Estimate leaf area index and leaf chlorophyll content in winter-wheat using image texture and vegetation indices derived from multi-temporal RGB images DOI Creative Commons
Xingjiao Yu,

Xuefei Huo,

Yingying Pi

et al.

Research Square (Research Square), Journal Year: 2023, Volume and Issue: unknown

Published: Dec. 21, 2023

Abstract Aims Investigating the potential of combining data dimensionality reduction methods with various linear regression models and machine learning algorithms to improve accuracy leaf area index (LAI) chlorophyll content (LCC) estimation in winter wheat based on UAV RGB imagery. Methods Constructed compared performance three techniques: multiple (MLR), ridge (RR), partial least squares (PLSR) algorithms: back-propagation neural networks(BP), random forests (RF) support vector (SVR) spectral vegetation indices (VIs), texture features (TEs) their combinations extracted from images. Moreover, different include principal component analysis (PCA), stepwise selection (ST) were used LAI LCC estimation. Results The highest correlation between LAI, was obtained window size 5 × 5, orientation 45° displacement 2 pixels. Combining VIs TEs improved for using or alone. RF model combined ST_PCA fusing achieved best estimations, R 0.86 0.91, RMSE 0.26 2.01, MAE 0.22 1.66 LCC, respectively. Conclusions ST_PCA, algorithms, holds promising monitoring crop physiological biochemical parameters.

Language: Английский

Combining spectrum, thermal, and texture features using machine learning algorithms for wheat nitrogen nutrient index estimation and model transferability analysis DOI
Shaohua Zhang,

Jianzhao Duan,

Xinghui Qi

et al.

Computers and Electronics in Agriculture, Journal Year: 2024, Volume and Issue: 222, P. 109022 - 109022

Published: May 12, 2024

Language: Английский

Citations

12
Improving Wheat Leaf Nitrogen Concentration (LNC) Estimation across Multiple Growth Stages Using Feature Combination Indices (FCIs) from UAV Multispectral Imagery DOI Creative Commons

Xiangxiang Su,

櫻井 克年,

Yue Hu

et al.

Agronomy, Journal Year: 2024, Volume and Issue: 14(5), P. 1052 - 1052

Published: May 15, 2024

Leaf nitrogen concentration (LNC) is a primary indicator of crop status, closely related to the growth and development dynamics crops. Accurate efficient monitoring LNC significant for precision field management enhancing productivity. However, biochemical properties canopy structure wheat change across different stages, leading variations in spectral responses that significantly impact estimation LNC. This study aims investigate construction feature combination indices (FCIs) sensitive multiple using remote sensing data develop an model suitable stages. The research employs UAV multispectral technology acquire imagery during early (Jointing stage Booting stage) late (Early filling Late stages) 2021 2022, extracting band reflectance texture metrics. Initially, twelve (SFCIs) were constructed information. Subsequently, (TFCIs) created metrics as alternative bands. Machine learning algorithms, including partial least squares regression (PLSR), random forest (RFR), support vector (SVR), Gaussian process (GPR), used integrate information, performance Results show Red, Red edge, Near-infrared bands, along with such Mean, Correlation, Contrast, Dissimilarity, has potential estimation. SFCIs TFCIs both enhanced responsiveness Additionally, index, Modified Vegetation Index (MVI), demonstrated improvement over NDVI, correcting over-saturation concerns NDVI time-series analysis displaying outstanding Spectral information outperforms capability, their integration, particularly SVR, achieves highest (coefficient determination (R2) = 0.786, root mean square error (RMSE) 0.589%, relative prediction deviation (RPD) 2.162). In conclusion, FCIs developed this improve enabling precise provides insights technical nutrition status

Language: Английский

Citations

9
Automated Phenotypic Analysis of Mature Soybean Using Multi-View Stereo 3D Reconstruction and Point Cloud Segmentation DOI Creative Commons

Dongcan Cui,

Pengfei Liu, Yunong Liu

et al.

Agriculture, Journal Year: 2025, Volume and Issue: 15(2), P. 175 - 175

Published: Jan. 14, 2025

Phenotypic analysis of mature soybeans is a critical aspect soybean breeding. However, manually obtaining phenotypic parameters not only time-consuming and labor intensive but also lacks objectivity. Therefore, there an urgent need for rapid, accurate, efficient method to collect the soybeans. This study develops novel pipeline acquiring traits based on three-dimensional (3D) point clouds. First, clouds are obtained using multi-view stereo 3D reconstruction method, followed by preprocessing construct dataset. Second, deep learning-based network, PVSegNet (Point Voxel Segmentation Network), proposed specifically segmenting pods stems. network enhances feature extraction capabilities through integration cloud voxel convolution, as well orientation-encoding (OE) module. Finally, such stem diameter, pod length, width extracted validated against manual measurements. Experimental results demonstrate that average Intersection over Union (IoU) semantic segmentation 92.10%, with precision 96.38%, recall 95.41%, F1-score 95.87%. For instance segmentation, achieves (AP@50) 83.47% (AR@50) 87.07%. These indicate feasibility In plant parameters, predicted values width, diameter exhibit coefficients determination (R2) 0.9489, 0.9182, 0.9209, respectively, demonstrates our can significantly improve efficiency accuracy, contributing application automated technology in

Language: Английский

Citations

1
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
Combining vegetation, color, and texture indices with hyperspectral parameters using machine-learning methods to estimate nitrogen concentration in rice stems and leaves DOI
Dunliang Wang, Rui Li, Tao Liu

et al.

Field Crops Research, Journal Year: 2023, Volume and Issue: 304, P. 109175 - 109175

Published: Nov. 1, 2023

Language: Английский

Citations

22
Combining features selection strategy and features fusion strategy for SPAD estimation of winter wheat based on UAV multispectral imagery DOI Creative Commons

Xiangxiang Su,

櫻井 克年,

Hiba Shaghaleh

et al.

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

Published: May 10, 2024

The Soil Plant Analysis Development (SPAD) is a vital index for evaluating crop nutritional status and serves as an essential parameter characterizing the reproductive growth of winter wheat. Non-destructive accurate monitorin3g wheat SPAD plays crucial role in guiding precise management nutrition. In recent years, spectral saturation problem occurring later stage has become major factor restricting accuracy estimation. Therefore, purpose this study to use features selection strategy optimize sensitive remote sensing information, combined with fusion integrate multiple characteristic features, order improve estimating SPAD. This conducted field experiments different varieties nitrogen treatments, utilized UAV multispectral sensors obtain canopy images during heading, flowering, late filling stages, extracted texture from images, employed (Boruta Recursive Feature Elimination) prioritize features. Support Vector Machine Regression algorithm are applied construct estimation model results showed that NIR band other bands can fully capture differences stage, red more During stability constructed using both superior models only single feature or no strategy. enhancement by method becomes significant, greatest improvement observed R 2 increasing 0.092-0.202, root mean squared error (RMSE) decreasing 0.076-4.916, ratio performance deviation (RPD) 0.237-0.960. conclusion, excellent application potential stages growth, providing theoretical basis technical support precision nutrient crops.

Language: Английский

Citations

8
Application of unmanned aerial vehicle optical remote sensing in crop nitrogen diagnosis: A systematic literature review DOI
Daoliang Li, S. Yang, Zhuangzhuang Du

et al.

Computers and Electronics in Agriculture, Journal Year: 2024, Volume and Issue: 227, P. 109565 - 109565

Published: Oct. 24, 2024

Language: Английский

Citations

6
Estimation of winter wheat LAI based on color indices and texture features of RGB images taken by UAV DOI
H.F. Li,

Xiaobin Yan,

Pengyan Su

et al.

Journal of the Science of Food and Agriculture, Journal Year: 2024, Volume and Issue: 105(1), P. 189 - 200

Published: Aug. 16, 2024

Abstract Background Leaf area index (LAI) is an important indicator for assessing plant growth and development, also closely related to photosynthesis in plants. The realization of rapid accurate estimation crop LAI plays role guiding farmland production. In study, the UAV‐RGB technology was used estimate based on 65 winter wheat varieties at different fertility periods, including farm varieties, main cultivars, new lines, core germplasm foreign varieties. Color indices (CIs) texture features were extracted from RGB images determine their quantitative link LAI. Results results revealed that among image features, exhibited a significant positive correlation with CIs ( r = 0.801), whereas there negative −0.783). Furthermore, visible atmospheric resistance index, green–red vegetation modified CIs, mean demonstrated strong > 0.8. With reference model input variables, backpropagation neural network (BPNN) R 2 0.730, RMSE 0.691, RPD 1.927) outperformed other models constructed by individual variables. Conclusion This study offers theoretical basis technical precise monitor consumer‐level UAVs. BPNN model, incorporating proved be superior estimating LAI, offered reliable method monitoring wheat. © 2024 Society Chemical Industry.

Language: Английский

Citations

5
SC-ResNeXt: A Regression Prediction Model for Nitrogen Content in Sugarcane Leaves DOI Creative Commons
Zihao Lu, Cuimin Sun,

Jinglie Dou

et al.

Agronomy, Journal Year: 2025, Volume and Issue: 15(1), P. 175 - 175

Published: Jan. 13, 2025

In agricultural production, the nitrogen content of sugarcane is assessed with precision and economy, which crucial for balancing fertilizer application, reducing resource waste, minimizing environmental pollution. As an important economic crop, productivity significantly influenced by various factors, especially supply. Traditional methods based on manually extracted image features are not only costly but also limited in accuracy generalization ability. To address these issues, a novel regression prediction model estimating sugarcane, named SC-ResNeXt (Enhanced Self-Attention, Spatial Attention, Channel Attention ResNeXt), has been proposed this study. The Self-Attention (SA) mechanism Convolutional Block Module (CBAM) have incorporated into ResNeXt101 to enhance model’s focus key its information extraction capability. It was demonstrated that achieved test R2 value 93.49% predicting leaves. After introducing SA CBAM attention mechanisms, improved 4.02%. Compared four classical deep learning algorithms, exhibited superior performance. This study utilized images captured smartphones combined automatic feature technologies, achieving precise economical predictions compared traditional laboratory chemical analysis methods. approach offers affordable technical solution small farmers optimize management plants, potentially leading yield improvements. Additionally, it supports development more intelligent farming practices providing predictions.

Language: Английский

Citations

0
Enhancing winter wheat plant nitrogen content prediction across different regions: Integration of UAV spectral data and transfer learning strategies DOI
Zongpeng Li,

Qian Cheng,

Li Chen

et al.

Computers and Electronics in Agriculture, Journal Year: 2025, Volume and Issue: 234, P. 110322 - 110322

Published: March 23, 2025

Language: Английский

Citations

0