Machine Learning and Remote Sensing for Precision Agriculture
摘要截稿:
全文截稿: 2024-12-31
影响因子: 3.726
期刊难度:
CCF分类: 无
中科院JCR分区:
• 大类 : 农林科学 - 1区
• 小类 : 农艺学 - 2区
Overview
As world population is expected to reach 9.7 billion by 2050, we are facing a great challenge to produce enough food with limited land resources under climate change without degrading our environment. Precision agriculture has the potential to make significant contributions to global food security, sustainable development, and climate change mitigation. Precision agriculture is data intensive. Proximal and remote sensing has been used for crop monitoring, nutrient level prediction, irrigation management, disease, and pest management to increase crop productivity, profitability, and sustainability. Unmanned Aerial Vehicles (UAV) remote sensing technologies provide new opportunities to timely acquire high resolution data for efficient crop monitoring, while application of satellite data opens for standardized and scaled-up solutions. Remote sensing data is coupled with large volume of other data describing soil, landscape, weather, management, and crop yield. Machine learning, including deep learning, has been increasingly used to better analyze the “Big Data” to support precision agriculture decisions.
Despite many beneficial trends, the combination of remote sensing with machine (deep) learning techniques in precision agriculture holds certain limitations. Challenges include high cost of system implementation, technical issues, network failures, privacy threat, data vulnerability issues, and storage problems, etc. The future research scope should consider solutions to the abovementioned limitations. Researchers and practitioners are invited to present cutting-edge research, review, and short technical communication articles on the advances in the intersection of machine (deep) learning, remote sensing and precision agriculture for enhanced crop productivity, profitability, sustainability, adaption to and mitigation of climate change.
Potential topics of the virtual special issue include, but are not limited to the following:
Emerging trends and applications of remote sensing technology for precision agriculture
Applications of machine (deep) learning and remote sensing for precision crop management
Recent trends and perspectives of remote sensing in building smart agriculture
Limitations and drawbacks of remote sensing in precision agriculture and innovative solutions
Implementation of machine (deep) learning and remote sensing technologies in precision agriculture
Machine (deep) learning in smart agriculture or precision agriculture: Pros and Cons
Frontiers, challenges and future perspectives of implementing machine (deep) learning and/or remote sensing technologies in precision agriculture
Insights in machine (deep) learning and remote sensing trends in precision agriculture
Spatial analysis and zoning of within-field and on-farm variability
Innovative soil and crop sensing technologies for precision crop management
Efficient sampling and geospatial analysis for precision crop management
Wireless sensor networks, Internet of Things, and Big Data in precision agriculture
Precision soil mapping, monitoring, and health management.
Variable rate tillage, seeding, nutrient/pest/disease/weed management, irrigation, and harvesting.
Climate change impact, adaptation, and mitigation
Precision conservation and biodiversity
On-farm precision agriculture experimentation and research
Integrated precision agricultural systems
Integration of crop growth modeling, remote sensing, machine (deep) learning for precision agriculture
Integration of precision agriculture and precision conservation
Decision support systems for precision agriculture
Guest editors:
Dr. Yuxin MiaoDirector, Precision Agriculture CenterDepartment of Soil, Water and Climate, University of Minnesota, St. Paul, MN, USAymiao@umn.edu
Dr. Alireza SharifiDepartment of Geomatics and Surveying Engineering, Faculty of Civil Engineering, Shahid Rajaee Teacher Training University, Tehran, Irana_sharifi@sru.ac.ir
Dr. Krzysztof KusnierekDepartment of Agriculture Technology, Center for Precision Agriculture, Norwegian Institute of Bioeconomy Research, Kapp, Norwaykrzysztof.kusnierek@nibio.no
Manuscript submission information:
Submission deadline: 31 December 2024