Artificial intelligence approach to estimate discharge of drip tape irrigation based on temperature and pressure

Seyedzadeh, Amin; Maroufpoor, Saman; Maroufpoor, Eisa; Shiri, Jalal; Bozorg‐Haddad, Omid; Gavazi, Farnoosh

doi:10.1016/j.agwat.2019.105905

Cited by 46 publications

(22 citation statements)

References 60 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…LSSVM is the modified version of the support vector machine (SVM), but both work on different principles in solving the hyperplane [20]. In the SVM model, quadratic programming is used to optimize the parameters of the hyperplane, while the LSSVM model uses linear programming to solve the problems [21]. Figure 1 illustrates the hierarchical network of the LSSVM model with inputs (Xi) and output (Y) series.…”

Section: Research Methods 21 Least Square Support Vector Machine (Lssvm)mentioning

confidence: 99%

Air temperature prediction using different machine learning models

Adnan

Liang

Kuriqi

et al. 2021

IJEECS

View full text Add to dashboard Cite

Air temperature is an essential climatic component particularly in water resources management and other agro-hydrological/meteorological activities planning This paper examines the prediction capability of three machine learning models, least square support vector machine (LSSVM), group method and data handling neural network (GMDHNN) and classification and regression trees (CART) in air temperature forecasting using monthly temperature data of Astore and Gilgit climatic stations of Pakistan. The prediction capability of three machine learning models is evaluated using different time lags input combinations with help of root mean square error (RMSE), the mean absolute error (MAE) and coefficient of determination (R<sup>2</sup>).statistical indicators. The obtained results indicated that the LSSVM model is more accurate in temperature forecasting than GMDHNN and CART models. LSSVM significantly decreases the mean RMSE of the GMHNN and CART models by 1.47-3.12% and 20.01-25.12% for the Chakdara and Kalam Stations, respectively.

show abstract

Section: Research Methods 21 Least Square Support Vector Machine (Lssvm)mentioning

confidence: 99%

Air temperature prediction using different machine learning models

Adnan

Liang

Kuriqi

et al. 2021

IJEECS

View full text Add to dashboard Cite

show abstract

“…ARX model outperforms other predictive models in terms of MSE and response time. Seyedzadeh et al [117] explored ML algorithms to optimize the uniform emitter discharge rate of drip irrigation system under varying pressure and temperature conditions. In this model operating pressure, water temperature, discharge coefficient, pressure exponent, and nominal discharge were taken as input parameters while ration of emitter discharge to nominal discharge is taken as output temperature.…”

Section: Drip Irrigationmentioning

confidence: 99%

Machine Learning Applications for Precision Agriculture: A Comprehensive Review

et al. 2021

View full text Add to dashboard Cite

Agriculture plays a vital role in the economic growth of any country. With the increase of population, frequent changes in climatic conditions and limited resources, it becomes a challenging task to fulfil the food requirement of the present population. Precision agriculture also known as smart farming have emerged as an innovative tool to address current challenges in agricultural sustainability. The mechanism that drives this cutting edge technology is machine learning (ML). It gives the machine ability to learn without being explicitly programmed. ML together with IoT (Internet of Things) enabled farm machinery are key components of the next agriculture revolution. In this article, authors present a systematic review of ML applications in the field of agriculture. The areas that are focused are prediction of soil parameters such as organic carbon and moisture content, crop yield prediction, disease and weed detection in crops and species detection. ML with computer vision are reviewed for the classification of a different set of crop images in order to monitor the crop quality and yield assessment. This approach can be integrated for enhanced livestock production by predicting fertility patterns, diagnosing eating disorders, cattle behaviour based on ML models using data collected by collar sensors, etc. Intelligent irrigation which includes drip irrigation and intelligent harvesting techniques are also reviewed that reduces human labour to a great extent. This article demonstrates how knowledge-based agriculture can improve the sustainable productivity and quality of the product.

show abstract

“…It is mainly used to reflect the distribution characteristics of original data and can also compare the distribution characteristics of multiple groups of data. The boxplot is displayed based on an error distribution of four values (Maroufpoor et al 2020Seyedzadeh et al 2020 which are the first quartile (Q1), the third quartile (Q3), the interquartile range (IQR), and the portion of the rectangle showing the median. This is shown in Figure 8.…”

Section: Comparison Of the Modelsmentioning

confidence: 99%

Estimation and Comparative Analysis of Daily Reference Evapotranspiration Based on Different Input Combinations and Different Models

Niu

Li²,

Liu³

et al. 2021

Preprint

View full text Add to dashboard Cite

Daily reference evapotranspiration (ET0) is the most crucial link in estimating crop water demand. In this study, Levenberg-Marquardt (L-M), Genetic Algorithm-Back Propagation (GA-BP) and Partial Least Squares Regression (PLSR) models were introduced to calculate the ET0 values, Based on the Pearson Correlation analysis method, five meteorological factors were obtained, which were combined into six different input scenarios. Compared with the values that calculated by the the Penman Monteith (PM) formula. Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Nash-Sutcliffe Efficiency (NSE), and Scatter Index (SI) were used to evaluate the simulation performance of the models. The results showed that the simulation effect of the L-M model is better than that of the GA-BP model and PLSR model in all scenarios. PLSR model has the worst performance. The SI index of L-M6 was 46.69% lower than that of GA-BP6 and 65.78% lower than that of PLSR6. When the input factors are 3, the simulation effect of the input wind speed, the maximum temperature and the minimum temperature is the best. L-M model and GA-BP model can predict the ET0 in the region with a lack of meteorological data. This study provides an important reference for high-precision prediction of ET0 under different input combinations of meteorological factors.

show abstract

Artificial intelligence approach to estimate discharge of drip tape irrigation based on temperature and pressure

Cited by 46 publications

References 60 publications

Air temperature prediction using different machine learning models

Air temperature prediction using different machine learning models

Machine Learning Applications for Precision Agriculture: A Comprehensive Review

Estimation and Comparative Analysis of Daily Reference Evapotranspiration Based on Different Input Combinations and Different Models

Contact Info

Product

Resources

About