Precision Plant Pathology: A New Approach to the Study of Epidemiology and Diagnosis of Plant Diseases
A special issue of Agriculture (ISSN 2077-0472). This special issue belongs to the section "Digital Agriculture".
Deadline for manuscript submissions: closed (10 April 2024) | Viewed by 2174
Special Issue Editors
2. Department of Agroforestry Sciences, ETSI University of Huelva, 21007 Huelva, Spain
Interests: integrated crop management; olive; verticillium; UAV; hyperspectral sensors; machine learning
Interests: olive; etiology; pathogenesis, epidemiology; control; Verticillium wilt
Interests: deep learning; open-source software; proximal sensing; logistic regression; multilayer perceptron; uncooled thermal sensor; precision agriculture; thermal orthomosaic
Special Issue Information
Dear Colleagues,
The abundance of information available currently, as well as the ease of generating new information at a reasonable cost, is crucial for current digitization processes. New satellite constellations (Sentinel), cloud computing, low-cost sensors, Internet of Things, big data, and "machine learning" and artificial intelligence are expected to be fundamental in various disciplines of plant pathology and the decision making that will drive integrated pest management in the coming years. A pathosystem is represented by the "disease triangle"; that is, disease requires the interaction of a susceptible host, a virulent pathogen, and a favorable environment. Under these conditions, an epidemic may occur, defined as an increase in the disease over time, generating a multitude of available data. In this context, "Precision plant pathology" is a set of techniques aimed at optimizing the management of diseases based on the quantification of their spatial and temporal variability. These techniques seek to reduce costs and improve production and sustainability by creating risk prediction algorithms and models for the main diseases and adapting them to specific conditions. The four following areas of research are proposed: (1) visualization and statistical analysis of disease data using R and Python; (2) disease modeling using machine learning techniques and fuzzy logic; (3) automatic plant disease diagnosis using deep learning; and (4) remote and proximal sensing for early plant disease detection.
Dr. Antonio Santos-Rufo
Prof. Dr. Francisco Javier López-Escudero
Dr. Fernando Pérez Porras
Guest Editors
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Keywords
- R
- Python
- cloud computing
- precision agriculture
- remote sensing
- smart sensors
- multispectral, hyperspectral and thermographic sensors
- vegetation index, NDVI
- artificial intelligence
- deep learning
- fuzzy logic
- prescription map
- UAV/drone
- big data
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