Crop yield prediction integrating genotype and weather variables using deep learning

J Shook, T Gangopadhyay, L Wu… - Plos one, 2021 - journals.plos.org
Accurate prediction of crop yield supported by scientific and domain-relevant insights, is
useful to improve agricultural breeding, provide monitoring across diverse climatic …

A comparative study of 11 non-linear regression models highlighting autoencoder, DBN, and SVR, enhanced by SHAP importance analysis in soybean branching …

W Zhou, Z Yan, L Zhang - Scientific Reports, 2024 - nature.com
To explore a robust tool for advancing digital breeding practices through an artificial
intelligence-driven phenotype prediction expert system, we undertook a thorough analysis of …

Temporal attention and stacked LSTMs for multivariate time series prediction

T Gangopadhyay, SY Tan, G Huang, S Sarkar - 2018 - openreview.net
Temporal attention mechanism has been applied to get state-of-the-art results in neural
machine translation. LSTMs can capture the long-term temporal dependencies in a …

Estimation and forecasting of soybean yield using artificial neural networks

V Barbosa dos Santos, AMF Santos… - Agronomy …, 2021 - Wiley Online Library
In science, estimation is the calculation of a current value, while forecasting (or prediction) is
the calculation of a future value. Both estimation and forecasting are based on covariates …

[PDF][PDF] Deep time series attention models for crop yield prediction and insights

T Gangopadhyay, J Shook… - … Learning and the …, 2019 - ml4physicalsciences.github.io
Soybean yield depends on both environmental and genetic factors which can be difficult to
dissect as multiple replications of many genotypes are needed in diverse environmental …

Trustworthy deep learning for cyber-physical systems

T Gangopadhyay - 2022 - search.proquest.com
In cyber-physical systems, along with the accuracy of decision-making, interpretability
remains one of the important aspects to build user trust and generate domain insights …