Artificial Intelligence in Agricultural Mapping: A Review

Managing agricultural activity encompasses technology, geographic information, spatial data and geomatic tools as support techniques. In this framework, agricultural mapping is an essential geomatic application due to its importance in managing food systems. This research aims to analyze the state of knowledge of geomatics tools and their applications in agriculture through a systematic review of scientific documents and methodological approaches, highlighting the use of geomatics in agricultural mapping to evaluate trends in agriculture management. The study methodology consists of a scientific base of publications on geomatics and its applications in sustainable agriculture, with a quantitative analysis of production and its approaches. Subsequently, PRISMA establishes a systematic review in search of the subject’s methods, applications and trends. The results show that of the total data analyzed, 60% corresponds to general agricultural mapping for crop/water/soil mapping using satellite images. Twenty percent for land use and coverage, considering the georeferencing that contributes to agricultural territorial planning. Nine percent consider geomatic key for agricultural cadastre (plot management). In addition, 6% corresponds to precision agriculture and 5% to watershed management. The most predominant geomatics tools are: Geographic Information System (GIS), Global Positioning System (GPS), unmanned aerial vehicle (UAV) and remote sensing (RS). Also, among the most used geomatic techniques in agricultural cartography, photogrammetry in crop phenology and multispectral analysis in the optimisation and monitoring of agricultural production stand out. Studies show that the geomatic application promotes sustainability practices such as crop rotation, seeds dispersed and germinated by animals, agricultural irrigation through rivers/basins/streams, family gardens and generation of employment sources. The geomatics use is of great utility/potential for the acquisition and generation of geospatial data accurately, with time and cost savings that contribute to the decision-making of city councils, public cadastral administrations, enterprises, educational institutions and agricultural foundations.

RBF neural network and support vector machine (SVM), two Artificial Intelligent (AI) methods, have been extensively applied on machinery fault diagnosis. Aero-engine, as one kind of rotating machine with complex structure and high rotating speed, has complicated vibration faults. As one kind of AI methods, RBF neural network has the advantages of fast learning, high accuracy and strong self-adapting ability. Support vector machine, another AI method, only needs a small quantity of fault data samples to train the classifier and does not need to extract signal features. In this paper, the applications of two AI methods on aero-engine vibration fault diagnosis are introduced. Firstly, the principles and algorithm of both two methods are presented. Secondly the fundamentals of two-shaft aero-engine vibration fault diagnosis are described and gotten the standard fault samples (training samples) and simulation samples (testing samples). Third, two AI methods are applied to the vibration fault diagnosis and obtained the diagnostic results. Finally, the advantages and disadvantages of the two methods are compared such as the computing speed, accuracy of diagnosis and complexity of algorithm, and given a suggestion of selecting the diagnostic methods.

Blood is a vital product with limited resources, available only from volunteers. For this reason, the blood components to be sent from the blood bank to the transfusion centers (hospitals) should be accurately predicted. There are many variables that affect the demand prediction. In this study, fifteen different qualitative and quantitative variables were determined. Artificial intelligence (AI) methods are used because the prediction has nonlinear, complex and uncertain relationships and thus it is also difficult to mathematically express on relationship in between input and output variables. AI methods have the feature of predicting the information that is not given or that may occur in the future by learning the past data. In the study, AI methods such as Decision Tree (DT), Support Vector Machine (SVM), Artificial Neural Network (ANN) and Deep Learning (DL) were applied to blood bank providing blood supply to public and private hospitals operating in four provinces. The data obtained from the prediction results of AI methods were compared with performance criteria (MAPE, MSE, MAE RMSE and R2) and values of overprediction, underprediction, minimum and maximum deviation. The weekly average over predictions are calculated as 9.69, 5.29, 8.45, and 15.65 and weekly average underpredictions as 17.57, 3.03, 3.94, and 14.69 for DT, SVM, ANN, and DL methods, respectively. SVM method was determined as giving the best prediction values. Therefore, it is envisaged that the blood component demand prediction can be calculated using the SVM method.

BackgroundThe application of artificial intelligence (AI) opens an interesting perspective for predicting injury risk and performance in team sports. A better understanding of the techniques of AI employed and of the sports that are using AI is clearly warranted. The purpose of this study is to identify which AI approaches have been applied to investigate sport performance and injury risk and to find out which AI techniques each sport has been using.MethodsSystematic searches through the PubMed, Scopus, and Web of Science online databases were conducted for articles reporting AI techniques or methods applied to team sports athletes.ResultsFifty-eight studies were included in the review with 11 AI techniques or methods being applied in 12 team sports. Pooled sample consisted of 6456 participants (97% male, 25 ± 8 years old; 3% female, 21 ± 10 years old) with 76% of them being professional athletes. The AI techniques or methods most frequently used were artificial neural networks, decision tree classifier, support vector machine, and Markov process with good performance metrics for all of them. Soccer, basketball, handball, and volleyball were the team sports with more applications of AI.ConclusionsThe results of this review suggest a prevalent application of AI methods in team sports based on the number of published studies. The current state of development in the area proposes a promising future with regard to AI use in team sports. Further evaluation research based on prospective methods is warranted to establish the predictive performance of specific AI techniques and methods.

This paper addresses processing of spectrophotometric array signals based on genetic algorithms (GA) least square support vector machines (LS-SVM) regression to provide a powerful model for machine learning and data mining. The key to complete LS-SVM regression is to choose its optimal parameters. Due to their outstanding ability in solving global optimization problems in complex multidimensional search space, GA are used in this study to obtain the optimal parameter combination of the LS-SVM model. Experimental results showed the GA-LS-SVM method to be successful for simultaneous multicomponent determination even where severe overlap of spectra was present. KeywordsLeast squares support vector machines; Genetic algorithms; Spectrophotometric array signals; Overlapping spectra; Artificial intelligence Nowadays, with the application of photometric diode array detector and computers, rapid scanning commercial spectrophotometers are capable of quickly generating huge data consisting of hundreds and even thousands of absorbance values per spectrum. The array data named fullspectrum contain sufficient information to be able to determine the contents of various compounds. The main drawback of ultraviolet-visible (UV-VIS) is its poor selectivity because in many cases UV-VIS spectra display strong overlaps, especially some less specific and selective chromagenic reagents often give rise to strongly overlapped spectra in many cases. The combination of artificial intelligence methods with the computer-controlled spectrophotometers was proven to be effective in overcoming this difficulty [1-3]. Artificial neural network (ANN) is a form of artificial intelligence that mathematically simulates biological nervous system [4, 5]. However, ANN often has slow convergence, is prone to the existence of many local minima during training, and has a tendency of overfitting. Recently, a promising technology called support vector machines (SVM) has been used for classification and regression problems. SVM pioneered by Vapnik is a kind of machine learning method based on modern statistical learning theory and has notable properties including absence of local minima and high generalization ability [6, 7]. Suykens and his coworkers [8] introduced a modified version of SVM called least square SVM (LS-SVM) , which requires solving a set of linear equations instead of a quadratic programming problem and is much easier and computationally simpler than SVM. SVM and LS-SVM represent relatively recent artificial intelligence method and have found some applications in image analysis, classification and disease diagnosis etc. [9, 10]. It is worth mentioning that the success of LS-SVM model is highly dependent on the optimum choice of two parameters, the relative weight of regression error γ and the kernel width σ of radial basis function (RBF). Genetic algorithms (GA) [11, 12] introduced by John Holland are probabilistic optimization techniques based on natural evolution and genetics and Darwin’s theory of survival of the best. With their efficient and robust global search ability, GA are used to search two optimal parameters for the LS-SVM model simultaneously and automatically. The LS-SVM model then performs the regression task using these optimal parameters.

Application of artificial intelligence to forecast hydrocarbon production from shales

Transmission line fault location has been estimated using various methods such as conventional distance relaying, differential relaying, artificial intelligent (AI) methods etc. Among all the methods AI based methods locates fault more accurately than others. In this work various AI methods used for transmission line fault location has been discussed with their advantages and limitations. Different AI methods that have been discussed are nearest neighbor algorithm, linear regression, logistic regression, artificial neural network, support vector machine, decision tree. With the various advantages of AI methods, it can be used effectively for locating faults in transmission lines.

BackgroundAlzheimer’s disease (AD) and Mild Cognitive Impairment (MCI) pose significant societal and healthcare burden. Artificial intelligence (AI) methods have been widely applied in AD and MCI studies. We conducted a bibliometric analysis of the 100 most cited articles on AI applied to AD and MCI.MethodsWe searched the Web of Science database using keywords related to AD, MCI, and AI (e.g., “deep learning,” “machine learning,” “neural networks”). Citation counts ranked articles, and the top 100 were manually screened. Key parameters such as authors, journals, citation count, countries, institutions, and keywords were automatically extracted. We also manually extracted key information, including publication type, impact factor (IF), Journal Citation Reports (JCR) Category Quartile, AI methods, and clinical data types. Analysis and visualization were conducted using VOSviewer.ResultsAmong the 100 articles, 13 were reviews, 2 were basic research papers, and 85 were clinical studies. Seventy seven articles focused on diagnosis and prediction. MRI data was the most frequently used analysis source. Shen Dinggang, the United States, and the University of North Carolina at Chapel Hill were respectively the individual, country, and institution with the highest publication volume. Neuroimage published the most papers (n = 14), and all the top 10 journals belonged to JCR Q1. Emerging keywords included “ensemble learning,” “transfer learning,” and “structural MRI.” Support Vector Machine (SVM) was the most commonly applied AI method (n = 25), closely followed by convolutional neural network (CNN, n = 24).ConclusionWe analyzed the top 100 cited articles on AI in AD and MCI across authors, journals, countries, institutions, keywords, and AI methods. Diagnosing AD/MCI is the primary research focus, with MRI as the most studied examination. SVM and CNN are the most frequently used AI methods in these studies.

Today, concrete quality prediction can be performed with the help of artificial intelligence (AI) to solve existing problems. However, determining the best AI method for predicting concrete compressive strength remains an open question. Therefore, this research evaluates the most accurate AI modeling for predicting various kinds of concrete mixtures. AI methods used in this study are artificial neural networks (ANN), support vector machines (SVM), classification and regression trees (CART), and linear regression (LR). Furthermore, these four AI methods are run with several parameters and tested with 4 different kinds of concrete dataset. Four error indicators and 1 normalization indicator are used to evaluate AI and determine the best AI method. From the Obtained results, indicate that ANN has the best performance when compared with 3 other AI methods. It can be seen that from ANN produced smaller error values when compared to the other three AI methods.

Concrete is one of the most used materials in buildings today; yet, predicting the accurate concrete compressive strength remains challenging because of the highly complex relationship between its mixture. An accurate method of predicting concrete compressive strength can provide a significant advantage to the construction material industry, particularly within the concrete material industry. Many methods can be used to build the prediction model of concrete compressive strength. However, the traditional methods have so many shortcomings, including expensive experimental costs and the inability to formulate an accurate complex relationship between the components of a concrete mixture with the compressive strength. To overcome this issue, this study applies multiple artificial intelligence (AI) methods to find the most accurate input and output relationships within concrete mixtures. The three types of AI methods that will be used in this study are artificial neural networks (ANN), support vector machine (SVM), and linear regression (LR). This study uses 1030 data samples from concrete compressive strength tests obtained from University of California, Irvine, to demonstrate the use of AI prediction models. The obtained results of the simulation show that these artificial intelligence methods can build predictive models without conducting any expensive experiments in the laboratory with good accuracy.

ObjectiveDevelopment of novel informatics methods focused on improving pregnancy outcomes remains an active area of research. The purpose of this study is to systematically review the ways that artificial intelligence (AI) and machine learning (ML), including deep learning (DL), methodologies can inform patient care during pregnancy and improve outcomes.Materials and methodsWe searched English articles on EMBASE, PubMed and SCOPUS. Search terms included ML, AI, pregnancy and informatics. We included research articles and book chapters, excluding conference papers, editorials and notes.ResultsWe identified 127 distinct studies from our queries that were relevant to our topic and included in the review. We found that supervised learning methods were more popular (n = 69) than unsupervised methods (n = 9). Popular methods included support vector machines (n = 30), artificial neural networks (n = 22), regression analysis (n = 17) and random forests (n = 16). Methods such as DL are beginning to gain traction (n = 13). Common areas within the pregnancy domain where AI and ML methods were used the most include prenatal care (e.g. fetal anomalies, placental functioning) (n = 73); perinatal care, birth and delivery (n = 20); and preterm birth (n = 13). Efforts to translate AI into clinical care include clinical decision support systems (n = 24) and mobile health applications (n = 9).ConclusionsOverall, we found that ML and AI methods are being employed to optimize pregnancy outcomes, including modern DL methods (n = 13). Future research should focus on less-studied pregnancy domain areas, including postnatal and postpartum care (n = 2). Also, more work on clinical adoption of AI methods and the ethical implications of such adoption is needed.

Artificial intelligence methods reliably predict crop evapotranspiration with different combinations of meteorological data for sugar beet in a semiarid area

Focus issue: Artificial intelligence in medical physics.

Diatom indices are used to assess the quality of aquatic plants in sustainable river ecosystems. The traditional assessment of diatom indices involves complicated and lengthy process steps. Today, artificial intelligence-based modelling plays a key role in overcoming this complexity. The aim of this work is to model selected diatom indices Biological Diatom Index (BDI), Trophic Diatom Index (TDI) and General Diatom Index (GDI) based on the physicochemical structure of river ecosystems using artificial intelligence and machine learning methods. The application part of the study used surface water variables from rivers monitored by 5 different stations for 24 months as a data set. Traditional analyses were compared with artificial intelligence and machine learning methods using the MATLAB programme. Different algorithms were considered, including Neural Network/Multilayer Perceptron (MLP), Support Vector Machine (SVM), Linear Regression (LR), Gaussian Process Regression (GPR), Decision Tree and Levenberg-Marquardt (LM) approach. To evaluate the quality of the models, the coefficient of determination (R2), root mean square error squared (RMSE) and mean absolute percentage error (MAPE) were compared. The R2 values of the Levenberg-Marquardt model, which gave the best prediction results for BDI, TDI and GDI, were found to be Validation; 0.7691, Training; 0.9620 Testing; 0.8613, Validation 0.9273, Training; 0.9303, Testing; 0.9199, Validation; 0.9273, Training; 0.9303, Testing; 0.9199, respectively. Levenberg Marquardt efficiently predicted Diatom index results accurately with high precision. Our results show that artificial intelligence and machine learning methods are highly efficient tools for the prediction of diatom indices. A time-efficient and labour-saving application in sustainable ecosystem management was successfully demonstrated.

CORR Synthesis: When Should the Orthopaedic Surgeon Use Artificial Intelligence, Machine Learning, and Deep Learning?