@article {164,
	title = {A Multispectral Dataset for the Detection of Tuta absoluta and Leveillula taurica in Tomato Plants},
	journal = {Smart Agricultural Technology},
	year = {2022},
	pages = {100146},
	abstract = {Tomato (Solanum lycopersicum) is one of the most important vegetables for human nutrition and its cultivation employs amounts of resources worldwide. However, tomato cultivation is plagued by several diseases and pests that increase production cost and introduce additional environmental and health risks due to pesticide use. Timely disease and pest detection is of high importance for tomato crop output and the environment, since plant protection input can be optimized. Here, we present a dataset of multispectral images (RGB and NIR) of tomato plants, at various stages of infection with Tuta absoluta and Leveillula taurica, which to our knowledge is unique. The dataset comprised of 263 images collected from a real greenhouse. Additionally, we applied a baseline Faster-RCNN object detector for the localization and classification lesions. Our experiments include (i) a version for the RGB channels and (ii) a custom backbone architecture version for feature fusion using the same Faster-RCNN head. Lastly, based on the detector{\textquoteright}s output, we compute an \&gt;0.9 F1-score on binary classification, while mAP 18.5\% and mAP 20.2\% on detection, highlight the added value of NIR spectral bands for detecting these diseases.},
	keywords = {Faster-RCNN, Feature fusion, Leaf Miner, Plant Disease Dataset, Powdery Mildew},
	issn = {2772-3755},
	doi = {https://doi.org/10.1016/j.atech.2022.100146},
	url = {https://www.sciencedirect.com/science/article/pii/S2772375522001101},
	author = {P.S. Georgantopoulos and D. Papadimitriou and C. Constantinopoulos and T. Manios and I.N. Daliakopoulos and D. Kosmopoulos}
}
@proceedings {147,
	title = {A Method for Recovering Near Infrared Information from RGB Measurements with Application in Precision Agriculture},
	journal = {European Signal Processing Conference},
	year = {2021},
	pages = {616-620},
	publisher = {IEEE},
	edition = {IEEE},
	abstract = {[size= 13.008px]In this work we develop a cost-efficient coupled dictionary learning based method for reconstructing multispectral images using only a single RGB commercial camera, without requiring the sensitivity function of the camera sensor. Considering the very high cost, the acquisition time and reduced mobility of multispectral cameras we claim that this is a very attractive option. In contrast to other approaches, the proposed method is not limited only to spectral bands inside the visible spectrum, but it also considers an even more challenging task, that is the reconstruction of spectral bands outside the visible range closer to the near-infrared wavelengths of the spectrum. Extensive experiments with real data demonstrate the effectiveness and applicability of the proposed method in the precision agriculture domain. To this end, we calculate one of the most widely used vegetation indices, the normalized difference vegetation index (NVDI), which may be used for plant health monitoring.[/size]},
	author = {A. Gkillas and D. Kosmopoulos and C. Constantinopoulos and D. Ampeliotis and K. Berberidis}
}
@proceedings {84,
	title = {A method for online analysis of structured processes using bayesian filters and echo state networks},
	journal = {Proceedings of the 12th European Conference on Computer Vision - Volume Part III},
	year = {2012},
	pages = {71-80},
	publisher = {Springer-Verlag},
	address = {Florence - Italy},
	isbn = {978-3-642-33884-7},
	author = {Kosmopoulos, Dimitrios I. and Makedon, Fillia}
}
@article {26,
	title = {Multimodal and ontology-based fusion approaches of audio and visual processing for violence detection in movies},
	journal = {Expert Systems with Applications},
	volume = {38},
	year = {2011},
	pages = {14102 - 14116},
	keywords = {Knowledge representation, Learning, Movie, Multimodal fusion, Ontology, Reasoning, Violence},
	issn = {0957-4174},
	doi = {10.1016/j.eswa.2011.04.219},
	author = {Thanassis Perperis and Theodoros Giannakopoulos and Alexandros Makris and D. Kosmopoulos and Sofia Tsekeridou and Stavros J. Perantonis and Sergios Theodoridis}
}
@article {39,
	title = {Multiview Behavior Monitoring for Assistive Environments},
	journal = {Universal Access in the Information Society},
	volume = {10},
	year = {2011},
	pages = {115-123},
	author = {D. Kosmopoulos}
}
@article {30,
	title = {Multiclass defect detection and classification in weld radiographic images using geometric and texture features},
	journal = {Expert Systems with Applications},
	volume = {37},
	year = {2010},
	pages = {7606 - 7614},
	keywords = {Classification, Defects, Geometrical features, Radiography, Segmentation, Texture, Welds},
	issn = {0957-4174},
	doi = {10.1016/j.eswa.2010.04.082},
	author = {Ioannis Valavanis and Dimitrios Kosmopoulos}
}
@article {25,
	title = {MD-SIR: a methodology for developing sensor{\textemdash}guided industry robots},
	journal = {Robotics and Computer-Integrated Manufacturing},
	volume = {18},
	year = {2002},
	pages = {403 - 419},
	keywords = {Application development, Control objects library, Open robots controller architecture, Sensor integration},
	issn = {0736-5845},
	doi = {10.1016/S0736-5845(02)00031-5},
	author = {D. Kosmopoulos and Varvarigou, T.A. and D.M Emiris and A.A Kostas}
}
