Special Issue "Crop Disease Detection Using Remote Sensing Image Analysis"
Deadline for manuscript submissions: 15 January 2022.
Interests: remote sensing for automated detection and mapping of crop enemies and threat situations (weeds, fungi, viruses, and insects); remote sensing for the detection, recognition, and mapping of nutrient stresses in crops; food safety sensing; post-harvest quality control; data fusion, hyperspectral, multispectral, fluorescence, fluorescence kinetics, computer vision, thermal, lidar, and multisensor systems for crop status sensing and phenotyping; traceability systems in the agricultural field through employing new technologies (RFID, barcode, GPS, wearable computers, etc.), self-organization, deep learning for image-based plant diseases, data mining and computational intelligence for crop monitoring; cyberphysical systems in the Internet of Things; information and data fusion; cognitive robotics and active learning systems; sensor-based environment awareness; visualization mapping for plant disease detection
Climate change and climate variability impact requires strategic innovations for timely and accurate plant disease assessment. Crop condition monitoring has a significant impact on disease control, limiting the tremendous effect to agricultural production, degrading yield and quality and consequently leading to severe financial loss for farmers. Conventional disease control is often based on the hypothesis that pathogenic factors are propagated uniformly over cultivated fields. Precision farming tools oriented to disease propagation assessment and location-dependent management are capable of leading to a lower environmental footprint yielded through lower pesticide application and relevant financial losses. Remote-sensing-based technologies have proven more effective compared to conventional ones on occasions where iterative large-scale measurements are needed as the only sole method for data acquisition. Innovative imaging sensor tools are capable of improving spatial and spectral resolution accuracies that enable not only the assessment of foliar symptoms (image, texture, and spectral sensors) and spatial disease manifestation, but also the evaluation of early detection approaches, aiming to detect changes in leaf optical behavior due to infection occurrence, which are not yet perceived by the human vision system (hyperspectral images). Recently, different approaches that are oriented to disease monitoring and detection through employing optical sensors fitted on a variety of platforms have been demonstrated, including portable solutions to satellite, aircraft, and unmanned aerial vehicles (UAVs) for efficient crop monitoring. Simultaneously, noticeable progress in the Artificial Intelligence (AI) field enables successful supervised and unsupervised image analysis based on deep learning methods to enhance the performance of crop health monitoring. This Special Issue aims to gather relevant research work of novel applications that employ remote sensing techniques for plant disease detection.
Dr. Xanthoula Eirini Pantazi
Manuscript Submission Information
Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.
Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Remote Sensing is an international peer-reviewed open access semimonthly journal published by MDPI.
Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.
- Field spectroscopy
- Crop health status
- Precision agriculture
- Deep learning
- Data mining
- Hyperspectral sensors
- Sensor fusion
- Data fusion
- Multispectral sensors
- Machine learning
- Artificial Intelligence
The below list represents only planned manuscripts. Some of these manuscripts have not been received by the Editorial Office yet. Papers submitted to MDPI journals are subject to peer-review.
Title: Screening defense responses in tomato, triggered by encapsulated biological control agents and organic defense inducers, with the use of Self Organizing Maps
Authors: Xanthoula Eirini Pantazi
Affiliation: Faculty of Agriculture, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece
Abstract: The current study demonstrates a novel Self-Organizing Maps approach for the diagnosis of induction of systemic resistance in tomato plants. Through the proposed method, the expression of the systemic resistance in tomato is verified by modeling Self-Organizing Maps (SOMs) in which kinetic fluorescence data is used as input. With the aid of SOMs, an intelligent system is created, capable of distinguishing those fluorescence parameters that are more closely related to the formation of characteristic clusters. Due to the fact that the clusters will be directly related to the induction of resistance by the applied biological control agents, and defense inducers, the specific parameters, out of the 27 measured by the fluorometer, have reflected changes in physiology related to the development of systemic resistance in tomato plants. The combined use of Fluorescence Kinetics with SOMs and the Nanoparticle inoculation method adds new dynamics in the field of Plant Protection and Precision Agriculture. The SOMs predict with high accuracy the expression of resistance in tomato plants taking advantage of the fluorescence kinetic features.