Winter wheat yield prediction using convolutional neural networks from environmental and phenological data

• Published in: Scientific reports Vol. 12; no. 1; pp. 3215 - 14
• Main Authors: Srivastava, Amit Kumar, Safaei, Nima, Khaki, Saeed, Lopez, Gina, Zeng, Wenzhi, Ewert, Frank, Gaiser, Thomas, Rahimi, Jaber
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 25.02.2022; Nature Publishing Group; Nature Portfolio
• Subjects: 631/449; 704/158; Agricultural production; Correlation coefficient; Crop yield; Crops; Deep learning; Genotypes; Humanities and Social Sciences; Learning algorithms; Machine Learning; multidisciplinary; Neural networks; Neural Networks, Computer; Predictions; Science; Science (multidisciplinary); Seasonal variations; Seasons; Soil; Soil moisture; Triticum; Triticum aestivum; Weather; Wheat; Wind speed; Winter wheat

Crop yield forecasting depends on many interactive factors, including crop genotype, weather, soil, and management practices. This study analyzes the performance of machine learning and deep learning methods for winter wheat yield prediction using an extensive dataset of weather, soil, and crop phen...