English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Precise crop yield prediction is essential for improving agriculturalpractices and ensuring crop resilience in varying climates. Integrating weatherdata across the growing season, especially for different crop varieties, iscrucial for understanding their adaptability in the face of climate change. Inthe MLCAS2021 Crop Yield Prediction Challenge, we utilized a dataset comprising93,028 training records to forecast yields for 10,337 test records, covering159 locations across 28 U.S. states and Canadian provinces over 13 years(2003-2015). This dataset included details on 5,838 distinct genotypes anddaily weather data for a 214-day growing season, enabling comprehensiveanalysis. As one of the winning teams, we developed two novel convolutionalneural network (CNN) architectures: the CNN-DNN model, combining CNN andfully-connected networks, and the CNN-LSTM-DNN model, with an added LSTM layerfor weather variables. Leveraging the Generalized Ensemble Method (GEM), wedetermined optimal model weights, resulting in superior performance compared tobaseline models. The GEM model achieved lower RMSE (5.55% to 39.88%), reducedMAE (5.34% to 43.76%), and higher correlation coefficients (1.1% to 10.79%)when evaluated on test data. We applied the CNN-DNN model to identifytop-performing genotypes for various locations and weather conditions, aidinggenotype selection based on weather variables. Our data-driven approach isvaluable for scenarios with limited testing years. Additionally, a featureimportance analysis using RMSE change highlighted the significance of location,MG, year, and genotype, along with the importance of weather variables MDNI andAP.

A Hybrid Deep Learning-based Approach for Optimal Genotype by Environment Selection