Deep learning for sustainable agriculture needs ecology and human involvement-Reference-Cited by-同舟云学术

Deep learning for sustainable agriculture needs ecology and human involvement

Published:2022-12-16 Issue:1 Volume:2 Page:40-44
ISSN:2767-035X
Container-title:Journal of Sustainable Agriculture and Environment
language:en
Short-container-title:J of Sust Agri & Env

Author:

Ryo Masahiro¹²,Schiller Josepha¹²,Stiller Stefan¹²,Rivera Palacio Juan Camilo¹²³,Mengsuwan Konlavach¹²,Safonova Anastasiia¹⁴⁵,Wei Yuqi¹⁶⁷

Affiliation:

1. Research Platform “Data Analysis & Simulation” Leibniz Centre for Agricultural Landscape Research (ZALF) Müncheberg Germany

2. Environment and Natural Sciences Brandenburg University of Technology Cottbus‐Senftenberg Cottbus Germany

3. Climate Action Program, Regional Office for Latin America Alliance of Bioversity International and International Center for Tropical Agriculture—Americas Hub Valle del Cauca Cali Columbia

4. Department of Information Systems Saint Petersburg Electrotechnical University “LETI” Saint Petersburg Russia

5. Department of Artificial Intelligence Systems Siberian Federal University Krasnoyarsk Russia

6. Department of Grassland Science, College of Grassland Science & Technology China Agricultural University Beijing China

7. Key Laboratory of Grasslands Management and Utilization Ministry of Agriculture and Rural Affairs Beijing China

Funder

Bundesministerium für Bildung und Forschung

Publisher

Wiley

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1002/sae2.12036

Reference18 articles.

1. bibliometrix : An R-tool for comprehensive science mapping analysis

2. Digital agriculture to design sustainable agricultural systems

3. Artificial intelligence solutions enabling sustainable agriculture: A bibliometric analysis

4. A Framework for Evaluating Land Use and Land Cover Classification Using Convolutional Neural Networks

5. Applications for deep learning in ecology

Cited by 6 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. Sustainable Agriculture-Based Climate Change Training Models using Remote Hyperspectral Image with Machine Learning Model;Remote Sensing in Earth Systems Sciences;2024-08-23

2. Improving spatial transferability of deep learning models for small-field crop yield prediction;ISPRS Open Journal of Photogrammetry and Remote Sensing;2024-04

3. Detection of artichoke on seedling based on YOLOV5 model;International Journal of Agriculture Environment and Food Sciences;2024-03-25

4. Comparative Analysis of Deep Learning Models for Olive Detection on the Branch;WSEAS TRANSACTIONS ON COMPUTERS;2024-02-27

5. Geographic-Scale Coffee Cherry Counting with Smartphones and Deep Learning;Plant Phenomics;2024-01