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

Comparative Analysis of RGB and Multispectral UAV Image Data for Leaf Area Index Estimation of Sweet Potato DOI Creative Commons

Shoki Ochiai,

Erika Kamada,

Ryo Sugiura

и другие.

Smart Agricultural Technology, Год журнала: 2024, Номер unknown, С. 100579 - 100579

Опубликована: Сен. 1, 2024

Язык: Английский

Процитировано

2
Diagnostic study of nitrogen nutrition in cotton based on unmanned aerial vehicle RGB images DOI Creative Commons
Lu Wang,

Qiushuang Yao,

Ze Zhang

и другие.

Notulae Botanicae Horti Agrobotanici Cluj-Napoca, Год журнала: 2024, Номер 52(2), С. 13728 - 13728

Опубликована: Май 21, 2024

Nitrogen fertilizer levels significantly affect crop growth and development, necessitating precision management. Most studies focus on nitrogen nutrient estimation using vegetation indices textural features, overlooking the diagnostic potential of color features. Hence, we investigated cotton nutrition status unmanned aerial vehicle (UAV) image features index (NNI). Random frog algorithm - random forest-screened feature sets correlated with NNI, which were substituted into four machine learning algorithms for NNI modeling. The composite scores (F) optimal calculated coefficient variation method comprehensive diagnosis. Validation model determining critical concentration in yielded a determination R2 = 0.89, root mean square error RMSE 0.50 g (100 g)-1, absolute MAE 0.44, demonstrating improved performance. Additionally, our novel constructed based exhibited R2c 0.97, RMSEc 0.02, MAEc R2v 0.85, RMSEv 0.05, MAEv 0.04. Polynomial fitting indicated that was reliable following criterion: 0.48 < F2 0.67 overapplication, whereas or > deficiency. This study demonstrates superior effectiveness UAV RGB quick, accurate diagnosis levels, will help guide application.

Язык: Английский

Процитировано

1
Estimating Rice Leaf Nitrogen Content and Field Distribution Using Machine Learning with Diverse Hyperspectral Features DOI Creative Commons
Ting Tian, Jianliang Wang,

Yueyue Tao

и другие.

Agronomy, Год журнала: 2024, Номер 14(12), С. 2760 - 2760

Опубликована: Ноя. 21, 2024

Leaf nitrogen content (LNC) is a vital agronomic parameter in rice, commonly used to evaluate photosynthetic capacity and diagnose crop nutrient levels. Nitrogen deficiency can significantly reduce yield, underscoring the importance of accurate LNC estimation for practical applications. This study utilizes hyperspectral UAV imagery acquire rice canopy data, applying various machine learning regression algorithms (MLR) develop an model create concentration distribution map, offering valuable guidance subsequent field management. The analysis incorporates four types spectral data extracted throughout growth cycle: original reflectance bands (OR bands), vegetation indices (VIs), first-derivative (FD variable parameters (HSPs) as inputs, while measured serves output. Results demonstrate that random forest (RFR) gradient boosting decision tree (GBDT) performed effectively, with GBDT achieving highest average R2 0.76 across different treatments. Among models varieties, RFR exhibited superior accuracy, 0.95 SuXiangJing100 variety, reached 0.93. Meanwhile, support vector (SVMR) showed slightly lower partial least-squares (PLSR) was least effective. developed method applicable whole stage common varieties. suitable estimating stages, treatments, it also provides reference fertilization planning at flight altitudes other than 120 m this study.

Язык: Английский

Процитировано

1
Identifying optimal ground feature classification and assessing leaf nitrogen status based on UAV multispectral images in an apple orchard DOI

Guangzhao Sun,

Shuaihong Chen,

Tiantian Hu

и другие.

Plant and Soil, Год журнала: 2024, Номер unknown

Опубликована: Окт. 22, 2024

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