Machine Learning in Agriculture: A Comprehensive Updated Review

Sensors, Journal Year: 2021, Volume and Issue: 21(11), P. 3758 - 3758

Published: May 28, 2021

The digital transformation of agriculture has evolved various aspects management into artificial intelligent systems for the sake making value from ever-increasing data originated numerous sources. A subset intelligence, namely machine learning, a considerable potential to handle challenges in establishment knowledge-based farming systems. present study aims at shedding light on learning by thoroughly reviewing recent scholarly literature based keywords’ combinations “machine learning” along with “crop management”, “water “soil and “livestock accordance PRISMA guidelines. Only journal papers were considered eligible that published within 2018–2020. results indicated this topic pertains different disciplines favour convergence research international level. Furthermore, crop was observed be centre attention. plethora algorithms used, those belonging Artificial Neural Networks being more efficient. In addition, maize wheat as well cattle sheep most investigated crops animals, respectively. Finally, variety sensors, attached satellites unmanned ground aerial vehicles, have been utilized means getting reliable input analyses. It is anticipated will constitute beneficial guide all stakeholders towards enhancing awareness advantages using contributing systematic topic.

Language: Английский

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A review of the application of machine learning in water quality evaluation

Eco-Environment & Health, Journal Year: 2022, Volume and Issue: 1(2), P. 107 - 116

Published: June 1, 2022

With the rapid increase in volume of data on aquatic environment, machine learning has become an important tool for analysis, classification, and prediction. Unlike traditional models used water-related research, data-driven based can efficiently solve more complex nonlinear problems. In water environment conclusions derived from have been applied to construction, monitoring, simulation, evaluation, optimization various treatment management systems. Additionally, provide solutions pollution control, quality improvement, watershed ecosystem security management. this review, we describe cases which algorithms evaluate different environments, such as surface water, groundwater, drinking sewage, seawater. Furthermore, propose possible future applications approaches environments.

Language: Английский

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Performance of machine learning methods in predicting water quality index based on irregular data set: application on Illizi region (Algerian southeast)

Applied Water Science, Journal Year: 2021, Volume and Issue: 11(12)

Published: Nov. 6, 2021

Abstract Groundwater quality appraisal is one of the most crucial tasks to ensure safe drinking water sources. Concurrently, a index (WQI) requires some parameters. Conventionally, WQI computation consumes time and often found with various errors during subindex calculation. To this end, 8 artificial intelligence algorithms, e.g., multilinear regression (MLR), random forest (RF), M5P tree (M5P), subspace (RSS), additive (AR), neural network (ANN), support vector (SVR), locally weighted linear (LWLR), were employed generate prediction in Illizi region, southeast Algeria. Using best subset regression, 12 different input combinations developed strategy work was based on two scenarios. The first scenario aims reduce consumption computation, where all parameters used as inputs. second intends show variation critical cases when necessary analyses are unavailable, whereas inputs reduced sensitivity analysis. models appraised using several statistical metrics including correlation coefficient (R), mean absolute error (MAE), root square (RMSE), relative (RAE), (RRSE). results reveal that TDS TH key drivers influencing study area. comparison performance evaluation metric shows MLR model has higher accuracy compared other terms 1, 1.4572*10–08, 2.1418*10–08, 1.2573*10–10%, 3.1708*10–08% for R, MAE, RMSE, RAE, RRSE, respectively. executed less rate by RF 0.9984, 1.9942, 3.2488, 4.693, 5.9642 outcomes paper would be interest planners improving sustainable management plans groundwater resources.

Language: Английский

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Application of machine learning in groundwater quality modeling - A comprehensive review

Water Research, Journal Year: 2023, Volume and Issue: 233, P. 119745 - 119745

Published: Feb. 16, 2023

Language: Английский

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Computational Intelligence: An Introduction

Studies in computational intelligence, Journal Year: 2022, Volume and Issue: unknown, P. 411 - 427

Published: Jan. 1, 2022

Language: Английский

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Using Machine Learning Models for Predicting the Water Quality Index in the La Buong River, Vietnam

Water, Journal Year: 2022, Volume and Issue: 14(10), P. 1552 - 1552

Published: May 12, 2022

For effective management of water quantity and quality, it is absolutely essential to estimate the pollution level existing surface water. This case study aims evaluate performance twelve machine learning (ML) models, including five boosting-based algorithms (adaptive boosting, gradient histogram-based light extreme boosting), three decision tree-based (decision tree, extra trees, random forest), four ANN-based (multilayer perceptron, radial basis function, deep feed-forward neural network, convolutional network), in estimating quality La Buong River Vietnam. Water data at monitoring stations alongside for period 2010–2017 were utilized calculate index (WQI). Prediction ML models was evaluated by using two efficiency statistics (i.e., R2 RMSE). The results indicated that all have good predicting WQI but boosting (XGBoost) has best with highest accuracy (R2 = 0.989 RMSE 0.107). findings strengthen argument especially XGBoost, may be employed prediction a high accuracy, which will further improve management.

Language: Английский

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An Integrated Statistical-Machine Learning Approach for Runoff Prediction

Sustainability, Journal Year: 2022, Volume and Issue: 14(13), P. 8209 - 8209

Published: July 5, 2022

Nowadays, great attention has been attributed to the study of runoff and its fluctuation over space time. There is a crucial need for good soil water management system overcome challenges scarcity other natural adverse events like floods landslides, among others. Rainfall–runoff (R-R) modeling an appropriate approach prediction, making it possible take preventive measures avoid damage caused by hazards such as floods. In present study, several data-driven models, namely, multiple linear regression (MLR), adaptive splines (MARS), support vector machine (SVM), random forest (RF), were used rainfall–runoff prediction Gola watershed, located in south-eastern part Uttarakhand. The model analysis was conducted using daily rainfall data 12 years (2009 2020) watershed. first 80% complete train model, remaining 20% testing period. performance models evaluated based on coefficient determination (R2), root mean square error (RMSE), Nash–Sutcliffe efficiency (NSE), percent bias (PBAIS) indices. addition numerical comparison, evaluated. Their performances graphical plotting, i.e., time-series line diagram, scatter plot, violin relative Taylor diagram (TD). comparison results revealed that four heuristic methods gave higher accuracy than MLR model. Among learning RF (RMSE (m3/s), R2, NSE, PBIAS (%) = 6.31, 0.96, 0.94, −0.20 during training period, respectively, 5.53, 0.95, 0.92, respectively) surpassed MARS, SVM, forecasting all cases studied. outperformed models’ periods. It can be summarized best-in-class delivers strong potential

Language: Английский

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Artificial intelligence and digital twins in sustainable agriculture and forestry: a survey

TURKISH JOURNAL OF AGRICULTURE AND FORESTRY, Journal Year: 2022, Volume and Issue: 46(5), P. 642 - 661

Published: Jan. 1, 2022

Affected by global economic pressure and epidemics, sustainable agriculture has received widespread attention from farmers agricultural engineers. Throughout history, technology closely followed the pace of scientific technological development footsteps mechanization, automation, intelligence to progress continuously. At this stage, artificial (AI) is dominating field advancing agriculture. However, large amount data required AI high cost have ensued, while rapid virtualization made people gradually begin consider application digital twins (DT) in This paper examines twin smart recent years discusses analyzes challenges they face future directions development. We find that great potential for success agriculture, which significance solutions achieve low precision meet growing demand high-yield production around world.

Language: Английский

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Prediction of meteorological drought and standardized precipitation index based on the random forest (RF), random tree (RT), and Gaussian process regression (GPR) models

Environmental Science and Pollution Research, Journal Year: 2023, Volume and Issue: 30(15), P. 43183 - 43202

Published: Jan. 17, 2023

Language: Английский

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Integrating Active and Passive Remote Sensing Data for Mapping Soil Salinity Using Machine Learning and Feature Selection Approaches in Arid Regions

Remote Sensing, Journal Year: 2023, Volume and Issue: 15(7), P. 1751 - 1751

Published: March 24, 2023

The prevention of soil salinization and managing agricultural irrigation depend greatly on accurately estimating salinity. Although the long-standing laboratory method measuring salinity composition is accurate for determining parameters, its use frequently constrained by high expense difficulty long-term in situ measurement. Soil northern Nile Delta Egypt severely affects agriculture sustainability food security Egypt. Understanding spatial distribution a critical factor development management drylands. This research aims to improve prediction using combined data collection consisting Sentinel-1 C radar Sentinel-2 optical acquired simultaneously via integrated sensor variables. modelling approach focuses feature selection strategies regression learning. Feature approaches that include filter, wrapper, embedded methods were used with 47 selected variables depending genetic algorithm scrutinize whether regions spectrum from indices SAR texture choose optimum combinations sub-setting resulting each train learners’ random forest (RF), linear (LR), backpropagation neural network (BPNN), support vector (SVR). Combining BPNN RF learner better predicted (RME 0.000246; = 18). Integrating different remote sensing machine learning provides an opportunity develop robust predict evaluated performances various models, overcame limitations conventional techniques, optimized variable input combinations. can assist farmers soil-salinization-affected areas planting procedures enhancing their lands.

Language: Английский

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