Utilizing VSWIR spectroscopy for macronutrient and micronutrient profiling in winter wheat DOI Creative Commons
Andrew C. Gill, Srishti Gaur, Clay Sneller

et al.

Frontiers in Plant Science, Journal Year: 2024, Volume and Issue: 15

Published: Oct. 31, 2024

This study explores the use of leaf-level visible-to-shortwave infrared (VSWIR) reflectance observations and partial least squares regression (PLSR) to predict foliar concentrations macronutrients (nitrogen, phosphorus, potassium, calcium, magnesium, sulfur), micronutrients (boron, copper, iron, manganese, zinc, molybdenum, aluminum, sodium), moisture content in winter wheat. A total 360 fresh wheat leaf samples were collected from a breeding population over two growing seasons. These used collect VSWIR across spectral range spanning 350 2,500 nm. then processed for nutrient composition allow examination ability accurately model diverse chemical components foliage. Models each developed using rigorous cross-validation methodology conjunction with three distinct component selection methods explore trade-offs between complexity performance final models. We examined absolute minimum predicted residual error sum (PRESS), backward iteration PRESS, Van der Voet's randomized

Language: Английский

Enhancing the accuracy of monitoring effective tiller counts of wheat using multi-source data and machine learning derived from consumer drones DOI

Ziheng Feng,

Jiaxiang Cai, Ke Wu

et al.

Computers and Electronics in Agriculture, Journal Year: 2025, Volume and Issue: 232, P. 110120 - 110120

Published: Feb. 24, 2025

Language: Английский

Citations

1
Forecasting yield and market classes of Vidalia sweet onions: A UAV-based multispectral and texture data-driven approach DOI Creative Commons
Marcelo Rodrigues Barbosa Júnior,

Lucas de Azevedo Sales,

Regimar Garcia dos Santos

et al.

Smart Agricultural Technology, Journal Year: 2025, Volume and Issue: unknown, P. 100808 - 100808

Published: Jan. 1, 2025

Language: Английский

Citations

0
Evaluation of cotton planting suitability in Xinjiang based on climate change and soil fertility factors simulated by coupled machine learning model DOI Creative Commons

Yonglin Jia,

Li Yi,

Asim Biswas

et al.

Resources Environment and Sustainability, Journal Year: 2025, Volume and Issue: unknown, P. 100200 - 100200

Published: March 1, 2025

Language: Английский

Citations

0
A novel vegetation index for monitoring the stress levels of pest caused by dusky cotton bug DOI

Hailin Yu,

Lianbin Hu,

Wenhao Cui

et al.

Computers and Electronics in Agriculture, Journal Year: 2025, Volume and Issue: 234, P. 110221 - 110221

Published: March 13, 2025

Language: Английский

Citations

0
Evaluation of crop water status using UAV-based images data with a model updating strategy DOI Creative Commons
Ning Yang, Zhitao Zhang, Xiaofei Yang

et al.

Agricultural Water Management, Journal Year: 2025, Volume and Issue: 312, P. 109445 - 109445

Published: March 20, 2025

Language: Английский

Citations

0
Transfer learning for enhancing the generality of leaf spectroscopic models in estimating crop foliar nutrients across growth stages DOI

Yurong Huang,

Wenqian Chen, Wei Tan

et al.

International Journal of Applied Earth Observation and Geoinformation, Journal Year: 2025, Volume and Issue: 139, P. 104481 - 104481

Published: March 17, 2025

Language: Английский

Citations

0
Data Integration Based on UAV Multispectra and Proximal Hyperspectra Sensing for Maize Canopy Nitrogen Estimation DOI Creative Commons

Fuhao Lu,

Sun Hai-ming,

Tao Leí

et al.

Remote Sensing, Journal Year: 2025, Volume and Issue: 17(8), P. 1411 - 1411

Published: April 16, 2025

Nitrogen (N) is critical for maize (Zea mays L.) growth and yield, necessitating precise estimation of canopy nitrogen concentration (CNC) to optimize fertilization strategies. Remote sensing technologies, such as proximal hyperspectral sensors unmanned aerial vehicle (UAV)-based multispectral imaging, offer promising solutions non-destructive CNC monitoring. This study evaluates the effectiveness sensor UAV-based data integration in estimating spring during key stages (from 11th leaf stage, V11, Silking R1). Field experiments were conducted collect (20 vegetation indices [MVI] 24 texture [MTI]), (24 [HVI] 20 characteristic [HCI]), alongside laboratory analysis 120 samples. The Boruta algorithm identified important features from integrated datasets, followed by correlation between these Random Forest (RF)-based modeling, with SHAP (SHapley Additive exPlanations) values interpreting feature contributions. Results demonstrated model achieved high accuracy Computational Efficiency (CE) (R2 = 0.879, RMSE 0.212, CE 2.075), outperforming HVI-HCI 0.832, 0.250, =2.080). Integrating yields a high-precision 0.903, 0.190), standalone models 2.73% 8.53%, respectively. However, decreased 1.93% 1.68%, Key included red-edge (NREI, NDRE, CI) parameters (R1m), (SR, PRI) spectral (SDy, Rg) exhibited varying directional impacts on using RF. Together, findings highlight that Boruta–RF–SHAP strategy demonstrates synergistic value integrating multi-source enhancing management cultivation.

Language: Английский

Citations

0
Better inversion of rice nitrogen nutrition index at early panicle initiation stage using spectral features, texture features, and wavelet features based on UAV multispectral imagery DOI Creative Commons
Ziwei Li, Yuliang Zhang, J. Lu

et al.

European Journal of Agronomy, Journal Year: 2025, Volume and Issue: 168, P. 127654 - 127654

Published: April 26, 2025

Language: Английский

Citations

0
Effects of Variety and Growth Stage on UAV Multispectral Estimation of Plant Nitrogen Content of Winter Wheat DOI Creative Commons
Meiyan Shu, Zhiyi Wang, Wei Guo

et al.

Agriculture, Journal Year: 2024, Volume and Issue: 14(10), P. 1775 - 1775

Published: Oct. 9, 2024

The accurate estimation of nitrogen content in crop plants is the basis precise fertilizer management. Unmanned aerial vehicle (UAV) imaging technology has been widely used to rapidly estimate plants, but accuracy will still be affected by variety, growth stage, and other factors. We aimed (1) analyze correlation between plant winter wheat spectral, texture, structural information; (2) compare at single versus multiple stages; (3) assess consistency UAV multispectral images estimating across different varieties; (4) identify best model for (PNC) comparing five machine learning algorithms. results indicated that PNC all varieties stages, random forest regression (RFR) performed among models, obtaining R2, RMSE, MAE, MAPE values 0.90, 0.10%, 0.08, 0.06%, respectively. Additionally, RFR achieved commendable three varieties, with R2 0.91, 0.93, 0.72. For dataset Gaussian process (GPR) ranging from 0.66 0.81. Due varying sensitivities, was also varieties. Among SL02-1 worst. This study helpful rapid diagnosis nutrition through technology.

Language: Английский

Citations

2
Estimating Rice Leaf Nitrogen Content and Field Distribution Using Machine Learning with Diverse Hyperspectral Features DOI Creative Commons
Ting Tian, Jianliang Wang,

Yueyue Tao

et al.

Agronomy, Journal Year: 2024, Volume and Issue: 14(12), P. 2760 - 2760

Published: Nov. 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.

Language: Английский

Citations

1