Smart irrigation for coriander plant: Saving water with AI and IoT DOI Creative Commons

Abhirup Paria,

Arindam Giri, Subrata Dutta

et al.

Research Square (Research Square), Journal Year: 2024, Volume and Issue: unknown

Published: June 3, 2024

Abstract Accurate forecasting of water requirements is crucial for optimizing irrigation and preservation. However, the Food Agriculture Organization(FAO Irrigation Drainage paper 56) Penman-Monteith(PM) model observed as highest quality method evapotranspiration (EVT0 ) forecasting. using PM often restricted by need predicted climatic factors, particularly solar radiation. This research article presents a real-time intelligent watering system coriander plants that can be monitored smartphones. The uses hybrid machine-learning technique Internet Things (IoT) sensors to sense weather circumstances directly from crop field. Nine distinct neural network models ((HML1, HML2 …, HML9)) are developed predict climate environmental variables. These optimized genetic algorithm achieve optimal efficiency. EVT0 forecasts proposed approach being compared against standard FAO56 Penman-Monteith technique. An in-depth analysis highly successful HML4 conducted, findings used in Android application enables monitoring. In addition, most favourable parameters determined even more improved outcomes. significantly minimize flood irrigation, consumption, labour expenses.

Language: Английский

A novel hybrid model combined with ensemble embedded feature selection method for estimating reference evapotranspiration in the North China Plain DOI Creative Commons
Hanmi Zhou,

Linshuang Ma,

Xiaoli Niu

et al.

Agricultural Water Management, Journal Year: 2024, Volume and Issue: 296, P. 108807 - 108807

Published: April 2, 2024

The reference evapotranspiration (ETo) is a key parameter in achieving sustainable use of agricultural water resources. To accurately acquire ETo under limited conditions, this study combined the northern goshawk optimization algorithm (NGO) with extreme gradient boosting (XGBoost) model to propose novel NGO-XGBoost model. performance was evaluated using meteorological data from 30 stations North China Plain and compared XGBoost, random forest (RF), k nearest neighbor (KNN) models. An ensemble embedded feature selection (EEFS) method results RF, adaptive (AdaBoost), categorical (CatBoost) models used obtain importance factors estimating ETo, thereby determine optimal combination inputs indicated that by top 3, 4, 5 important as input combinations, all achieved high estimation accuracy. It worth noting there were significant spatial differences precisions four models, but exhibited consistently precisions, global indicator (GPI) rankings 1st, range coefficient determination (R2), nash efficiency (NSE), root mean square error (RMSE), absolute (MAE) bias (MBE) 0.920–0.998, 0.902–0.998, 0.078–0.623 mm d−1, 0.058–0.430 −0.254–0.062 respectively. Furthermore, accuracy varied across different seasons, which more significantly affected humidity wind speed winter. When target station insufficient, trained historical neighboring still maintained precision. Overall, recommends reliable for provides calculating absence data.

Language: Английский

Citations

11
Better estimation of evapotranspiration and transpiration using an improved modified Priestly-Taylor model based on a new parameter of leaf senescence in a rice field DOI

Yujie Zhang,

Yansen Xu, Jianghua Wu

et al.

The Science of The Total Environment, Journal Year: 2024, Volume and Issue: 927, P. 171842 - 171842

Published: March 20, 2024

Language: Английский

Citations

4
Estimating Daily Reference Crop Evapotranspiration in Northeast China Using Optimized Empirical Models Based on Heuristic Intelligence Algorithms DOI Creative Commons
Zongyang Li, Zhongkuo Zhao, Liwen Xing

et al.

Agronomy, Journal Year: 2025, Volume and Issue: 15(3), P. 599 - 599

Published: Feb. 27, 2025

Accurately estimating reference crop evapotranspiration (ETo) improves agricultural water use efficiency. However, the accuracy of ETo estimation needs to be further improved in Northeast region China, country’s main grain production area. In this research, meteorological data from 30 sites China over past 59 years (1961–2019) were selected evaluate simulation 11 models. By using least square method (LSM) and three population heuristic intelligent algorithms—a genetic algorithm (GA), a particle swarm optimization (PSO), differential evolution (DE)—the parameters eleven kinds models optimized, respectively, model suitable for northeast was selected. The results showed that radiation-based Jensen Haise (JH) had best among empirical models, with R2 0.92. Hamon an acceptable accuracy, while combination low ranges 0.74–0.88. After LSM optimization, all been significantly by 0.58–12.1%. algorithms Door optimized GA DE higher Although JH requires more factors than model, it shows better stability. Regardless original formula or various algorithms, has is greater 0.91. Therefore, when only temperature radiation available, recommended estimate ETo, respectively; both underestimated absolute error range 0.01–0.02 mm d−1 compared Penman–Monteith (P–M) equation. When could used less 0.01 d−1. This study provided accurate within regional scope incomplete data.

Language: Английский

Citations

0
Future projection of compound flooding using downscaled CMIP6 GCM climate projections in the Mekong River Basin DOI
Sophal Try, Xiaosheng Qin, Ty Sok

et al.

Hydrological Sciences Journal, Journal Year: 2025, Volume and Issue: unknown

Published: April 9, 2025

Language: Английский

Citations

0
An intelligent and uncertain optimization framework for water-nitrogen synergistic management under extreme supply and demand water risks DOI

Xianghui Xu,

Yaowen Xu, Yan Zhou

et al.

Expert Systems with Applications, Journal Year: 2025, Volume and Issue: unknown, P. 127829 - 127829

Published: April 1, 2025

Language: Английский

Citations

0
Estimation of daily reference crop evapotranspiration in China based on time-space-LSTM model DOI

Zhenyuan Sun,

Boyan Sun, Shuang Li

et al.

Journal of Hydrology, Journal Year: 2025, Volume and Issue: unknown, P. 133430 - 133430

Published: May 1, 2025

Language: Английский

Citations

0
Development of temperature and mass transfer-based empirical models for estimating reference evapotranspiration in Nigeria DOI Creative Commons

Dauda Pius Awhari,

Mohamad Hidayat Jamal, Mohd Khairul Idlan Muhammad

et al.

Journal of Water and Climate Change, Journal Year: 2024, Volume and Issue: 15(7), P. 3377 - 3394

Published: June 25, 2024

ABSTRACT The empirical models commonly employed as alternatives for estimating evapotranspiration provide constraints and yield inaccurate results when applied to Nigeria. This study aims develop novel enhance (ET0) estimation accuracy in coefficients of non-linear equations were optimised using the particle swarm optimisation (PSO) algorithm development two new ET0 Nigeria, Awhari1 (temperature-based) Awhari2 (mass transfer-based). ERA5 reanalysis data with a 0.1° × resolution was used. rigorously assessed against FAO-56 Penman–Monteith method, resulting Kling–Gupta efficiency (KGE) percentage bias (Pbias) values 0.75, 6.49, 0.92, 5.67, respectively. spatial distribution analysis performance metrics showed both exhibited superior across diverse climatic zones incorporation PSO model development, coupled analysis, highlights study's multidimensional approach. indicates that they can be valuable tools water resource management, irrigation planning, sustainable agriculture practices

Language: Английский

Citations

3
Smart Irrigation for Coriander Plant: Saving Water with AI and IoT DOI

Abhirup Paria,

Arindam Giri, Subrata Dutta

et al.

Water Resources Management, Journal Year: 2025, Volume and Issue: unknown

Published: Jan. 22, 2025

Language: Английский

Citations

0
Estimating and forecasting daily reference crop evapotranspiration in China with temperature-driven deep learning models DOI Creative Commons
Jia Zhang, Yimin Ding, Lei Zhu

et al.

Agricultural Water Management, Journal Year: 2024, Volume and Issue: 307, P. 109268 - 109268

Published: Dec. 24, 2024

Language: Английский

Citations

2
Estimating the transpiration of kiwifruit using an optimized canopy resistance model based on the synthesis of sunlit and shaded leaves DOI Creative Commons
Zongyang Li, Lu Zhao, Zhongkuo Zhao

et al.

Agricultural Water Management, Journal Year: 2024, Volume and Issue: 306, P. 109193 - 109193

Published: Nov. 26, 2024

Language: Английский

Citations

0