แจ้งเอกสารไม่ครบถ้วน, ไม่ตรงกับชื่อเรื่อง หรือมีข้อผิดพลาดเกี่ยวกับเอกสาร ติดต่อที่นี่ ==>
หากไม่มีอีเมลผู้รับให้กรอก thailis-noc@uni.net.th
หากไม่มีอีเมลผู้รับให้กรอก thailis-noc@uni.net.th
Pattharaporn Thongnim. Crop evapotranspiration forecasting for iot-based smart irrigation via a data science review of statistical, machine learning and deep learning methods. (). King Mongkut's University of Technology North Bangkok. Central Library. : , 2026.
Crop evapotranspiration forecasting for iot-based smart irrigation via a data science review of statistical, machine learning and deep learning methods
Name: Sirawit Wongjeam
Organization :
Burapha University. Department of Mathematics
Email :
sirawit.wongjeam@gmail.com
Name: Preeyanuch Chuasuk
Organization :
Burapha University. Department of Mathematics
Email :
preeyanuch.ch@buu.ac.th
Name: Pattharaporn Thongnim
Organization :
Burapha University. Department of Mathematics
Email :
pattharaporn@buu.ac.th
LCSH:
Evapotranspiration
-- Forecasting
LCSH:
Irrigation
-- Management
LCSH:
Internet of things
LCSH:
Machine learning
LCSH:
Precision farming
Abstract:
Accurate crop evapotranspiration (ETc) forecasting is essential for smart irrigation management in tropical horticulture. Over the past two decades, forecasting methods have developed from classical statistical models to machine learning and deep learning approaches. However, a systematic review that compares all three paradigms under consistent criteria, especially in IoT-based tropical environments. This paper presents a critical data science review of statistical, machine learning, and deep learning methods for ETc forecasting with a focus on IoT-based smart irrigation in tropical horticulture. The review covers SARIMA, random forests, XGBoost, Long Short-Term Memory (LSTM), and Transformer encoder models. Six key gaps are identified, including climate bias, limited multi-step horizon evaluation and the lack of operational deployment validation. Five future research directions are proposed to address these gaps, including tropical benchmark datasets, standardized horizon evaluation, and multi-site transfer learning. The findings suggest that Deep Learning models currently offer the highest accuracy for hourly ETc forecasting in IoT-monitored tropical orchards but significant challenges remain before these models can be widely deployed in precision irrigation systems.
King Mongkut's University of Technology North Bangkok. Central Library
Address:
BANGKOK
Email:
library@kmutnb.ac.th
Created:
2026
Modified:
2026-08-14
Issued:
2026-08-14
บทความ/Article
application/pdf
BibliograpyCitation :
In ECTI Association and Burapha University. Faculty of Engineering. 2026 23rd International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology (ECTI-CON 2026) (pp.764-769). Piscataway, NJ : IEEE
eng
©copyrights King Mongkut's University of Technology North Bangkok
RightsAccess:
ใช้เวลา
0.039413 วินาที
Sirawit Wongjeam
| Title | Contributor | Type |
|---|---|---|
| Crop evapotranspiration forecasting for iot-based smart irrigation via a data science review of statistical, machine learning and deep learning methods
มหาวิทยาลัยเทคโนโลยีพระจอมเกล้าพระนครเหนือ Sirawit Wongjeam;Preeyanuch Chuasuk;Pattharaporn Thongnim | บทความ/Article |
Preeyanuch Chuasuk
| Title | Contributor | Type |
|---|---|---|
| Crop evapotranspiration forecasting for iot-based smart irrigation via a data science review of statistical, machine learning and deep learning methods
มหาวิทยาลัยเทคโนโลยีพระจอมเกล้าพระนครเหนือ Sirawit Wongjeam;Preeyanuch Chuasuk;Pattharaporn Thongnim | บทความ/Article |
Pattharaporn Thongnim
| Title | Contributor | Type |
|---|---|---|
| An EM algorithm for Gaussian Mixture Linear Regression with employment and carbon dioxide emissions data
มหาวิทยาลัยเทคโนโลยีพระจอมเกล้าพระนครเหนือ Pattharaporn Thongnim;Vasin Yuvanatemiya;Ploypailin Yodkham;Porawee Chotpitayasunon | บทความ/Article | |
| Smart agricultural computing development of robust data processing and machine learning pipeline for durian price prediction
มหาวิทยาลัยเทคโนโลยีพระจอมเกล้าพระนครเหนือ Chatdanai Roothan;Naritsara Aimkuy;Pattharaporn Thongnim | บทความ/Article | |
| Crop evapotranspiration forecasting for iot-based smart irrigation via a data science review of statistical, machine learning and deep learning methods
มหาวิทยาลัยเทคโนโลยีพระจอมเกล้าพระนครเหนือ Sirawit Wongjeam;Preeyanuch Chuasuk;Pattharaporn Thongnim | บทความ/Article |