Introduction to the Special Issue of Plants on “The Application of Spectral Techniques in Agriculture and Forestry” DOI Creative Commons
Youzhen Xiang

Plants, Journal Year: 2024, Volume and Issue: 13(18), P. 2632 - 2632

Published: Sept. 20, 2024

This Special Issue, titled “Applications of Spectral Technology in Agriculture and Forestry”, presents a collection cutting-edge research findings exploring various applications spectral analysis agricultural forestry environments [...]

Language: Английский

Monitoring Soybean Soil Moisture Content Based on UAV Multispectral and Thermal-Infrared Remote-Sensing Information Fusion DOI Creative Commons

Hongzhao Shi,

Zhiying Liu,

Siqi Li

et al.

Plants, Journal Year: 2024, Volume and Issue: 13(17), P. 2417 - 2417

Published: Aug. 29, 2024

By integrating the thermal characteristics from thermal-infrared remote sensing with physiological and structural information of vegetation revealed by multispectral sensing, a more comprehensive assessment crop soil-moisture-status response can be achieved. In this study, remote-sensing data, along soil-moisture-content (SMC) samples (0~20 cm, 20~40 40~60 cm soil layers), were collected during flowering stage soybean. Data sources included indices, texture features, indices. Spectral parameters significant correlation level (

Language: Английский

Citations

4
Winter Oilseed Rape LAI Inversion via Multi-Source UAV Fusion: A Three-Dimensional Texture and Machine Learning Approach DOI Creative Commons
Zijun Tang, Junsheng Lu, Ahmed Elsayed Abdelghany

et al.

Plants, Journal Year: 2025, Volume and Issue: 14(8), P. 1245 - 1245

Published: April 19, 2025

Leaf area index (LAI) serves as a critical indicator for evaluating crop growth and guiding field management practices. While spectral information (vegetation indices texture features) extracted from multispectral sensors mounted on unmanned aerial vehicles (UAVs) holds promise LAI estimation, the limitations of single-texture features necessitate further exploration. Therefore, this study conducted experiments over two consecutive years (2021–2022) to collect winter oilseed rape ground truth data corresponding UAV imagery. Vegetation were constructed, canopy extracted. Subsequently, correlation matrix method was employed establish novel randomized combinations three-dimensional indices. By analyzing correlations between these parameters LAI, variables with significant (p < 0.05) selected model inputs. These then partitioned into distinct input three machine learning models—Support Vector Machine (SVM), Backpropagation Neural Network (BPNN), Extreme Gradient Boosting (XGBoost)—to estimate LAI. The results demonstrated that majority vegetation exhibited 0.05). All also showed strong Notably, NDTTI highest (R = 0.725), derived spatial combination DIS5, VAR5, VAR3. Integrating indices, features, inputs XGBoost yielded estimation accuracy. validation set achieved determination coefficient (R2) 0.882, root mean square error (RMSE) 0.204 cm2cm−2, relative (MRE) 6.498%. This provides an effective methodology UAV-based monitoring offers scientific technical support precision agriculture

Language: Английский

Citations

0
Nitrogen nutritional diagnosis of summer maize (Zea mays L.) based on a hyperspectral data collaborative approach-evaluation of the estimation potential of three-dimensional spectral indices DOI
Zijun Tang, Yaohui Cai,

Youzhen Xiang

et al.

Computers and Electronics in Agriculture, Journal Year: 2024, Volume and Issue: 229, P. 109713 - 109713

Published: Dec. 10, 2024

Language: Английский

Citations

1
Introduction to the Special Issue of Plants on “The Application of Spectral Techniques in Agriculture and Forestry” DOI Creative Commons
Youzhen Xiang

Plants, Journal Year: 2024, Volume and Issue: 13(18), P. 2632 - 2632

Published: Sept. 20, 2024

This Special Issue, titled “Applications of Spectral Technology in Agriculture and Forestry”, presents a collection cutting-edge research findings exploring various applications spectral analysis agricultural forestry environments [...]

Language: Английский

Citations

0