# An IoT Transfer Learning-Based Service for the Health Status Monitoring of Grapevine  
[PERSON]  
1 Institute for Bio-Economy and Agri-Technology (IBO), Centre for Research and Technology--Hellas (CERTH), 38333 Volos, Greece; [EMAIL_ADDRESS] (A.M.j. ; [EMAIL_ADDRESS] (K.D. ); [EMAIL_ADDRESS] (G.T. ); [EMAIL_ADDRESS] (D.B.) [PERSON]  
2 Laboratory of Agricultural Engineering, School of Agriculture, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece; [EMAIL_ADDRESS] 2  
[PERSON]  
1 Institute for Bio-Economy and Agri-Technology (IBO), Centre for Research and Technology--Hellas (CERTH), 3833 Volos, Greece; [EMAIL_ADDRESS] (A.M.j. ; [EMAIL_ADDRESS] (K.D. ); [EMAIL_ADDRESS] (G.T. ); [EMAIL_ADDRESS] (D.B.) [PERSON]  
2 Laboratory of Agricultural Engineering, School of Agriculture, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece; [EMAIL_ADDRESS] 2  
[PERSON]  
1 Institute for Bio-Economy and Agri-Technology (IBO), Centre for Research and Technology--Hellas (CERTH), 3833 Volos, Greece; [EMAIL_ADDRESS] (A.M.j. ; [EMAIL_ADDRESS] (K.D. ); [EMAIL_ADDRESS] (G.T. ); [EMAIL_ADDRESS] (D.B.) [PERSON]  
3 Interuniversity Department of Regional and Urban Studies and Planning, Polytechnic and University of Turin, Viale Mattitiol 39, 10125 Torino, Italy; [EMAIL_ADDRESS] 3  
[PERSON]  
4 CERER, 40000-00  
diseases can affect the plant leaves or the trunk, or they can directly affect the grapes. They have the potential to cause significant damage, resulting in huge yield losses or severely inferior product quality [12]. Traditional methods to determine the pathogen infecting the grapevine rely on visual inspection by trained experts and, for some pathogens, further evaluation by laboratory techniques may be necessary. The aforementioned techniques are destructive, time consuming, and, in the case of the visual inspection, may not always be accurate [13]. Moreover, infections are commonly addressed with the use of chemical compounds for preventive reasons or curable cases, or with destruction of crops to avoid further spread. This method, however, results in significant losses in crop yield, decrease in farmer's income, and ecological contamination [14; 15; 16], without ensuring fewer losses [17]. In recent years, phytopathology and crop protection have been enhanced with the use of technologies such as robotics, sensing technologies, and artificial intelligence. These novel agro-technology methods belong to the field of precision agriculture (PA) and have helped minimise agricultural waste and maximise productivity [18; 19; 20; 21]. To detect grapevine diseases, several studies have employed spectral sensing techniques, such as remote sensing that uses a multispectral camera on unmanned aerial vehicles (UAVs) [22; 23], proximal sensing using thermal imaging [24; 25] or hyperspectral sensing [26; 27; 28]. Although the use of the aforementioned equipment gives accurate results even at a pre-symptomatic level, they must be used by experienced scientific personnel, and they are also expensive. For this reason, and with the rise of deep learning (DL), the use of simpler input signals, such as RGB images, has drawn scientific attention to disease detection [29; 30]. Deep learning is a subcategory of machine learning and, as the name suggests, it is a method that employs a neural network with a large number of layers [31]. Thanks to convolutional neural networks (CNNs), DL has gained popularity in the field of computer vision, especially for classification problems [32; 33; 34], and has become a popular approach for plant disease identification [35; 36; 37; 38]. Specifically for the detection of grapevine diseases, DL has been used to detect downy mildew and spider mite; this has been completed using RGB photos in [PERSON] et al. (2021) [39] or esca in [PERSON] et al. (2021) [40]. Transfer learning (TL) is a DL approach that combats problems of limited data by training a pre-trained model that has proven its efficiency in classification problem using pre-existing datasets, such as those from online libraries [41].