a nursery for antibiotic-resistant systems, hence monitoring with great attention is also needed. Many trials with different treatment process, in combination, were considered. Many countries are paying great attention to this topic by considering the severity of the risk involved in it.

Previous studies by several scientists show that the pharmaceutical residues in the discharged effluent displayed direct toxic effects, and sometimes, detrimental effects in the mixture were also observed. The discharge of untreated effluent from hospitals and pharmaceuticals and personal care products in the natural ecosystem poses a significant threat to human beings. The pharmaceuticals, like antibiotics, in the aquatic environment, accelerate the development of the antibiotic-resistant genes in bacteria, which causes fatal health risks to animals and human beings. Others, like analgesics, are known to affect development in fishes. They also degrade the water quality and may lead to DNA damage, toxicity in lower organisms like daphnia and have the potential to bioaccumulate. A few commonly used nanoadsorbents for water and wastewater treatment along with their specific properties can also be used. The main advantages of them are high adsorption capacity and superior efficiency, their high reusability, synthesis at room temperatures, super magnetism, quantum confinement effect as well as eco-toxicity. This review will focus on the applicability of different nanoscale materials and their uses in treating wastewater polluted by organic and inorganic compounds, heavy metals, bacteria and viruses. Moreover, the use of various nanoadsorbents and nano-based filtration membranes is also examined.

A number of different pharmaceutical residues derived from various activities like production facilities, domestic use and hospitals have been reported earlier to be present in groundwater, effluents and rivers, they include antibiotics, psycho-actives, analgesics, illicit drugs, antihistamine, etc. In past few years environmental scientists are more concerned toward the effluents generated from medical care facilities, community health centers and hospitals. Various chemical and biological characteristics of hospital effluents have been assessed keeping in the view the common threats pose by them to the entire ecosystem. In this study, seven multispecialty hospitals with nonidentical pretreatment were selected for three aspects i.e. conventional wastewater characteristics, high priority pharmaceuticals and microbial analyses. The present work is to evaluate efficacy of advanced wastewater treatment methods with regard to removal of these three aspects from hospital effluents before discharge into a sewage treatment plant (STP). Based on test results, two out of seven treatment technologies, i.e. MBR and CW effectively reducing conventional parameters and pharmaceuticals from secondary and tertiary treatments except regeneration of microbes were observed in tertiary level by these two treatments.

This review has aimed to identify the emerging contaminants, including pharmaceutical residues, highly consumed chemicals that are present in the hospital effluent, along with their physicochemical and biological characteristics. In this, the main objective was to review the occurrences and fate of common drugs and antibiotics present in effluents from hospital wastewaters. As far as global constraints that need to be discussed, there are only selected pharmaceuticals compounds generally analyzed, issue regarding management and detection based on method of sampling, frequency of analysis and observation, spatial as well as temporal concentration of these concerned micropollutants, accuracy in detecting these compounds, reliability of results and predictions, prioritization and the method of treatment in use for such type of wastewater stream are among the major issues (Akter et al., 2012; Ashfaq et al., 2016; García-Mateos et al., 2015; Liu et al., 2014; Mubedi et al., 2013; Prabhasankar et al., 2016; Sun et al., 2016; Suriyanon et al., 2015; Wang et al., 2016; Wen et al., 2004). This paper covers some aspect about the disposal and regulatory standard around the world toward hospital effluent discharge, its managements and treatment technologies that are adopted and best suitable nowadays.

This study many multispecialty hospitals with nonidentical pretreatment were selected for three aspects i.e. conventional wastewater characteristics high priority pharmaceuticals and microbial analyses. The present work is to evaluate efficacy of advanced wastewater treatment methods with regard to removal of these three aspects from hospital effluents before discharge into an STP. Based on test results, two out of different treatment effectively reducing conventional parameters and pharmaceuticals from secondary and tertiary treatments except regeneration of microbes were observed in the tertiary level by these two treatments were studies followed by ozonation and ultraviolet-ray treatment.