Winter Wheat Yield Prediction Using Satellite Remote Sensing Data and Deep Learning Models

Agronomy, Journal Year: 2025, Volume and Issue: 15(1), P. 205 - 205

Published: Jan. 16, 2025

Accurate crop yield prediction is crucial for formulating agricultural policies, guiding management, and optimizing resource allocation. This study proposes a method predicting yields in China’s major winter wheat-producing regions using MOD13A1 data deep learning model which incorporates an Improved Gray Wolf Optimization (IGWO) algorithm. By adjusting the key parameters of Convolutional Neural Network (CNN) with IGWO, accuracy significantly enhanced. Additionally, explores potential Green Normalized Difference Vegetation Index (GNDVI) prediction. The research utilizes collected from March to May between 2001 2010, encompassing vegetation indices, environmental variables, statistics. results indicate that IGWO-CNN outperforms traditional machine approaches standalone CNN models terms accuracy, achieving highest performance R2 0.7587, RMSE 593.6 kg/ha, MAE 486.5577 MAPE 11.39%. finds April optimal period early wheat. validates effectiveness combining remote sensing prediction, providing technical support precision agriculture contributing global food security sustainable development.

Language: Английский

Discrete Grey Wolf Optimizer for Solving Urban Traffic Light Scheduling Problem

Arabian Journal for Science and Engineering, Journal Year: 2024, Volume and Issue: unknown

Published: Oct. 8, 2024

Language: Английский