Optimizing Artificial Neural Network-Based Models to Predict Rice Blast Epidemics in Korea

Predicting rice blast disease: machine learning versus process-based models

(2019) David F. Nettleton et al.   BMC BIOINFORMATICS

Long Short-Term Memory Networks for Accurate State-of-Charge Estimation of Li-ion Batteries

(2018) Ephrem Chemali et al.   IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS

Early Forecasting of Rice Blast Disease Using Long Short-Term Memory Recurrent Neural Networks

(2017) Yangseon Kim et al.   Sustainability

Predicting potential epidemics of rice leaf blast and sheath blight in South Korea under the RCP 4.5 and RCP 8.5 climate change scenarios using a rice disease epidemiology model, EPIRICE

(2015) Kwang-Hyung Kim et al.   AGRICULTURAL AND FOREST METEOROLOGY

Characterization of rice blast resistance genes in rice germplasm with monogenic lines and pathogenicity assays

(2015) J.C. Wang et al.   CROP PROTECTION

Potential strategies and future requirements for plant disease management under a changing climate

(2011) P. Juroszek et al.   PLANT PATHOLOGY