[ML-news] CFP- Special Issue on Artificial Intelligence for Techniques and Methods on Disease Detection for Forest Vegetation
Francesca M
Please accept our apologies in case of multiple receptions.
Please send to interested colleagues and students.
*** Call for Papers ***
Special Issue on Artificial Intelligence for Techniques and Methods on Disease Detection for Forest Vegetation
Impact Factor: 3.282 (indexed on Scopus)
Link: https://www.mdpi.com/journal/forests/special_issues/HQZ16O804P
*** Important dates ***
Deadline for manuscript submissions: 27 September 2023
*** Aims and Scope ***
Forest ecosystems are increasingly affected by a variety of environmental disturbances, including biotic adversities, such as diseases and infestations caused by insects or fungi. As a result of human activities and climate change, forest ecosystems are increasingly prone to environmental and socio-economic damage.
The prediction and early diagnosis of disease outbreaks, as well as their control, are of fundamental importance for timely and efficient forest management in accordance with the principles of environmental sustainability, as envisaged by Sustainable Development Goal (SDG) 15 of the Agenda UN 2023.
In recent years, the development of information and communication technologies has favored applications of new management strategies and a methodology known as precision forestry to this sector. Precision forestry employs multiple data acquisition sources used for building decision support tools (DSSs), including multi- and hyperspectral satellite images, RGB images, airborne, unmanned aerial vehicles (UAV), proximal remote sensing, global positioning systems (Gnss—Global Navigation Satellite System), geographic information systems (Gis—Geographic Information System) and other geospatial devices.
The cross-analysis of the aforementioned data makes it possible to study the distribution areas in real time, allowing for strategies for optimizing the use of resources and predicting risk situations to be suggested and implemented, thus avoiding the need for unnecessary, unproductive and costly interventions. In this context, artificial intelligence (AI) has been integrated in the forestry sector in response to the needs that have emerged in recent decades, as AI models can discover and evaluate relationships, patterns and trends from data originating from multiple sources.
We are pleased to invite contributions of research articles and reviews to this Special Issue, “Artificial Intelligence for Techniques and Methods on Disease Detection for Forest Vegetation”.
Studies covering different ways of analyzing and modelling AI and methods for data acquisition using different sensors and platforms are welcome. Also of interest is any application for crop diseases and weed detection. Hence, we welcome all papers focused on the handling of large amounts of data collected over an entire growing season as well as the necessary software, including those on multisource data integration (e.g., weather time-series, RGB, multispectral, hyperspectral and thermal), multiscale approaches and large/ local scale monitoring in forests.
Original research articles and reviews are welcome.
Research areas may include (but are not limited to) the following:
- Artificial intelligence for disease and weed detection in forest vegetation;
- Decision support systems;
- Remote and proximal sensing;
- Open dataset for disease and weed detection in forests;
- Predictive analytics for disease and weed detection prediction, estimation or monitoring;
- Artificial-intelligence-based image-processing approaches.
Gianni Fenu
Department of Mathematics and Computer Science, University of Cagliari, Cagliari, Italy
Email: fe...@unica.it
Francesca Maridina Malloci
Department of Mathematics and Computer Science, University of Cagliari, Cagliari, Italy
Email: francesca...@unica.it