Sotirios V. Archontoulis
Plant Science · 2010–2021
A researcher in the Science corpus: 14 papers; trusts 37 researchers and is trusted by 5 researchers here, as inferred from the citation graph.
Subfields
- Plant Science — 120 citations into it; #3,360 of 8,232 by trust here, #5,217 by citations
- Agronomy and Crop Science — 86 citations into it
- Ecology, Evolution, Behavior and Systematics — 52 citations into it
- Artificial Intelligence — 34 citations into it
- Ecology — 25 citations into it
- Global and Planetary Change — 23 citations into it
- Statistics and Probability — 14 citations into it
- Computer Vision and Pattern Recognition — 11 citations into it
Whom Sotirios V. Archontoulis trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Xinyou Yin
- Jan de Leeuw
- Walter Zucchini
- In Jae Myung
- Thomas B. L. Kirkwood
- Giulia Vico
- Fraser Iain Lewis
- Robert Tibshirani
- Amilcare Porporato
- Paul C. Struik
Who trusts Sotirios V. Archontoulis overall
Papers from Sotirios V. Archontoulis's perspective
Ordered by how strongly the researchers Sotirios V. Archontoulis trusts endorse each paper. Same corpus, a different order for every researcher.
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Long Short-Term Memory
- ImageNet classification with deep convolutional neural networks
- Going deeper with convolutions
- Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
- Gradient-based learning applied to document recognition
- Dropout: a simple way to prevent neural networks from overfitting
- Attention is All you Need (Neural Information Processing Systems)
- ImageNet: A large-scale hierarchical image database
Their papers
- Insufficient and excessive N fertilizer input reduces maize root mass across soil types (2021) — 64 citations
- Assessing the uncertainty of maize yield without nitrogen fertilization (2021) — 53 citations
- Predicting crop yields and soil‐plant nitrogen dynamics in the US Corn Belt (2020) — 137 citations
- Root to shoot and carbon to nitrogen ratios of maize and soybean crops in the US Midwest (2020) — 95 citations
- Coupling machine learning and crop modeling improves crop yield prediction in the US Corn Belt (2020) — 379 citations
- Planting Date, Hybrid Maturity, and Weather Effects on Maize Yield and Crop Stage (2019) — 114 citations
- Development of a nitrogen recommendation tool for corn considering static and dynamic variables (2019) — 48 citations
- A CNN-RNN Framework for Crop Yield Prediction (2019) — 612 citations
- Simulation of maize evapotranspiration: An inter-comparison among 29 maize models (2019) — 88 citations
- Cereal yield gaps across Europe (2018) — 173 citations
- Maize and soybean root front velocity and maximum depth in Iowa, USA (2018) — 95 citations
- Tillage and Crop Rotation Effects on Corn Agronomic Response and Economic Return at Seven Iowa Locations (2015) — 83 citations
- Nonlinear Regression Models and Applications in Agricultural Research (2015) — 520 citations
- Phenological growth stages of Cynara cardunculus: codification and description according to the BBCH scale (2010) — 96 citations