Research on the Inversion of Key Growth Parameters of Rice Based on Multisource Remote Sensing Data and Deep Learning DOI Creative Commons
Jian Li, Jian Lü,

Hongkun Fu

и другие.

Agriculture, Год журнала: 2024, Номер 14(12), С. 2326 - 2326

Опубликована: Дек. 19, 2024

This study accurately inverts key growth parameters of rice, including Leaf Area Index (LAI), chlorophyll content (SPAD) value, and height, by integrating multisource remote sensing data (including MODIS ERA5 imagery) deep learning models. Dehui City in Jilin Province, China, was selected as the case area, where multidimensional vegetation indices, ecological function parameters, environmental variables were collected, covering seven stages rice. Data analysis parameter prediction conducted using a variety machine models Partial Least Squares (PLSs), Support Vector Machine (SVM), Random Forest (RF), Long Short-Term Memory Networks (LSTM), among which LSTM model demonstrated superior performance, particularly at multiple critical time points. The results show that performed best inverting three with LAI inversion accuracy on 21 August reaching coefficient determination (R2) 0.72, root mean square error (RMSE) 0.34, absolute (MAE) 0.27. SPAD same date achieved an R2 0.69, RMSE 1.45, MAE 1.16. height 25 July reached 0.74, 2.30, 2.08. not only verifies effectiveness combining advanced algorithms but also provides scientific basis for precision management decision-making rice cultivation.

Язык: Английский

Preparation of iron-rich sulfoaluminate cement by regulating Fe-bearing minerals DOI
Shuang Wu,

Yunfei Cui,

Xingliang Yao

и другие.

Ceramics International, Год журнала: 2025, Номер unknown

Опубликована: Фев. 1, 2025

Язык: Английский

Процитировано

1
Hydraulic Performance Optimization of Airfoil Weir-Orifice Facilities Based on Improved Hicks-Henne Shape Function and MOPSO Algorithm DOI Creative Commons
Bin Sun, Xiangyang Liu,

Lianghan Hu

и другие.

Research Square (Research Square), Год журнала: 2025, Номер unknown

Опубликована: Янв. 16, 2025

Abstract In order to better measure and control the flow rate in open channel systems, this study proposes a hydraulic performance optimization system for airfoil weir facilities. The is built around three essential modules: modified Hicks-Henne shape function, CFD numerical simulation, Multi-Objective Particle Swarm Optimization (MOPSO) algorithm, which are central reconstruction. specific example with of 0.033 m³/s rotation angle 15°, concepts head loss submergence were applied. results show that optimized design reduced by 9.14% increased 5.99%. Moreover, demonstrated excellent under different angles conditions. To further validate effect, formula was derived using π-theorem dimensional analysis theory incomplete similarity. indicate facility’s more accurate significantly errors. This provides strong theoretical practical support similar structures.

Язык: Английский

Процитировано

0
Intercropping with oilseeds enhances greenhouse gas mitigation during the initial establishment phase of tung trees DOI
Maiara Figueiredo Ramires, Douglas Adams Weiler, Eduardo Lorensi de Souza

и другие.

Agroforestry Systems, Год журнала: 2025, Номер 99(3)

Опубликована: Март 1, 2025

Язык: Английский

Процитировано

0
Effects of different irrigation treatments on dry matter accumulation, allocation and yield of grapes in solar greenhouse DOI Creative Commons

D. Wang,

Kaige Zhu,

Xinguang Wei

и другие.

Research Square (Research Square), Год журнала: 2025, Номер unknown

Опубликована: Март 3, 2025

Abstract Excessive irrigation wastes resources and impairs plant dry matter yield. The study explored the effects of three levels (I1: 65–85% θf, I2: 60–80% I3: 55–75% θf) a fully irrigated control (CK: 70–90% on grape matter, yield, resource use efficiency in solar greenhouse from 2023 to 2024. Results showed that treatments significantly affected accumulation organs aboveground parts, especially during fruit swelling maturity stages. logistic model simulated accumulation, with maximum theoretical (A) being most sensitive water changes. I3 treatment reduced A by 12.4-43.04% stem, 3.80-15.09% leaf, 3.87–26.45% fruit, 8.23–35.27% parts. Lower amount shortened rapid growth stage duration (T2) decreased rate time (Xmax) (Vmax) average (Vavg) rates. At maturity, lower promoted allocation leaves fruits but Mantel test revealed seven characteristic parameters were positively correlated yield radiation (RUE) (p < 0.05, r ≥ 0.2). random forest identified y3 y1 (the gradually slow stages) as critical influencing RUE. I1 was optimal increased (WUE) index 7.36 8.37%, 2.78 2.78% 2024, no significant impact or RUE > 0.05).

Язык: Английский

Процитировано

0
Research on the Inversion of Key Growth Parameters of Rice Based on Multisource Remote Sensing Data and Deep Learning DOI Creative Commons
Jian Li, Jian Lü,

Hongkun Fu

и другие.

Agriculture, Год журнала: 2024, Номер 14(12), С. 2326 - 2326

Опубликована: Дек. 19, 2024

This study accurately inverts key growth parameters of rice, including Leaf Area Index (LAI), chlorophyll content (SPAD) value, and height, by integrating multisource remote sensing data (including MODIS ERA5 imagery) deep learning models. Dehui City in Jilin Province, China, was selected as the case area, where multidimensional vegetation indices, ecological function parameters, environmental variables were collected, covering seven stages rice. Data analysis parameter prediction conducted using a variety machine models Partial Least Squares (PLSs), Support Vector Machine (SVM), Random Forest (RF), Long Short-Term Memory Networks (LSTM), among which LSTM model demonstrated superior performance, particularly at multiple critical time points. The results show that performed best inverting three with LAI inversion accuracy on 21 August reaching coefficient determination (R2) 0.72, root mean square error (RMSE) 0.34, absolute (MAE) 0.27. SPAD same date achieved an R2 0.69, RMSE 1.45, MAE 1.16. height 25 July reached 0.74, 2.30, 2.08. not only verifies effectiveness combining advanced algorithms but also provides scientific basis for precision management decision-making rice cultivation.

Язык: Английский

Процитировано

0