1
|
2015 |
( Haghverdi et al., 2015Haghverdi A , Leib BG , Washington-Allen RA , Ayers PD , Buschermohle MJ ( 2015 ) High-resolution prediction of soil available water content within the crop root zone . Journal of Hydrology 530 : 167 – 179 . DOI: https://doi.org/10.1016/j.jhydrol.2015.09.061 . https://doi.org/10.1016/j.jhydrol.2015.0...
) |
USA |
High-resolution prediction of soil available water content within the crop root zone |
Journal of Hydrology |
2
|
2015 |
( Giusti and Marsili-Libelli, 2015Giusti E , Marsili-Libelli S ( 2015 ) A Fuzzy Decision Support System for irrigation and water conservation in agriculture . Environmental Modelling and Software 63 : 73 – 86 . DOI: https://doi.org/10.1016/j.envsoft.2014.09.020 . https://doi.org/10.1016/j.envsoft.2014.0...
) |
Italy |
A Fuzzy Decision Support System for irrigation and water conservation in agriculture |
Environmental Modelling & Software |
3
|
2015 |
( Stone et al., 2015)Stone KC , Bauer PJ , Busscher WJ , Millen JA , Evans DE , Strickland EE ( 2015 ) Variable-rate irrigation management using an expert system in the eastern coastal plain . Irrigation Science 33 ( 3 ): 167 – 175 . DOI: https://doi.org/10.1007/s00271-014-0457-x . https://doi.org/10.1007/s00271-014-0457-...
|
USA |
Variable-rate irrigation management using an expert system in the eastern coastal plain |
Irrigation Science |
4
|
2016 |
( Feng et al., 2016)Feng Y , Cui N , Zhao L , Hu X , Gong D ( 2016 ) Comparison of ELM, GANN, WNN and empirical models for estimating reference evapotranspiration in humid region of Southwest China . Journal of Hydrology 536 : 376 - 383 . DOI: https://doi.org/10.1016/j.jhydrol.2016.02.053 . https://doi.org/10.1016/j.jhydrol.2016.0...
|
China |
Comparison of ELM, GANN, WNN, and empirical models for estimating reference evapotranspiration in the humid region of Southwest China |
Journal of Hydrology |
5
|
2016 |
( King and Shellie, 2016)King BA , Shellie KC ( 2016 ) Evaluation of neural network modeling to predict non-water-stressed leaf temperature in wine grape for calculation of crop water stress index . Agricultural Water Management 167 : 38 – 52 . DOI: https://doi.org/10.1016/j.agwat.2015.12.009 . https://doi.org/10.1016/j.agwat.2015.12....
|
USA |
Evaluation of neural network modeling to predict non-water-stressed leaf temperature in wine grape for calculation of crop water stress index |
Agricultural Water Management |
6
|
2016 |
( Karandish and Šimůnek, 2016)Karandish F , Šimůnek J ( 2016 ) A comparison of numerical and machine-learning modeling of soil water content with limited input data . Journal of Hydrology 543 : 892 – 909 . DOI: https://doi.org/10.1016/j.jhydrol.2016.11.007 . https://doi.org/10.1016/j.jhydrol.2016.1...
|
Iran |
A comparison of numerical and machine-learning modeling of soil water content with limited input data |
Journal of Hydrology |
7
|
2016 |
( Navarro-Hellín et al., 2016)Navarro-Hellín H , Martínez-del-Rincon J , Domingo-Miguel R , Soto-Valles F , Torres-Sánchez R ( 2016 ) A decision support system for managing irrigation in agriculture . Computers and Electronics in Agriculture 124 : 121 – 131 . DOI: https://doi.org/10.1016/j.compag.2016.04.003 . https://doi.org/10.1016/j.compag.2016.04...
|
Spain |
A decision support system for managing irrigation in agriculture |
Computers and Electronics in Agriculture |
8
|
2017 |
( Villarrubia et al., 2017)Villarrubia G , Paz JF , De La Iglesia DH , Bajo J ( 2017 ) Combining multi-agent systems and wireless sensor networks for monitoring crop irrigation . Sensors 17 ( 8 ): 1775 . DOI: https://doi.org/10.3390/s17081775 . https://doi.org/10.3390/s17081775...
|
Spain |
Combining Multi-Agent Systems and Wireless Sensor Networks for Monitoring Crop Irrigation |
Sensors |
9
|
2017 |
( Elnesr and Alazba, 2017)Elnesr MN , Alazba AA ( 2017 ) Simulation of water distribution under surface dripper using artificial neural networks . Computers and Electronics in Agriculture 143 : 90 – 99 . DOI: https://doi.org/10.1016/j.compag.2017.10.003 . https://doi.org/10.1016/j.compag.2017.10...
|
Saudi Arabia |
Simulation of water distribution under surface dripper using artificial neural networks |
Computers and Electronics in Agriculture |
10
|
2017 |
( Kontogiannis et al., 2017)Kontogiannis S , Kokkonis G , Ellinidou S , Valsamidis S ( 2017 ) Proposed Fuzzy-NN Algorithm with LoRaCommunication Protocol for Clustered Irrigation Systems . Future Internet 9 ( 4 ): 78 . DOI: https://doi.org/10.3390/fi9040078 . https://doi.org/10.3390/fi9040078...
|
Greece |
Proposed Fuzzy-NN Algorithm with LoRa Communication Protocol for Clustered Irrigation Systems |
Future Internet |
11
|
2017 |
( Yang et al., 2017)Yang G , Liu L , Guo P , Li M ( 2017 ) A flexible decision support system for irrigation scheduling in an irrigation district in China . Agricultural Water Management 179 : 378 - 389 . DOI: https://doi.org/10.1016/j.agwat.2016.07.019 . https://doi.org/10.1016/j.agwat.2016.07....
|
China |
A flexible decision support system for irrigation scheduling in an irrigation district in China |
Agricultural Water Management |
12
|
2018 |
( Chang and Lin, 2018)Chang CL , Lin KM ( 2018 ) Smart agricultural machine with a computer vision-based weeding and variable-rate irrigation scheme . Robotics 7 ( 3 ): 38 . DOI: https://doi.org/10.3390/robotics7030038 . https://doi.org/10.3390/robotics7030038...
|
Taiwan |
Smart Agricultural Machine with a Computer Vision-Based Weeding and Variable-Rate Irrigation Scheme |
Robotics |
13
|
2018 |
( Al-Ghobari et al., 2018)Al-Ghobari HM , El-Marazky MS , Dewidar AZ , Mattar MA ( 2018 ) Prediction of wind drift and evaporation losses from sprinkler irrigation using neural network and multiple regression techniques . Agricultural Water Management 195 : 211 – 221 . DOI: https://doi.org/10.1016/j.agwat.2017.10.005 . https://doi.org/10.1016/j.agwat.2017.10....
|
Saudi Arabia |
Prediction of wind drift and evaporation losses from sprinkler irrigation using neural network and multiple regression techniques |
Agricultural Water Management |
14
|
2018 |
( Goap et al., 2018)Goap A , Sharma D , Shukla AK , Rama Krishna C ( 2018 ) An IoT based smart irrigation management system using Machine learning and open source technologies . Computers and Electronics in Agriculture 155 : 41 – 49 . DOI: https://doi.org/10.1016/j.compag.2018.09.040 . https://doi.org/10.1016/j.compag.2018.09...
|
India |
An IoT-based smart irrigation management system using Machine learning and open-source technologies |
Computers and Electronics in Agriculture |
15
|
2018 |
( Munir et al., 2018)Munir MS , Bajwa IS , Naeem MA , Ramzan B ( 2018 ) Design and implementation of an IoT system for smart energy consumption and smart irrigation in tunnel farming . Energies 11 ( 12 ): 3427 . DOI: https://doi.org/10.3390/en11123427 . https://doi.org/10.3390/en11123427...
|
Pakistan |
Design and Implementation of an IoT System for Smart Energy Consumption and Smart Irrigation in Tunnel Farming |
Energies |
16
|
2019 |
( Shi et al., 2019)Shi X , Han W , Zhao T , Tang J ( 2019 ) Decision support system for variable rate irrigation based on UAV multispectral remote sensing . Sensors 19 ( 13 ): 2880 . DOI: https://doi.org/10.3390/s19132880 . https://doi.org/10.3390/s19132880...
|
China |
Decision Support System for Variable Rate Irrigation Based on UAV Multispectral Remote Sensing |
Sensors |
17
|
2019 |
( Mouatadid et al., 2019)Mouatadid S , Adamowski JF , Tiwari MK , Quilty JM ( 2019 ) Coupling the maximum overlap discrete wavelet transform and long short-term memory networks for irrigation flow forecasting . Agricultural Water Management 219 : 72 – 85 . DOI: https://doi.org/10.1016/j.agwat.2019.03.045 . https://doi.org/10.1016/j.agwat.2019.03....
|
Canada |
Coupling the maximum overlap discrete wavelet transform and long short-term memory networks for irrigation flow forecasting |
Agricultural Water Management |
18
|
2019 |
( Ferreira et al., 2019)Ferreira LB , Duarte AB , Da Cunha FF , Fernandes Filho EI ( 2019 ) Multivariate adaptive regression splines (MARS) applied to daily reference evapotranspiration modeling with limited weather data . Acta Scientiarum. Agronomy 41 ( 1 ): 39880 . DOI: https://doi.org/10.4025/actasciagron.v41i1.39880 . https://doi.org/10.4025/actasciagron.v41...
|
Brazil |
Multivariate adaptive regression splines (MARS) applied to daily reference evapotranspiration modeling with limited weather data |
Acta Scientiarum |
19
|
2019 |
( Hoseini, 2019)Hoseini Y ( 2019 ) Use fuzzy interface systems to optimize land suitability evaluation for surface and trickle irrigation . Information Processing in Agriculture 6 ( 1 ): 11 – 19 . DOI: https://doi.org/10.1016/j.inpa.2018.09.003 . https://doi.org/10.1016/j.inpa.2018.09.0...
|
Iran |
Use fuzzy interface systems to optimize land suitability evaluation for surface and trickle irrigation |
Information Processing in Agriculture |
20
|
2019 |
( Nadafzadeh and Mehdizadeh, 2019)Nadafzadeh M , Mehdizadeh SA ( 2019 ) Design and fabrication of an intelligent control system for determination of watering time for turfgrass plant using computer vision system and artificial neural network . Precision Agriculture 20 ( 5 ): 857 – 879 . DOI: https://doi.org/10.1007/s11119-018-9618-x . https://doi.org/10.1007/s11119-018-9618-...
|
Iran |
Design and fabrication of an intelligent control system for determination of watering time for turfgrass plant using a computer vision system and artificial neural network |
Precision Agriculture |
21
|
2019 |
( Keswani et al., 2019)Keswani B , Mohapatra AG , Mohanty A , Khanna A , Rodrigues JJPC , Gupta D , De Albuquerque VHC ( 2019 ) Adapting weather conditions based IoT enabled smart irrigation technique in precision agriculture mechanisms . Neural Computing and Applications 31 : 277 – 292 . DOI: https://doi.org/10.1007/s00521-018-3737-1 . https://doi.org/10.1007/s00521-018-3737-...
|
India |
Adapting weather conditions based IoT enabled smart irrigation techniques in precision agriculture mechanisms |
Neural Computing and Applications |
22
|
2019 |
( Kelley and Pardyjak, 2019)Kelley J , Pardyjak E ( 2019 ) Using Neural Networks to Estimate Site-Specific Crop Evapotranspiration with Low-Cost Sensors . Agronomy 9 ( 2 ): 108 . DOI: https://doi.org/10.3390/agronomy9020108 . https://doi.org/10.3390/agronomy9020108...
|
USA |
Using Neural Networks to Estimate Site-Specific Crop Evapotranspiration with Low-Cost Sensors |
Agronomy |
23
|
2019 |
( González Perea et al., 2019)González Perea R , Camacho Poyato E , Montesinos P , Rodríguez Díaz JA ( 2019 ) Prediction of irrigation event occurrence at farm level using optimal decision trees . Computers and Electronics in Agriculture 157 : 173 – 180 . DOI: https://doi.org/10.1016/j.compag.2018.12.043 . https://doi.org/10.1016/j.compag.2018.12...
|
Spain |
Prediction of irrigation event occurrence at farm level using optimal decision trees |
Computers and Electronics in Agriculture |
24
|
2020 |
( Torres-Sanchez et al., 2020)Torres-Sanchez R , Navarro-Hellin H , Guillamon-Frutos A , San-Segundo R , Ruiz-Abellón MC , Domingo-Miguel R ( 2020 ) A decision support system for irrigation management: Analysis and implementation of different learning techniques . Water 12 ( 2 ): 548 . DOI: https://doi.org/10.3390/w12020548 . https://doi.org/10.3390/w12020548...
|
Spain |
A decision support system for irrigation management: Analysis and implementation of different learning techniques |
Water |
25
|
2020 |
( Jamroen et al., 2020)Jamroen C , Komkum P , Fongkerd C , Krongpha W ( 2020 ) An intelligent irrigation scheduling system using low-cost wireless sensor network toward sustainable and precision agriculture . IEEE Access 8 : 172756 – 172769 . DOI: https://doi.org/10.1109/ACCESS.2020.3025590 . https://doi.org/10.1109/ACCESS.2020.3025...
|
Thailand |
An intelligent irrigation scheduling system using a low-cost wireless sensor network toward sustainable and precision agriculture |
IEEE Access |
26
|
2020 |
( Seyedzadeh et al., 2020)Seyedzadeh A , Maroufpoor S , Maroufpoor E , Shiri J , Bozorg-Haddad O , Gavazi F ( 2020 ) Artificial intelligence approach to estimate discharge of drip tape irrigation based on temperature and pressure . Agricultural Water Management 228 : 105905 . DOI: https://doi.org/10.1016/j.agwat.2019.105905 . https://doi.org/10.1016/j.agwat.2019.105...
|
Iran |
Artificial intelligence approach to estimate discharge of drip tape irrigation based on temperature and pressure |
Agricultural Water Management |
27
|
2020 |
( Raza et al., 2020)Raza A , Shoaib M , Faiz MA , Baig F , Khan MM , Ullah MK , Zubair M ( 2020 ) Comparative assessment of reference evapotranspiration estimation using conventional method and machine learning algorithms in four climatic regions . Pure and Applied Geophysics 177 ( 9 ): 4479 – 4508 . DOI: https://doi.org/10.1007/s00024-020-02473-5 . https://doi.org/10.1007/s00024-020-02473...
|
Pakistan |
Comparative Assessment of Reference Evapotranspiration Estimation Using Conventional Method and Machine Learning Algorithms in Four Climatic Regions |
Pure and Applied Geophysics |
28
|
2020 |
( Omidzade et al., 2020)Omidzade F , Ghodousi H , Shahverdi K ( 2020 ) Comparing Fuzzy SARSA learning and ant colony optimization algorithms in water delivery scheduling under water shortage conditions . Journal of Irrigation and Drainage Engineering 146 ( 9 ): 04020028 . DOI: https://doi.org/10.1061/(asce)ir.1943-4774.0001496 . https://doi.org/10.1061/(asce)ir.1943-47...
|
Iran |
Comparing Fuzzy SARSA Learning and Ant Colony Optimization Algorithms in Water Delivery Scheduling under Water Shortage Conditions |
Journal of Irrigation and Drainage Engineering |
29
|
2020 |
( Sidhu et al., 2020aSidhu RK , Kumar R , Rana PS ( 2020a) Long short-term memory neural network-based multi-level model for smart irrigation . Modern Physics Letters B 34 ( 36 ): 2050418 . DOI: https://doi.org/10.1142/S0217984920504187 . https://doi.org/10.1142/S021798492050418...
) |
India |
Long short-term memory neural network-based multi-level model for smart irrigation |
Modern Physics Letters B |
30
|
2020 |
( Sidhu et al., 2020bSidhu RK , Kumar R , Rana PS ( 2020 b) Machine learning based crop water demand forecasting using minimum climatological data . Multimedia Tools and Applications 79 ( 19-20 ): 13109 - 13124 . DOI: https://doi.org/10.1007/s11042-019-08533-w . https://doi.org/10.1007/s11042-019-08533...
) |
India |
Machine learning-based crop water demand forecasting using minimum climatological data |
Multimedia Tools and Applications |
31
|
2020 |
( Torres et al., 2020)Torres ABB , Da Rocha AR , Coelho da Silva TL , De Souza JN , Gondim RS ( 2020 ) Multilevel data fusion for the internet of things in smart agriculture . Computers and Electronics in Agriculture 171 : 105309 . DOI: https://doi.org/10.1016/j.compag.2020.105309 . https://doi.org/10.1016/j.compag.2020.10...
|
Brazil |
Multilevel data fusion for the internet of things in smart agriculture |
Computers and Electronics in Agriculture |
32
|
2020 |
( Wakamori et al., 2020)Wakamori K , Mizuno R , Nakanishi G , Mineno H ( 2020 ) Multimodal neural network with clustering-based drop for estimating plant water stress . Computers and Electronics in Agriculture 168 : 105118 . DOI: https://doi.org/10.1016/j.compag.2019.105118 . https://doi.org/10.1016/j.compag.2019.10...
|
Japan |
Multimodal neural network with clustering-based drop for estimating plant water stress |
Computers and Electronics in Agriculture |
33
|
2020 |
( Shiri et al., 2020)Shiri J , Karimi B , Karimi N , Kazemi MH , Karimi S ( 2020 ) Simulating wetting front dimensions of drip irrigation systems: Multi criteria assessment of soft computing models . Journal of Hydrology 585 : 124792 . DOI: https://doi.org/10.1016/j.jhydrol.2020.124792 . https://doi.org/10.1016/j.jhydrol.2020.1...
|
Iran |
Simulating wetting front dimensions of drip irrigation systems: Multi-criteria assessment of soft computing models |
Journal of Hydrology |
34
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2020 |
( Filgueiras et al., 2020)Filgueiras R , Almeida TS , Mantovani EC , Dias SHB , Fernandes-Filho EI , Da Cunha FF , Venancio LP ( 2020 ) Soil water content and actual evapotranspiration predictions using regression algorithms and remote sensing data . Agricultural Water Management 241 : 106346 . DOI: https://doi.org/10.1016/j.agwat.2020.106346 . https://doi.org/10.1016/j.agwat.2020.106...
|
Brazil |
Soil water content and actual evapotranspiration predictions using regression algorithms and remote sensing data |
Agricultural Water Management |
35
|
2020 |
( Karimi et al., 2020)Karimi S , Shiri J , Marti P ( 2020 ) Supplanting missing climatic inputs in classical and random forest models for estimating reference evapotranspiration in humid coastal areas of Iran . Computers and Electronics in Agriculture 176 : 105633 . DOI: https://doi.org/10.1016/j.compag.2020.105633 . https://doi.org/10.1016/j.compag.2020.10...
|
Iran |
Supplanting missing climatic inputs in classical and random forest models for estimating reference evapotranspiration in humid coastal areas of Iran |
Computers and Electronics in Agriculture |
36
|
2021 |
( Blasi et al., 2021)Blasi AH , Abbadi MA , Al-Huweimel R ( 2021 ) Machine Learning Approach for an Automatic Irrigation System in Southern Jordan Valley . Engineering, Technology & Applied Science Research 11 ( 1 ): 6609 – 6613 . DOI: https://doi.org/10.48084/etasr.3944 . https://doi.org/10.48084/etasr.3944...
|
Jordan |
Machine Learning Approach for an Automatic Irrigation System in Southern Jordan Valley |
Engineering, Technology & Applied Science Research |
37
|
2021 |
( Gu et al., 2021)Gu Z , Zhu T , Jiao X , Xu J , Qi Z ( 2021 ) Neural network soil moisture model for irrigation scheduling . Computers and Electronics in Agriculture 180 ( 1 ): 105801 . DOI: https://doi.org/10.1016/j.compag.2020.105801 . https://doi.org/10.1016/j.compag.2020.10...
|
China |
Neural network soil moisture model for irrigation scheduling |
Computers and Electronics in Agriculture |
38
|
2021 |
( Dias et al., 2021)Dias SHB , Filgueiras R , Filho EIF , Arcanjo GS , Da Silva GH , Mantovani EC , Da Cunha FF ( 2021 ) Reference evapotranspiration of Brazil modeled with machine learning techniques and remote sensing . PLoS ONE 16 ( 2 ): 1 – 20 . DOI: https://doi.org/10.1371/journal.pone.0245834 . https://doi.org/10.1371/journal.pone.024...
|
Brazil |
Reference evapotranspiration of Brazil modeled with machine learning techniques and remote sensing |
PLOS One |