Remote Sensing in Earth Systems Sciences, Journal Year: 2024, Volume and Issue: unknown
Published: Sept. 20, 2024
Language: Английский
Remote Sensing in Earth Systems Sciences, Journal Year: 2024, Volume and Issue: unknown
Published: Sept. 20, 2024
Language: Английский
Computers and Electronics in Agriculture, Journal Year: 2024, Volume and Issue: 227, P. 109501 - 109501
Published: Oct. 15, 2024
Language: Английский
Citations
9Precision Agriculture, Journal Year: 2024, Volume and Issue: 25(4), P. 1933 - 1957
Published: May 2, 2024
Language: Английский
Citations
7Computers and Electronics in Agriculture, Journal Year: 2024, Volume and Issue: 224, P. 109190 - 109190
Published: June 26, 2024
Language: Английский
Citations
7European Journal of Agronomy, Journal Year: 2024, Volume and Issue: 160, P. 127299 - 127299
Published: Aug. 7, 2024
Language: Английский
Citations
7Computers and Electronics in Agriculture, Journal Year: 2025, Volume and Issue: 234, P. 110317 - 110317
Published: March 23, 2025
Language: Английский
Citations
1Agronomy, 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
0Published: Jan. 1, 2025
Language: Английский
Citations
0European Journal of Agronomy, Journal Year: 2024, Volume and Issue: 161, P. 127338 - 127338
Published: Sept. 11, 2024
Language: Английский
Citations
1Applied 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
0Remote Sensing in Earth Systems Sciences, Journal Year: 2024, Volume and Issue: unknown
Published: Sept. 20, 2024
Language: Английский
Citations
0