Identification of High-Photosynthetic-Efficiency Wheat Varieties Based on Multi-Source Remote Sensing from UAVs DOI Creative Commons

Weiyi Feng,

Yubin Lan, Hongzhi Zhao

et al.

Agronomy, Journal Year: 2024, Volume and Issue: 14(10), P. 2389 - 2389

Published: Oct. 16, 2024

Breeding high-photosynthetic-efficiency wheat varieties is a crucial link in safeguarding national food security. Traditional identification methods necessitate laborious on-site observation and measurement, consuming time effort. Leveraging unmanned aerial vehicle (UAV) remote sensing technology to forecast photosynthetic indices opens up the potential for swiftly discerning varieties. The objective of this research develop multi-stage predictive model encompassing nine indicators at field scale breeding. These include soil plant analyzer development (SPAD), leaf area index (LAI), net rate (Pn), transpiration (Tr), intercellular CO2 concentration (Ci), stomatal conductance (Gsw), photochemical quantum efficiency (PhiPS2), PSII reaction center excitation energy capture (Fv’/Fm’), quenching coefficient (qP). ultimate goal differentiate through model-based predictions. This gathered red, green, blue spectrum (RGB) multispectral (MS) images eleven stages jointing, heading, flowering, filling. Vegetation (VIs) texture features (TFs) were extracted as input variables. Three machine learning regression models (Support Vector Machine Regression (SVR), Random Forest (RF), BP Neural Network (BPNN)) employed construct across multiple growth stages. Furthermore, conducted principal component analysis (PCA) membership function on predicted values optimal each indicator, established comprehensive evaluation high efficiency, cluster screen test materials. categorized into three groups, with SH06144 Yannong 188 demonstrating higher efficiency. moderately efficient group comprises Liangxing 19, SH05604, SH06085, Chaomai 777, SH05292, Jimai 22, Guigu 820, totaling seven Xinmai 916 Jinong 114 fall category lower aligning closely results clustering based actual measurements. findings suggest that employing UAV-based multi-source identify feasible. study provide theoretical basis winter phenotypic monitoring breeding using sensing, offering valuable insights advancement smart practices

Language: Английский

Construction and Optimization of Integrated Yield Prediction Model Based on Phenotypic Characteristics of Rice Grown in Small–Scale Plantations DOI Creative Commons

Jihong Sun,

Peng Tian,

Zhaowen Li

et al.

Agriculture, Journal Year: 2025, Volume and Issue: 15(2), P. 181 - 181

Published: Jan. 15, 2025

An intelligent prediction model for rice yield in small-scale cultivation areas can provide precise forecasting results farmers, planting enterprises, and researchers, holding significant importance agricultural industries crop science research within small regions. Although machine learning handle complex nonlinear problems to enhance accuracy, further improvements models are still needed accurately predict yields facing environments, thereby enhancing performance. This study employs four phenotypic traits, namely, panicle angle, length, total branch grain number, along with seven methods—multiple linear regression, support vector machine, MLP, random forest, GBR, XGBoost, LightGBM—to construct a group. Subsequently, the top three best performance individual predictions integrated using voting stacking ensemble methods obtain optimal model. Finally, impact of different traits on stacked is explored. Experimental indicate that forest performs after modeling, RMSE, R2, MAPE values 0.2777, 0.9062, 17.04%, respectively. After integration, Stacking–3m demonstrates performance, 0.2483, 0.9250, 6.90%, Compared RMSE decreased by 10.58%, R2 increased 1.88%, 0.76%, indicating improved ensemble. The model, which demonstrated comprehensive evaluation metrics, was selected validation, validation were satisfactory, MAE, 8.3384, 0.9285, 0.2689, above findings demonstrate this possesses high practical value fills gap Yunnan Plateau region.

Language: Английский

Citations

1
Modern computational approaches for rice yield prediction: A systematic review of statistical and machine learning-based methods DOI
Djavan De Clercq, Adam Mahdi

Computers and Electronics in Agriculture, Journal Year: 2025, Volume and Issue: 231, P. 109852 - 109852

Published: Feb. 5, 2025

Language: Английский

Citations

1
Counterfactual Based Approaches for Feature Attributions of Stress Factors Affecting Rice Yield DOI Creative Commons
Nisha P. Shetty,

Balachandra Muniyal,

Ketavarapu Sriyans

et al.

Engineering Reports, Journal Year: 2025, Volume and Issue: 7(1)

Published: Jan. 1, 2025

ABSTRACT Agriculture is a crucial sector in many countries, particularly India, where it significantly influences the economy, food supply, and rural livelihoods. The increased integration of Deep Learning (DL) Machine (ML) into agriculture has enabled substantial advancements predicting crop yields analyzing factors affecting them. counterfactual reasoning framework DICE outperforms LIME offering finer insights feature importance relative impact different on yield prediction. provided clearest causal insights, demonstrating how adjustments to attributes like sandy alfisols surface texture could lead significant changes by water retention nutrient availability. SHAP ranked features phosphate potash based their average across dataset, global view influential but lacking in‐depth understanding. localized immediate influences, such as rainfall nitrogen content, although fell short revealing broader interactions essential for targeted agricultural interventions. findings highlight significance explanations ML models, they provide robust understanding relationships, going beyond correlation‐based attributions. study provides understandable practical allowing focused actions enhance productivity adaptability agriculture. By improving interpretability machine learning research ultimately supports creation predictive systems that strengthen sustainable practices economic development within industry.

Language: Английский

Citations

0
UAV-based rice aboveground biomass estimation using a random forest model with multi-organ feature selection DOI Creative Commons
Jing Shi, Kaili Yang,

Ningge Yuan

et al.

European Journal of Agronomy, Journal Year: 2025, Volume and Issue: 164, P. 127529 - 127529

Published: Feb. 10, 2025

Language: Английский

Citations

0
Towards optimal anticipatory action: Maximizing the effectiveness of agricultural early warning systems with operations research DOI
Djavan De Clercq, Lily Xu, Marleen de Ruiter

et al.

International Journal of Disaster Risk Reduction, Journal Year: 2025, Volume and Issue: unknown, P. 105249 - 105249

Published: Feb. 1, 2025

Language: Английский

Citations

0
Ensemble Machine Learning Models for Rice and Wheat Yield Prediction: A Comparative Study Across Districts in India’s Kharif and Rabi Seasons DOI
Vladimir P. Zhdanov,

Dmitry Kaplin,

Mikhail Akimov

et al.

Journal of the Indian Society of Remote Sensing, Journal Year: 2025, Volume and Issue: unknown

Published: April 28, 2025

Language: Английский

Citations

0
Prediction of Spatial Winter Wheat Yield by Combining Multiscale Time Series of Vegetation and Meteorological Indices DOI Creative Commons
Hao Xu,

Hongfei Yin,

Jia Liu

et al.

Agronomy, Journal Year: 2025, Volume and Issue: 15(5), P. 1114 - 1114

Published: April 30, 2025

In the context of climate change and development sustainable agricultural, crop yield prediction is key to ensuring food security. this study, long-term vegetation meteorological indices were obtained from MOD09A1 product daily weather data. Three types time series data constructed by aggregating an 8-day period (DP), 9-month (MP), six growth periods (GP). And we developed model using random forest (RF) long short-term memory (LSTM) networks. Results showed that average root mean squared error (RMSE) RF in each province was 0.5 Mg/ha lower than LSTM model. Both accuracies increased with later stages Partial dependence plots influence degree DVI on above 2 Mg/ha. When length feature variables shortened MP or GP, growing days (GDD), minimum temperature (AveTmin), effective precipitation (EP) stronger nonlinear relationships statistical yields.

Language: Английский

Citations

0
Identification of High-Photosynthetic-Efficiency Wheat Varieties Based on Multi-Source Remote Sensing from UAVs DOI Creative Commons

Weiyi Feng,

Yubin Lan, Hongzhi Zhao

et al.

Agronomy, Journal Year: 2024, Volume and Issue: 14(10), P. 2389 - 2389

Published: Oct. 16, 2024

Breeding high-photosynthetic-efficiency wheat varieties is a crucial link in safeguarding national food security. Traditional identification methods necessitate laborious on-site observation and measurement, consuming time effort. Leveraging unmanned aerial vehicle (UAV) remote sensing technology to forecast photosynthetic indices opens up the potential for swiftly discerning varieties. The objective of this research develop multi-stage predictive model encompassing nine indicators at field scale breeding. These include soil plant analyzer development (SPAD), leaf area index (LAI), net rate (Pn), transpiration (Tr), intercellular CO2 concentration (Ci), stomatal conductance (Gsw), photochemical quantum efficiency (PhiPS2), PSII reaction center excitation energy capture (Fv’/Fm’), quenching coefficient (qP). ultimate goal differentiate through model-based predictions. This gathered red, green, blue spectrum (RGB) multispectral (MS) images eleven stages jointing, heading, flowering, filling. Vegetation (VIs) texture features (TFs) were extracted as input variables. Three machine learning regression models (Support Vector Machine Regression (SVR), Random Forest (RF), BP Neural Network (BPNN)) employed construct across multiple growth stages. Furthermore, conducted principal component analysis (PCA) membership function on predicted values optimal each indicator, established comprehensive evaluation high efficiency, cluster screen test materials. categorized into three groups, with SH06144 Yannong 188 demonstrating higher efficiency. moderately efficient group comprises Liangxing 19, SH05604, SH06085, Chaomai 777, SH05292, Jimai 22, Guigu 820, totaling seven Xinmai 916 Jinong 114 fall category lower aligning closely results clustering based actual measurements. findings suggest that employing UAV-based multi-source identify feasible. study provide theoretical basis winter phenotypic monitoring breeding using sensing, offering valuable insights advancement smart practices

Language: Английский

Citations

1