@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},
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{Moysiadis2021,
title = {Smart Farming in Europe},
author = { V. Moysiadis and P. Sarigiannidis and V. Vitsas and A. Khelifi},
doi = {10.1016/j.cosrev.2020.100345},
year = {2021},
date = {2021-01-01},
journal = {Computer Science Review},
abstract = {Smart Farming is the new term in the agriculture sector, aiming to transform the traditional techniques to innovative solutions based on Information Communication Technologies (ICT). Concretely, technologies like Unmanned Aerial Vehicles (UAVs), Unmanned Ground Vehicles (UGVs), Image Processing, Machine Learning, Big Data, Cloud Computing, and Wireless Sensor Networks (WSNs), are expected to bring significant changes in this area. Expected benefits are the increase in production, the decrease in cost by reducing the inputs needed such as fuel, fertilizer and pesticides, the reduction in labor efforts, and finally improvement in the quality of the final products. Such innovative methods are crucial in recent days, due to the exponential increase of the global population, the importance of producing healthier products grown with as much fewer pesticides, where public opinion of European citizens is sensitized. Moreover, due to the globalization of the world economy, European countries face the low cost of production of other low-income countries. In this vein, Europe tries to evolve its agriculture domain using technology, aiming at the sustainability of its agricultural sector. Although many surveys exist, most of them tackle in a specific scientific area of Smart Farming. An overview of Smart Farming covering all the involved technologies and providing an extensive reference of good practices around Europe is essential. Our expectation from our work is to become a good reference for researchers and help them with their future work. This paper aims to provide a comprehensive reference for European research efforts in Smart Farming and is two-fold. First, we present the research efforts from researchers in Smart Farming, who apply innovative technology trends in various crops around Europe. Second, we provide and analyze the most significant projects in Europe in the area of Smart Farming. © 2021 Institute of Electrical and Electronics Engineers Inc.. All rights reserved.},
keywords = {Big data, Cloud Computing, Image Processing, Machine learning, Smart farming, Unmanned Aerial Vehicles (UAVs), Unmanned Ground Vehicles (UGVs), Wireless Sensor Networks (WSNs)},
pubstate = {published},
tppubtype = {article}
}

@article{Kakamoukas2020,
title = {FANETs in agriculture A routing protocol survey},
author = { G.A. Kakamoukas and P.G. Sarigiannidis and A.A. Economides},
doi = {10.1016/j.iot.2020.100183},
year = {2020},
date = {2020-01-01},
journal = {Internet Things},
abstract = {Breakthrough advances on communication technology, electronics and sensors have led to integrated commercialized products ready to be deployed in several domains. Agriculture is and has always been a domain that adopts state of the art technologies in time, in order to optimize productivity, cost, convenience, and environmental protection. The deployment of Unmanned Aerial Vehicles (UAVs) in agriculture constitutes a recent example. A timely topic in UAV deployment is the transition from a single UAV system to a multi-UAV system. Collaboration and coordination of multiple UAVs can build a system that far exceeds the capabilities of a single UAV. However, one of the most important design problems multi-UAV systems face is choosing the right routing protocol which is prerequisite for the cooperation and collaboration among UAVs. In this study, an extensive review of Flying Ad-hoc network (FANET) routing protocols is performed, where their different strategies and routing techniques are thoroughly described. A classification of UAV deployment in agriculture is conducted resulting in six (6) different applications: Crop Scouting, Crop Surveying and Mapping, Crop Insurance, Cultivation Planning and Management, Application of Chemicals,and Geofencing. Finally, a theoretical analysis is performed that suggests which routing protocol can serve better each agriculture application, depending on the mobility models and the agricultural-specific application requirements.},
keywords = {Flyingadhoc networks (FANETs), Mobility models, Precision agriculture, Routing protocols, Smart farming, Unmanned Aerial Vehicles (UAVs)},
pubstate = {published},
tppubtype = {article}
}

@article{Lytos2020,
title = {Towards smart farming: Systems, frameworks and exploitation of multiple sources},
author = { A. Lytos and T. Lagkas and P. Sarigiannidis and M. Zervakis and G. Livanos},
doi = {10.1016/j.comnet.2020.107147},
year = {2020},
date = {2020-01-01},
journal = {Computer Networks},
abstract = {Agriculture is by its nature a complicated scientific field, related to a wide range of expertise, skills, methods and processes which can be effectively supported by computerized systems. There have been many efforts towards the establishment of an automated agriculture framework, capable to control both the incoming data and the corresponding processes. The recent advances in the Information and Communication Technologies (ICT) domain have the capability to collect, process and analyze data from different sources while materializing the concept of agriculture intelligence. The thriving environment for the implementation of different agriculture systems is justified by a series of technologies that offer the prospect of improving agricultural productivity through the intensive use of data. The concept of big data in agriculture is not exclusively related to big volume, but also on the variety and velocity of the collected data. Big data is a key concept for the future development of agriculture as it offers unprecedented capabilities and it enables various tools and services capable to change its current status. This survey paper covers the state-of-the-art agriculture systems and big data architectures both in research and commercial status in an effort to bridge the knowledge gap between agriculture systems and exploitation of big data. The first part of the paper is devoted to the exploration of the existing agriculture systems, providing the necessary background information for their evolution until they have reached the current status, able to support different platforms and handle multiple sources of information. The second part of the survey is focused on the exploitation of multiple sources of information, providing information for both the nature of the data and the combination of different sources of data in order to explore the full potential of ICT systems in agriculture. © 2020 The Authors},
keywords = {Agriculture, Big data, Internet of things, Machine learning, Smart farming},
pubstate = {published},
tppubtype = {article}
}