Coping with the Impact of Climate Change: A Dive into Precision Agriculture in the United States

With the continued increase in the number of people that are food insecure globally, which could be increasing because of the ongoing Ukraine-Russia war, leading to reduction in international agribusinesses, coupled with drastic climate change exacerbating the problem of food insecurity, there is a constant need to come up with innovative approaches to solve this global issue. In this article, we articulated how precision agriculture can be a tool for ensuring food security in the United States. This study aims to reiterate the significance of precision agriculture in solving global food insecurity.

Computer Vision and Deep Learning-enabled Weed Detection Model for Precision Agriculture

Presently,precision agriculture processes like plant disease,crop yield prediction,species recognition,weed detection,and irrigation can be accom-plished by the use of computer vision(CV)approaches.Weed plays a vital role in influencing crop productivity.The wastage and pollution of farmland's natural atmosphere instigated by full coverage chemical herbicide spraying are increased.Since the proper identification of weeds from crops helps to reduce the usage of herbicide and improve productivity,this study presents a novel computer vision and deep learning based weed detection and classification(CVDL-WDC)model for precision agriculture.The proposed CVDL-WDC technique intends to prop-erly discriminate the plants as well as weeds.The proposed CVDL-WDC technique involves two processes namely multiscale Faster RCNN based object detection and optimal extreme learning machine(ELM)based weed classification.The parameters of the ELM model are optimally adjusted by the use of farmland fertility optimization(FFO)algorithm.A comprehensive simulation analysis of the CVDL-WDC technique against benchmark dataset reported the enhanced out-comes over its recent approaches interms of several measures.

Deep Learning-Based Model for Detection of Brinjal Weed in the Era of Precision Agriculture

The overgrowth of weeds growing along with the primary crop in the fields reduces crop production.Conventional solutions like hand weeding are labor-intensive,costly,and time-consuming;farmers have used herbicides.The application of herbicide is effective but causes environmental and health concerns.Hence,Precision Agriculture(PA)suggests the variable spraying of herbicides so that herbicide chemicals do not affect the primary plants.Motivated by the gap above,we proposed a Deep Learning(DL)based model for detecting Eggplant(Brinjal)weed in this paper.The key objective of this study is to detect plant and non-plant(weed)parts from crop images.With the help of object detection,the precise location of weeds from images can be achieved.The dataset is collected manually from a private farm in Gandhinagar,Gujarat,India.The combined approach of classification and object detection is applied in the proposed model.The Convolutional Neural Network(CNN)model is used to classify weed and non-weed images;further DL models are applied for object detection.We have compared DL models based on accuracy,memory usage,and Intersection over Union(IoU).ResNet-18,YOLOv3,CenterNet,and Faster RCNN are used in the proposed work.CenterNet outperforms all other models in terms of accuracy,i.e.,88%.Compared to other models,YOLOv3 is the least memory-intensive,utilizing 4.78 GB to evaluate the data.

Clustered Wireless Sensor Network in Precision Agriculture via Graph Theory

Food security and sustainable development is making a mandatory move in the entire human race.The attainment of this goal requires man to strive for a highly advanced state in thefield of agriculture so that he can produce crops with a minimum amount of water and fertilizer.Even though our agricultural methodol-ogies have undergone a series of metamorphoses in the process of a present smart-agricultural system,a long way is ahead to attain a system that is precise and accurate for the optimum yield and profitability.Towards such a futuristic method of cultivation,this paper proposes a novel method for monitoring the efficientflow of a small quantity of water through the conventional irrigation system in cultiva-tion using Clustered Wireless Sensor Networks(CWSN).The performance measure is simulated the creation of edge-fixed geodetic clusters using Mat lab’s Cup-carbon tool in order to evaluate the suggested irrigation process model’s performance.Thefindings of blocks 1 and 2 are assessed.Each signal takes just a little amount of energy to communicate,according to the performance.It is feasible to save energy while maintaining uninterrupted communication between nodes and cluster chiefs.However,the need for proper placement of a dynamic control station in WSN still exists for maintaining connectivity and for improving the lifetime fault tolerance of WSN.Based on the minimum edgefixed geodetic sets of the connected graph,this paper offers an innovative method for optimizing the placement of control stations.The edge-fixed geodetic cluster makes the network fast,efficient and reliable.Moreover,it also solves routing and congestion problems.

Harris Hawks Optimizer with Graph Convolutional Network Based Weed Detection in Precision Agriculture

Precision agriculture includes the optimum and adequate use of resources depending on several variables that govern crop yield.Precision agriculture offers a novel solution utilizing a systematic technique for current agricultural problems like balancing production and environmental concerns.Weed control has become one of the significant problems in the agricultural sector.In traditional weed control,the entire field is treated uniformly by spraying the soil,a single herbicide dose,weed,and crops in the same way.For more precise farming,robots could accomplish targeted weed treatment if they could specifically find the location of the dispensable plant and identify the weed type.This may lessen by large margin utilization of agrochemicals on agricultural fields and favour sustainable agriculture.This study presents a Harris Hawks Optimizer with Graph Convolutional Network based Weed Detection(HHOGCN-WD)technique for Precision Agriculture.The HHOGCN-WD technique mainly focuses on identifying and classifying weeds for precision agriculture.For image pre-processing,the HHOGCN-WD model utilizes a bilateral normal filter(BNF)for noise removal.In addition,coupled convolutional neural network(CCNet)model is utilized to derive a set of feature vectors.To detect and classify weed,the GCN model is utilized with the HHO algorithm as a hyperparameter optimizer to improve the detection performance.The experimental results of the HHOGCN-WD technique are investigated under the benchmark dataset.The results indicate the promising performance of the presented HHOGCN-WD model over other recent approaches,with increased accuracy of 99.13%.

A Framework for Introducing Precision Agriculture Technologies in Egypt

Precision Agriculture(PA)has been used in many countries and serving the agricultural sectors.The use of PA solutions intervened with many agricultural businesses and supported decision making using data analytics.Precision Agriculture depends on weather,soil,plants,and water information that are essential for farming.Precision Agriculture depends on the use of several technologies such as image sensors,vision machines,drones,robots,machine learning,and artificial intelligence.The use of Precision Agriculture Technologies(PAT)depends on integration between devices,sensors,and systems to ensure the proper implementation of activities.This paper is generated from research on the applicability of PA in in Egypt that ended with a proposed framework for proper implementation of it.The conducted research depended on a survey,focus group discussions,and an online questionnaire that reached 271 respondents from 19 Egyptian governorates.The framework has been developed to enhance the role of an initiative leader to promote PAT through collaboration with other stakeholders in the agricultural sector.The proposed framework can be used by governmental,non-governmental entities,universities and private sector institutions and could be used at countries facing issues with land fragmentation,limited access to information,limited access to agricultural extension services,and increase in agricultural input’s prices.

Journal of Integrative Agriculture(JIA)Instruction to Authors

Description Journal of Integrative Agriculture(JIA),formerly Agricultural Sciences in China(ASC),founded in 2002,is sponsored by Chinese Academy of Agricultural Sciences(CAAS),co-sponsored by Chinese Association of Agricultural Science Societies(CAASS).

Journal of Integrative Agriculture Instruction to Authors

Description Journal of Integrative Agriculture(JIA),formerly Agricultural Sciences in China(ASC),founded in 2002,is sponsored by Chinese Academy of Agricultural Sciences(CAAS),co-sponsored by Chinese Association of Agricultural Science Societies(CAASS).JIA is a peer-reviewed and multi-disciplinary international journal and published monthly in English.JIA Editorial Board consists of 289 well-respected scholars of agricultural scientific fields.

Nano/Micro-Structural Supramolecular Biopolymers: Innovative Networks with the Boundless Potential in Sustainable Agriculture

Sustainable agriculture plays a crucial role in meeting the growing global demand for food while minimizing adverse environmental impacts from the overuse of synthetic pesticides and conventional fertilizers.In this context,renewable biopolymers being more sustainable offer a viable solution to improve agricultural sustainability and production.Nano/micro-structural supramolecular biopolymers are among these innovative biopolymers that are much sought after for their unique features.These biomaterials have complex hierarchical structures,great stability,adjustable mechanical strength,stimuli-responsiveness,and self-healing attributes.Functional molecules may be added to their flexible structure,for enabling novel agricultural uses.This overview scrutinizes how nano/micro-structural supramolecular biopolymers may radically alter farming practices and solve lingering problems in agricultural sector namely improve agricultural production,soil health,and resource efficiency.Controlled bioactive ingredient released from biopolymers allows the tailored administration of agrochemicals,bioactive agents,and biostimulators as they enhance nutrient absorption,moisture retention,and root growth.Nano/micro-structural supramolecular biopolymers may protect crops by appending antimicrobials and biosensing entities while their eco-friendliness supports sustainable agriculture.Despite their potential,further studies are warranted to understand and optimize their usage in agricultural domain.This effort seeks to bridge the knowledge gap by investigating their applications,challenges,and future prospects in the agricultural sector.Through experimental investigations and theoretical modeling,this overview aims to provide valuable insights into the practical implementation and optimization of supramolecular biopolymers in sustainable agriculture,ultimately contributing to the development of innovative and eco-friendly solutions to enhance agricultural productivity while minimizing environmental impact.

Push forward LC-MS-based therapeutic drug monitoring and pharmacometabolomics for anti-tuberculosis precision dosing and comprehensive clinical management

The spread of tuberculosis(TB),especially multidrug-resistant TB and extensively drug-resistant TB,has strongly motivated the research and development of new anti-TB drugs.New strategies to facilitate drug combinations,including pharmacokinetics-guided dose optimization and toxicology studies of first-and second-line anti-TB drugs have also been introduced and recommended.Liquid chromatography-mass spectrometry(LC-MS)has arguably become the gold standard in the analysis of both endo-and exo-genous compounds.This technique has been applied successfully not only for therapeutic drug monitoring(TDM)but also for pharmacometabolomics analysis.TDM improves the effectiveness of treatment,reduces adverse drug reactions,and the likelihood of drug resistance development in TB patients by determining dosage regimens that produce concentrations within the therapeutic target window.Based on TDM,the dose would be optimized individually to achieve favorable outcomes.Pharmacometabolomics is essential in generating and validating hypotheses regarding the metabolism of anti-TB drugs,aiding in the discovery of potential biomarkers for TB diagnostics,treatment monitoring,and outcome evaluation.This article highlighted the current progresses in TDM of anti-TB drugs based on LC-MS bioassay in the last two decades.Besides,we discussed the advantages and disadvantages of this technique in practical use.The pressing need for non-invasive sampling approaches and stability studies of anti-TB drugs was highlighted.Lastly,we provided perspectives on the prospects of combining LC-MS-based TDM and pharmacometabolomics with other advanced strategies(pharmacometrics,drug and vaccine developments,machine learning/artificial intelligence,among others)to encapsulate in an all-inclusive approach to improve treatment outcomes of TB patients.

Stability Analysis and Performance Evaluation of Additive Mixed-Precision Runge-Kutta Methods

Additive Runge-Kutta methods designed for preserving highly accurate solutions in mixed-precision computation were previously proposed and analyzed.These specially designed methods use reduced precision for the implicit computations and full precision for the explicit computations.In this work,we analyze the stability properties of these methods and their sensitivity to the low-precision rounding errors,and demonstrate their performance in terms of accuracy and efficiency.We develop codes in FORTRAN and Julia to solve nonlinear systems of ODEs and PDEs using the mixed-precision additive Runge-Kutta(MP-ARK)methods.The convergence,accuracy,and runtime of these methods are explored.We show that for a given level of accuracy,suitably chosen MP-ARK methods may provide significant reductions in runtime.

Developing risk models and subtypes of autophagy-associated LncRNAs for enhanced prognostic prediction and precision in therapeutic approaches for liver cancer patients

Background:Limited research has been conducted on the influence of autophagy-associated long non-coding RNAs(ARLncRNAs)on the prognosis of hepatocellular carcinoma(HCC).Methods:We analyzed 371 HCC samples from TCGA,identifying expression networks of ARLncRNAs using autophagy-related genes.Screening for prognostically relevant ARLncRNAs involved univariate Cox regression,Lasso regression,and multivariate Cox regression.A Nomogram was further employed to assess the reliability of Riskscore,calculated from the signatures of screened ARLncRNAs,in predicting outcomes.Additionally,we compared drug sensitivities in patient groups with differing risk levels and investigated potential biological pathways through enrichment analysis,using consensus clustering to identify subgroups related to ARLncRNAs.Results:The screening process identified 27 ARLncRNAs,with 13 being associated with HCC prognosis.Consequently,a set of signatures comprising 8 ARLncRNAs was successfully constructed as independent prognostic factors for HCC.Patients in the high-risk group showed very poor prognoses in most clinical categories.The Riskscore was closely related to immune cell scores,such as macrophages,and the DEGs between different groups were implicated in metabolism,cell cycle,and mitotic processes.Notably,high-risk group patients demonstrated a significantly lower IC50 for Paclitaxel,suggesting that Paclitaxel could be an ideal treatment for those at elevated risk for HCC.We further identified C2 as the Paclitaxel subtype,where patients exhibited higher Riskscores,reduced survival rates,and more severe clinical progression.Conclusion:The 8 signatures based on ARLncRNAs present novel targets for prognostic prediction in HCC.The drug candidate Paclitaxel may effectively treat HCC by impacting ARLncRNAs expression.With the identification of ARLncRNAsrelated isoforms,these results provide valuable insights for clinical exploration of autophagy mechanisms in HCC pathogenesis and offer potential avenues for precision medicine.

Bronchoalveolar lavage fluid assessment facilitates precision medicine for lung cancer

Lung cancer is the most common and fatal malignant disease worldwide and has the highest mortality rate among tumor-related causes of death.Early diagnosis and precision medicine can significantly improve the survival rate and prognosis of lung cancer patients.At present,the clinical diagnosis of lung cancer is challenging due to a lack of effective non-invasive detection methods and biomarkers,and treatment is primarily hindered by drug resistance and high tumor heterogeneity.Liquid biopsy is a method for detecting circulating biomarkers in the blood and other body fluids containing genetic information from primary tumor tissues.Bronchoalveolar lavage fluid(BALF)is a potential liquid biopsy medium that is rich in a variety of bioactive substances and cell components.BALF contains information on the key characteristics of tumors,including the tumor subtype,gene mutation type,and tumor environment,thus BALF may be used as a diagnostic supplement to lung biopsy.In this review,the current research on BALF in the diagnosis,treatment,and prognosis of lung cancer is summarized.The advantages and disadvantages of different components of BALF,including cells,cell-free DNA,extracellular vesicles,and micro RNA are introduced.In particular,the great potential of extracellular vesicles in precision diagnosis and detection of drug-resistant for lung cancer is highlighted.In addition,the performance of liquid biopsies with different body fluid sources in lung cancer detection are compared to facilitate more selective studies involving BALF,thereby promoting the application of BALF for precision medicine in lung cancer patients in the future.

Hepatitis B virus genotypes in precision medicine of hepatitis Brelated hepatocellular carcinoma:Where we are now

Hepatitis B virus(HBV)infection is a major player in chronic hepatitis B that may lead to the development of hepatocellular carcinoma(HCC).HBV genetics are diverse where it is classified into at least 9 genotypes(A to I)and 1 putative genotype(J),each with specific geographical distribution and possible different clinical outcomes in the patient.This diversity may be associated with the precision medicine for HBV-related HCC and the success of therapeutical approaches against HCC,related to different pathogenicity of the virus and host response.This Editorial discusses recent updates on whether the classification of HBV genetic diversity is still valid in terms of viral oncogenicity to the HCC and its precision medicine,in addition to the recent advances in cellular and molecular biology technologies.

A new method for evaluating the firing precision of multiple launch rocket system based on Bayesian theory

How to effectively evaluate the firing precision of weapon equipment at low cost is one of the core contents of improving the test level of weapon system.A new method to evaluate the firing precision of the MLRS considering the credibility of simulation system based on Bayesian theory is proposed in this paper.First of all,a comprehensive index system for the credibility of the simulation system of the firing precision of the MLRS is constructed combined with the group analytic hierarchy process.A modified method for determining the comprehensive weight of the index is established to improve the rationality of the index weight coefficients.The Bayesian posterior estimation formula of firing precision considering prior information is derived in the form of mixed prior distribution,and the rationality of prior information used in estimation model is discussed quantitatively.With the simulation tests,the different evaluation methods are compared to validate the effectiveness of the proposed method.Finally,the experimental results show that the effectiveness of estimation method for firing precision is improved by more than 25%.

Planetary health risks in urban agriculture

Urban agriculture is gaining recognition for its potential contributions to environmental resilience and climate change adaptation,providing advantages such as urban greening,reduced heat island effects,and decreased air pollution.Moreover,it indirectly supports communities during weather events and natural disasters,ensuring food security and fostering community cohesion.However,concerns about planetary health risks persist in highly urbanized and climate-affected areas.Employing electronic databases such as Web of Science and PubMed and adhering to Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines,we identified 55 relevant papers to comprehend the planetary health risks associated with urban agriculture,The literature review identified five distinct health risks related to urban agriculture:(1)trace metal risks in urban farms;(2)health risks associated with wastewater irrigation;(3)zoonotic risks;(4)other health risks;and(5)social and economic risks.The study highlights that urban agriculture,while emphasizing environmental benefits,particularly raises concerns about trace metal bioaccumulation in soil and vegetables,posing health risks for populations.Other well studied risks included wastewater irrigation and backyard livestock farming.The main limitations in the available literature were in studying infectious diseases and antibiotic resistance associated with urban agriculture.

Improving the spaceborne GNSS-R altimetric precision based on the novel multilayer feedforward neural network weighted joint prediction model

Global navigation satellite system-reflection(GNSS-R)sea surface altimetry based on satellite constellation platforms has become a new research direction and inevitable trend,which can meet the altimetric precision at the global scale required for underwater navigation.At present,there are still research gaps for GNSS-R altimetry under this mode,and its altimetric capability cannot be specifically assessed.Therefore,GNSS-R satellite constellations that meet the global altimetry needs to be designed.Meanwhile,the matching precision prediction model needs to be established to quantitatively predict the GNSS-R constellation altimetric capability.Firstly,the GNSS-R constellations altimetric precision under different configuration parameters is calculated,and the mechanism of the influence of orbital altitude,orbital inclination,number of satellites and simulation period on the precision is analyzed,and a new multilayer feedforward neural network weighted joint prediction model is established.Secondly,the fit of the prediction model is verified and the performance capability of the model is tested by calculating the R2 value of the model as 0.9972 and the root mean square error(RMSE)as 0.0022,which indicates that the prediction capability of the model is excellent.Finally,using the novel multilayer feedforward neural network weighted joint prediction model,and considering the research results and realistic costs,it is proposed that when the constellation is set to an orbital altitude of 500 km,orbital inclination of 75and the number of satellites is 6,the altimetry precision can reach 0.0732 m within one year simulation period,which can meet the requirements of underwater navigation precision,and thus can provide a reference basis for subsequent research on spaceborne GNSS-R sea surface altimetry.

Fertilization and Soil Ploughing Practices under Changing Physical Environment Lead to Soil Organic Carbon Dynamics under Conservation Agriculture in Rice-Wheat Cropping System: A Scoping Review

Ploughing and fertilization practices in rice-wheat system have deteriorated the soil carbon (C) pools. Conservation agriculture (CA) based management approaches have proven to enhance C sequestration and reverse the loss of soil-organic-carbon (SOC), which further enhances soil fertility. Different fractions of SOC pools react to the alterations in management practices and indicate changes in SOC dynamics as compared to total C in the soil. Higher SOC levels in soil have been observed in case of reduced/no-till (NT) practices than conventional tillage (CT). However, between CT and zero tillage/NT, total SOC stocks diminished with an increase in soil depth, which demonstrated that the benefits of SOC are more pronounced in the topsoil under NT. Soil aggregation provides physical protection to C associated with different-sized particles, thus, the improvement in soil aggregation through CA is an effective way to mitigate soil C loss. Along with less soil disturbance, residual management, suitable crop rotation, rational application of manures and fertilizers, and integrated nutrient management have been found to be effective in not only improving soil C stock but also enhancing the soil health and productivity. Thus, CA can be considered as a potential method in the build-up of SOC of soil in rice-wheat system.

Genotype-based precision nutrition strategies for the prediction and clinical management of type 2 diabetes mellitus

Globally,type 2 diabetes mellitus(T2DM)is one of the most common metabolic disorders.T2DM physiopathology is influenced by complex interrelationships between genetic,metabolic and lifestyle factors(including diet),which differ between populations and geographic regions.In fact,excessive consumptions of high fat/high sugar foods generally increase the risk of developing T2DM,whereas habitual intakes of plant-based healthy diets usually exert a protective effect.Moreover,genomic studies have allowed the characterization of sequence DNA variants across the human genome,some of which may affect gene expression and protein functions relevant for glucose homeostasis.This comprehensive literature review covers the impact of gene-diet interactions on T2DM susceptibility and disease progression,some of which have demonstrated a value as biomarkers of personal responses to certain nutritional interventions.Also,novel genotype-based dietary strategies have been developed for improving T2DM control in comparison to general lifestyle recommendations.Furthermore,progresses in other omics areas(epigenomics,metagenomics,proteomics,and metabolomics)are improving current understanding of genetic insights in T2DM clinical outcomes.Although more investigation is still needed,the analysis of the genetic make-up may help to decipher new paradigms in the pathophysiology of T2DM as well as offer further opportunities to personalize the screening,prevention,diagnosis,management,and prognosis of T2DM through precision nutrition.

Integrating disulfidptosis-related long noncoding RNAs in colorectal cancer prognosis:A path to precision medicine

This commentary explores the burgeoning field of disulfidptosis-related long noncoding RNAs(lncRNAs)in the prognosis and therapeutic targeting of colorectal cancer(CRC).By evaluating recent research,including the pivotal study"Predicting colorectal cancer prognosis based on long noncoding RNAs of disulfidptosis genes"by Wang et al,this analysis underscores the critical role of lncRNAs in deciphering the molecular complexities of CRC.Highlighting the innovative methodologies and significant findings,I discuss the implications for patient survival,therapeutic response,and the potential of lncRNAs as biomarkers for precision medicine.The integration of bioinformatics,clinical databases,and molecular biology in these studies offers a promising avenue for advancing CRC treatment strategies and improving patient outcomes.