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

Yogita Rahulsing Chavan,

Brinthakumari Swamikan,

Megha Varun Gupta

et al.

Remote Sensing in Earth Systems Sciences, Journal Year: 2024, Volume and Issue: unknown

Published: Sept. 20, 2024

Language: Английский

UAS-based remote sensing for agricultural Monitoring: Current status and perspectives DOI
Jingzhe Wang, Silu Zhang, Iván Lizaga

et al.

Computers and Electronics in Agriculture, Journal Year: 2024, Volume and Issue: 227, P. 109501 - 109501

Published: Oct. 15, 2024

Language: Английский

Citations

9
Multi-modal fusion and multi-task deep learning for monitoring the growth of film-mulched winter wheat DOI
Zhikai Cheng, Xiaobo Gu,

Yadan Du

et al.

Precision Agriculture, Journal Year: 2024, Volume and Issue: 25(4), P. 1933 - 1957

Published: May 2, 2024

Language: Английский

Citations

7
Crop aboveground biomass monitoring model based on UAV spectral index reconstruction and Bayesian model averaging: A case study of film-mulched wheat and maize DOI

Zhikai Cheng,

Xiaobo Gu,

Zhihui Zhou

et al.

Computers and Electronics in Agriculture, Journal Year: 2024, Volume and Issue: 224, P. 109190 - 109190

Published: June 26, 2024

Language: Английский

Citations

7
Improving UAV hyperspectral monitoring accuracy of summer maize soil moisture content with an ensemble learning model fusing crop physiological spectral responses DOI
Hao Liu, Junying Chen, Youzhen Xiang

et al.

European Journal of Agronomy, Journal Year: 2024, Volume and Issue: 160, P. 127299 - 127299

Published: Aug. 7, 2024

Language: Английский

Citations

7
Hybridization of process-based models, remote sensing, and machine learning for enhanced spatial predictions of wheat yield and quality DOI Creative Commons

Ahmed M. S. Kheir,

Ajit Govind, Vinay Nangia

et al.

Computers and Electronics in Agriculture, Journal Year: 2025, Volume and Issue: 234, P. 110317 - 110317

Published: March 23, 2025

Language: Английский

Citations

1
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
Advancing Table Beet Root Yield Estimation Via Unmanned Aerial Systems (Uas) Multimodal Sensing DOI
Mohammad S. Saif, Robert Chancia, Sean P. Murphy

et al.

Published: Jan. 1, 2025

Language: Английский

Citations

0
Monitoring aboveground organs biomass of wheat and maize: A novel model combining ensemble learning and allometric theory DOI

Zhikai Cheng,

Xiaobo Gu,

Chunyu Wei

et al.

European Journal of Agronomy, Journal Year: 2024, Volume and Issue: 161, P. 127338 - 127338

Published: Sept. 11, 2024

Language: Английский

Citations

1
Identification of traditional settlement landscape areas in Hainan based on unmanned aerial remote sensing imagery DOI Open Access
Minghui Xu,

Zhanchuan Chen

Applied Mathematics and Nonlinear Sciences, Journal Year: 2024, Volume and Issue: 9(1)

Published: Jan. 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.

Language: Английский

Citations

0
Enhanced Crop Yield Forecasting Using Deep Reinforcement Learning and Multi-source Remote Sensing Data DOI

Yogita Rahulsing Chavan,

Brinthakumari Swamikan,

Megha Varun Gupta

et al.

Remote Sensing in Earth Systems Sciences, Journal Year: 2024, Volume and Issue: unknown

Published: Sept. 20, 2024

Language: Английский

Citations

0