@article{article,

title = {Detection and Characterization of Stressed Sweet Cherry Tissues Using Machine Learning},

author = { C. Chaschatzis and C. Karaiskou and E. Mouratidis and E. Karagiannis and P. Sarigiannidis},

url = {https://www.researchgate.net/publication/357257849_Detection_and_Characterization_of_Stressed_Sweet_Cherry_Tissues_Using_Machine_Learning},

doi = {10.3390/drones6010003},

year  = {2021},

date = {2021-12-22},

journal = {Drones},

volume = {6},

pages = {3},

abstract = {Recent technological developments in the primary sector and machine learning algorithms allow the combined application of many promising solutions in precision agriculture. For example, the YOLOv5 (You Only Look Once) and ResNet Deep Learning architecture provide high-precision real-time identifications of objects. The advent of datasets from different perspectives provides multiple benefits, such as spheric view of objects, increased information, and inference results from multiple objects detection per image. However, it also raises crucial obstacles such as total identifications (ground truths) and processing concerns that can lead to devastating consequences, including false-positive detections with other erroneous conclusions or even the inability to extract results. This paper introduces experimental results from the machine learning algorithm (Yolov5) on a novel dataset based on perennial fruit crops, such as sweet cherries, aiming to enhance precision agriculture resiliency. Detection is oriented on two points of interest: (a) Infected leaves and (b) Infected branches. It is noteworthy that infected leaves or branches indicate stress, which may be due to either a stress/disease (e.g., Armillaria for sweet cherries trees, etc.) or other factors (e.g., water shortage, etc). Correspondingly, the foliage of a tree shows symptoms, while this indicates the stages of the disease.},

keywords = {diseases detection, machine learning, precision agriculture, ResNet, smart farming, stress detection, sweet cherries trees, Yolov5},

pubstate = {published},

tppubtype = {article}

}

@article{Boursianis2020,

title = {Smart Irrigation System for Precision Agriculture - The AREThOU5A IoT Platform},

author = { A. D. Boursianis and M. S. Papadopoulou and A. Gotsis and S. Wan and P. Sarigiannidis and S. Nikolaidis and S. K. Goudos},

url = {Smart Irrigation System for Precision Agriculture - The AREThOU5A IoT Platform},

doi = {10.1109/jsen.2020.3033526},

year  = {2020},

date = {2020-01-01},

journal = {IEEE Sensors Journal},

pages = {1--1},

publisher = {Institute of Electrical and Electronics Engineers (IEEE)},

abstract = {Agriculture 4.0, as the future of farming technology, includes several key enabling technologies towards sustainable agriculture. The use of state-of-the-art technologies, such as the Internet of Things, transform traditional cultivation practices, like irrigation, to modern solutions of precision agriculture. In this paper, we present in detail the subsystems and the architecture of an intelligent irrigation system for precision agriculture, the AREThOU5A IoT platform. We describe the operation of the IoT node that is utilized in the platform. Moreover, we apply the radiofrequency energy harvesting technique to the presented IoT platform, as an alternative technique to deliver power to the IoT node of the platform. To this end, we fabricate and validate a rectenna module for radiofrequency energy harvesting. Experimental results of the fabricated rectenna exhibit a satisfactory performance as a harvester of ambient sources in an outdoor environment. IEEE},

keywords = {Intelligent sensors, IoT technology, Irrigation, precision agriculture, Radio frequency, radio frequency energy harvesting, smart irrigation, Wireless communication, wireless sensor networks},

pubstate = {published},

tppubtype = {article}

}

@article{Tsouros2019b,

title = {A review on UAV-based applications for precision agriculture},

author = { D.C. Tsouros and S. Bibi and P.G. Sarigiannidis},

url = {https://www.researchgate.net/publication/337187714_A_Review_on_UAV-Based_Applications_for_Precision_Agriculture},

doi = {10.3390/info10110349},

year  = {2019},

date = {2019-01-01},

journal = {Information (Switzerland)},

volume = {10},

number = {11},

abstract = {Emerging technologies such as Internet of Things (IoT) can provide significant potential in Smart Farming and Precision Agriculture applications, enabling the acquisition of real-time environmental data. IoT devices such as Unmanned Aerial Vehicles (UAVs) can be exploited in a variety of applications related to crops management, by capturing high spatial and temporal resolution images. These technologies are expected to revolutionize agriculture, enabling decision-making in days instead of weeks, promising significant reduction in cost and increase in the yield. Such decisions enable the effective application of farm inputs, supporting the four pillars of precision agriculture, i.e., apply the right practice, at the right place, at the right time and with the right quantity. However, the actual proliferation and exploitation of UAVs in Smart Farming has not been as robust as expected mainly due to the challenges confronted when selecting and deploying the relevant technologies, including the data acquisition and image processing methods. The main problem is that still there is no standardized workflow for the use of UAVs in such applications, as it is a relatively new area. In this article, we review the most recent applications of UAVs for Precision Agriculture. We discuss the most common applications, the types of UAVs exploited and then we focus on the data acquisition methods and technologies, appointing the benefits and drawbacks of each one. We also point out the most popular processing methods of aerial imagery and discuss the outcomes of each method and the potential applications of each one in the farming operations. © 2019 by the authors.},

keywords = {Image Processing, IoT, precision agriculture, remote sensing, Review, smart farming, UAS, UAV, Unmanned Aerial System, Unmanned Aerial Vehicle},

pubstate = {published},

tppubtype = {article}

}

@article{Triantafyllou2019b,

title = {Precision agriculture: A remote sensing monitoring system architecture},

author = { A. Triantafyllou and P. Sarigiannidis and S. Bibi},

url = {https://www.researchgate.net/publication/337192880_Precision_Agriculture_A_Remote_Sensing_Monitoring_System_Architecture},

doi = {10.3390/info10110348},

year  = {2019},

date = {2019-01-01},

journal = {Information (Switzerland)},

volume = {10},

number = {11},

abstract = {Smart Farming is a development that emphasizes on the use of modern technologies in the cyber-physical field management cycle. Technologies such as the Internet of Things (IoT) and Cloud Computing have accelerated the digital transformation of the conventional agricultural practices promising increased production rate and product quality. The adoption of smart farming though is hampered because of the lack of models providing guidance to practitioners regarding the necessary components that constitute IoT-based monitoring systems. To guide the process of designing and implementing Smart farming monitoring systems, in this paper we propose a generic reference architecture model, taking also into consideration a very important non-functional requirement, the energy consumption restriction. Moreover, we present and discuss the technologies that incorporate the seven layers of the architecture model that are the Sensor Layer, the Link Layer, the Encapsulation Layer, the Middleware Layer, the Configuration Layer, the Management Layer and the Application Layer. Furthermore, the proposed Reference Architecture model is exemplified in a real-world application for surveying Saffron agriculture in Kozani, Greece. © 2019 by the authors.},

keywords = {Cloud Computing, Communication technologies, Internet of things, precision agriculture, smart farming, wireless sensor networks},

pubstate = {published},

tppubtype = {article}

}