Can machine learning classification methods improve the prediction of leaf wetness in North-Western Europe compared to established empirical methods?-Reference-Cited by-同舟云学术

Can machine learning classification methods improve the prediction of leaf wetness in North-Western Europe compared to established empirical methods?

Published:2021-11 Issue: Volume:182 Page:115255
ISSN:0957-4174
Container-title:Expert Systems with Applications
language:en
Short-container-title:Expert Systems with Applications

Author:

Gillespie Gary D.,McDonnell Kevin P.,O'Hare Gregory M.P.

Funder

Science Foundation Ireland

Publisher

Elsevier BV

Subject

Artificial Intelligence,Computer Science Applications,General Engineering

Reference64 articles.

1. Pesticides deposition in vineyards on different conditions of leaf wetness;Saab;Journal of the Brazilian Association of Agricultural Engineering,2017

2. Crop Evapotranspiration - Guidelines for computing crop water requirements - FAO Irrigation and Drainage paper 56;Allen,1998

3. Introduction to Machine Learning (T. Dietterich Ed;Alpaydin,2010

4. Leaf Wetness Duration Modelling using Adaptive Neuro Fuzzy Inference System;Amir,2016

5. Deep learning for supervised classification of spatial epidemics;Augusta;Spatial and Spatio-temporal Epidemiology,2019

Cited by 5 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Artificial intelligence and machine learning in environmental impact prediction for soil pollution management – case for EIA process;Environmental Advances;2024-10

2. A novel fusion technology utilizing complex network and sequence information for FAD-binding site identification;Analytical Biochemistry;2024-01

3. Iot Based Remote Operated Smart Irrigation Systemfor Enhancing The Crop Yield;2023 IEEE 5th International Conference on Cybernetics, Cognition and Machine Learning Applications (ICCCMLA);2023-10-07

4. On removing conflicts for machine learning;Expert Systems with Applications;2022-11

5. Evaluation of a multi-model approach to estimate leaf wetness duration: an essential input for disease alert systems;Theoretical and Applied Climatology;2022-04-03