UAS-based remote sensing for agricultural Monitoring: Current status and perspectives

Computers and Electronics in Agriculture, Год журнала: 2024, Номер 227, С. 109501 - 109501

Опубликована: Окт. 15, 2024

Язык: Английский

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Multi-modal fusion and multi-task deep learning for monitoring the growth of film-mulched winter wheat

Precision Agriculture, Год журнала: 2024, Номер 25(4), С. 1933 - 1957

Опубликована: Май 2, 2024

Язык: Английский

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Crop aboveground biomass monitoring model based on UAV spectral index reconstruction and Bayesian model averaging: A case study of film-mulched wheat and maize

Computers and Electronics in Agriculture, Год журнала: 2024, Номер 224, С. 109190 - 109190

Опубликована: Июнь 26, 2024

Язык: Английский

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Improving UAV hyperspectral monitoring accuracy of summer maize soil moisture content with an ensemble learning model fusing crop physiological spectral responses

European Journal of Agronomy, Год журнала: 2024, Номер 160, С. 127299 - 127299

Опубликована: Авг. 7, 2024

Язык: Английский

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Hybridization of process-based models, remote sensing, and machine learning for enhanced spatial predictions of wheat yield and quality

Computers and Electronics in Agriculture, Год журнала: 2025, Номер 234, С. 110317 - 110317

Опубликована: Март 23, 2025

Язык: Английский

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Prediction of Spatial Winter Wheat Yield by Combining Multiscale Time Series of Vegetation and Meteorological Indices

Agronomy, Год журнала: 2025, Номер 15(5), С. 1114 - 1114

Опубликована: Апрель 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.

Язык: Английский

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Advancing Table Beet Root Yield Estimation Via Unmanned Aerial Systems (Uas) Multimodal Sensing

Опубликована: Янв. 1, 2025

Язык: Английский

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Monitoring aboveground organs biomass of wheat and maize: A novel model combining ensemble learning and allometric theory

European Journal of Agronomy, Год журнала: 2024, Номер 161, С. 127338 - 127338

Опубликована: Сен. 11, 2024

Язык: Английский

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Identification of traditional settlement landscape areas in Hainan based on unmanned aerial remote sensing imagery

Applied Mathematics and Nonlinear Sciences, Год журнала: 2024, Номер 9(1)

Опубликована: Янв. 1, 2024

Abstract To enhance the accuracy of recognizing traditional settlement landscapes in Hainan, this study introduces a landscape recognition model predicated on full convolutional neural network (FCN). The research delineates selection specific types and sensors for Unmanned Aerial Vehicles (UAVs), employing UAV remote sensing technology to capture image data Hainan's landscapes. collected initial data, laden with interference information, underwent preprocessing step. On foundation, settlements was developed utilizing FCN. empirical results derived from deploying reveal presence ancient buildings within 400-meter radius principal river. Notably, these structures predominantly span distance ranging 100 350 meters. spatial distribution pattern edifices notably centers around Zong ancestral hall. Furthermore, when compared other benchmark models, proposed FCN exhibits superior performance forest, grassland, farmland Hainan landscape, achieving average rates 88.66% 84.91%, respectively. This investigation underscores significant potential applying identifying It provides pivotal technical support reference point survey forest resources ecological monitoring, thereby enhancing applicability dissemination tasks.

Язык: Английский

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Enhanced Crop Yield Forecasting Using Deep Reinforcement Learning and Multi-source Remote Sensing Data

Remote Sensing in Earth Systems Sciences, Год журнала: 2024, Номер unknown

Опубликована: Сен. 20, 2024

Язык: Английский

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