Hyperspectral Estimation of Leaf Nitrogen Content in White Radish Based on Feature Selection and Integrated Learning DOI Creative Commons
Yafeng Li, Xingang Xu,

Wenbiao Wu

et al.

Remote Sensing, Journal Year: 2024, Volume and Issue: 16(23), P. 4479 - 4479

Published: Nov. 29, 2024

Nitrogen is the main nutrient element in growth process of white radish, and accurate monitoring radish leaf nitrogen content (LNC) an important guide for precise fertilization decisions field. Using LNC as object, research on hyperspectral estimation methods was carried out based field sample data at multiple stages using feature selection integrated learning algorithm models. First, Vegetation Index (VI) constructed from data. We extracted sensitive features VI response to Pearson’s feature-selection approach. Second, a stacking-integrated approach proposed machine algorithms such Support Vector Machine (SVM), Random Forest (RF), Ridge K-Nearest Neighbor (KNN) base model first layer architecture, Lasso meta-model second realize LNC. The analysis results show following: (1) bands are mainly centered around 600–700 nm 1950 nm, VIs also concentrated this band range. (2) Stacking with spectral inputs achieved good prediction accuracy leaf, R2 = 0.7, MAE 0.16, MSE 0.05 estimated over whole stage radish. (3) variable filtering function chosen meta-model, which has redundant model-selection effect helps improve quality framework. This study demonstrates potential method stages.

Language: Английский

DM_CorrMatch: A Semi-Supervised Semantic Segmentation Framework for Rapeseed Flower Coverage Estimation Using UAV Imagery DOI Creative Commons
Jie Li, Chengyong Zhu, Chenbo Yang

et al.

Research Square (Research Square), Journal Year: 2025, Volume and Issue: unknown

Published: March 28, 2025

Abstract Background Rapeseed(Brassica napus L.) inflorescence coverage is a crucial phenotypic parameter for assessing crop growth and estimating yield. Accurate cover assessment typically performed using Unmanned Aerial Vehicles (UAVs) in combination with semantic segmentation methods. However, the irregular variable morphology of rapeseed inflorescences presents significant challenges segmentation. To address these challenges, advanced methods that can improve accuracy, particularly under limited data conditions, are needed. Results In this study, we propose cost-effective high-throughput approach semi-supervised learning framework, DM_CorrMatch. This method enhances input images through strong weak augmentation techniques, while leveraging Denoising Diffusion Probabilistic Model (DDPM) to generate additional samples data-scarce scenarios.We an automatic update strategy labeled dilute proportion erroneous labels manual Furthermore, novel network architecture, Mamba-Deeplabv3+, proposed, combining strengths Mamba Convolutional Neural Networks (CNNs) both global local feature extraction. architecture effectively captures key features, even varying poses, reducing influence complex backgrounds. The proposed validated on Rapeseed Flower Segmentation Dataset (RFSD), which consists 720 UAV from Yangluo experimental station Oil Crops Research Institute Chinese Academy Agricultural Sciences (CAAS). results showed our outperforms four traditional eleven deep methods, achieving Intersection over Union (IoU) 0.886, Precision 0.942, Recall 0.940. Conclusions The learning-based method, combined Mamba-Deeplabv3+ demonstrates superior performance accurately segmenting challenging conditions. Our handles backgrounds various poses inflorescences, providing reliable tool flower estimation. aid development high-yield cultivars monitoring UAV-based technologies.

Language: Английский

Citations

0
Rapid mapping of soybean planting areas under complex crop structures: A modified GWCCI approach DOI

Linsheng Huang,

B. K. Miao,

Bao She

et al.

Computers and Electronics in Agriculture, Journal Year: 2025, Volume and Issue: 235, P. 110326 - 110326

Published: April 8, 2025

Language: Английский

Citations

0
A New Method for Optimizing the Jet-Cleaning Performance of Self-Cleaning Screen Filters: The 3D CFD-ANN-GA Framework DOI Open Access
Zheng Qin, Zhen Chen, Rui Chen

et al.

Processes, Journal Year: 2025, Volume and Issue: 13(4), P. 1194 - 1194

Published: April 15, 2025

The jet-type self-cleaning screen filter integrates industrial jet-cleaning technology into the process of filters in drip irrigation system, which has advantages low water consumption, high cleaning capacity, and wide applicability compared to traditional filters. However, its commercialization faces challenges as optimal jet mode optimization method have not been determined. This study proposes a framework that combines computational fluid dynamics (CFD), artificial neural networks (ANN), genetic algorithms (GA) for optimizing parameters improve performance. results show that, among main influencing nozzle, incident section diameter d V-groove half angle β most significant effects on peak wall shear stress, action area, consumption cleaning. ANN higher accuracy predicting performance (R2 = 0.9991, MAE 9.477), it can effectively replace CFD model parameters. resulted 1.34% reduction 16.82% 7.6% increase area base model. combining CFD, ANN, GA provide an parameter scheme

Language: Английский

Citations

0
A robust two-stage framework for maize above-ground biomass prediction integrating spectral remote sensing and allometric growth model DOI

Mohan Yang,

Qiang Wu, Jianbo Qi

et al.

Computers and Electronics in Agriculture, Journal Year: 2025, Volume and Issue: 235, P. 110398 - 110398

Published: April 19, 2025

Language: Английский

Citations

0
Accurate recognition and segmentation of northern corn leaf blight in drone RGB Images: A CycleGAN-augmented YOLOv5-Mobile-Seg lightweight network approach DOI
Fei Wen,

Hua Wu,

Xingxing Zhang

et al.

Computers and Electronics in Agriculture, Journal Year: 2025, Volume and Issue: 236, P. 110433 - 110433

Published: April 25, 2025

Language: Английский

Citations

0
DM_CorrMatch: a semi-supervised semantic segmentation framework for rapeseed flower coverage estimation using UAV imagery DOI Creative Commons
Jie Li, Chengyong Zhu, Chenbo Yang

et al.

Plant Methods, Journal Year: 2025, Volume and Issue: 21(1)

Published: April 25, 2025

Language: Английский

Citations

0
Hyperspectral Estimation of Leaf Nitrogen Content in White Radish Based on Feature Selection and Integrated Learning DOI Creative Commons
Yafeng Li, Xingang Xu,

Wenbiao Wu

et al.

Remote Sensing, Journal Year: 2024, Volume and Issue: 16(23), P. 4479 - 4479

Published: Nov. 29, 2024

Nitrogen is the main nutrient element in growth process of white radish, and accurate monitoring radish leaf nitrogen content (LNC) an important guide for precise fertilization decisions field. Using LNC as object, research on hyperspectral estimation methods was carried out based field sample data at multiple stages using feature selection integrated learning algorithm models. First, Vegetation Index (VI) constructed from data. We extracted sensitive features VI response to Pearson’s feature-selection approach. Second, a stacking-integrated approach proposed machine algorithms such Support Vector Machine (SVM), Random Forest (RF), Ridge K-Nearest Neighbor (KNN) base model first layer architecture, Lasso meta-model second realize LNC. The analysis results show following: (1) bands are mainly centered around 600–700 nm 1950 nm, VIs also concentrated this band range. (2) Stacking with spectral inputs achieved good prediction accuracy leaf, R2 = 0.7, MAE 0.16, MSE 0.05 estimated over whole stage radish. (3) variable filtering function chosen meta-model, which has redundant model-selection effect helps improve quality framework. This study demonstrates potential method stages.

Language: Английский

Citations

1