2020 IEEE International Workshop on Metrology for Agriculture and Forestry (MetroAgriFor)最新文献

Enabling Context Aware Tuning of Low Power Sensors for Smart Agriculture 为智能农业实现低功耗传感器的上下文感知调谐

2020 IEEE International Workshop on Metrology for Agriculture and Forestry (MetroAgriFor) Pub Date : 2020-11-04 DOI: 10.1109/MetroAgriFor50201.2020.9277635

S. Sindaco, S. Nanni, Cristiano Aguzzi, L. Roffia, Tullio Salmon Cinotti

引用次数: 0

Simply Time Domain Reflectometry system for food analysis 用于食品分析的简单时域反射系统

2020 IEEE International Workshop on Metrology for Agriculture and Forestry (MetroAgriFor) Pub Date : 2020-11-04 DOI: 10.1109/MetroAgriFor50201.2020.9277605

Eleonora Iaccheri, A. Berardinelli, L. Ragni

引用次数: 1

In-situ detection of apple fruit using a 2D LiDAR laser scanner 利用二维激光雷达激光扫描仪对苹果果实进行原位检测

2020 IEEE International Workshop on Metrology for Agriculture and Forestry (MetroAgriFor) Pub Date : 2020-11-04 DOI: 10.1109/MetroAgriFor50201.2020.9277629

Nikos Tsoulias, G. Xanthopoulos, S. Fountas, M. Zude

{"title":"In-situ detection of apple fruit using a 2D LiDAR laser scanner","authors":"Nikos Tsoulias, G. Xanthopoulos, S. Fountas, M. Zude","doi":"10.1109/MetroAgriFor50201.2020.9277629","DOIUrl":"https://doi.org/10.1109/MetroAgriFor50201.2020.9277629","url":null,"abstract":"The development of reliable fruit detection and localization systems has been approached with remote sensing methods aimed at optimizing the orchard management to obtain high crop quality and economically improved harvesting practice. This work presents a new technique that uses a light detection and ranging (LiDAR) laser scanner to detect and localize apple fruits in the orchard. In a 1 ha apple orchard (Malus x domestica 'Gala') two trees were defoliated before harvest period. A LiDAR scanner emitting at 905 nm, with a real time kinematic global navigation satellite system to geo-reference the data and an inertial measurement unit to acquire orientation data were mounted on a tractor (0.2 km/h) to produce the 3D tree point cloud before and after defoliation. Subsequently, the apples of each tree were harvested and classified in four size classes according to height (Hmanual) and diameter (Dmanual).An intensity analysis of tree elements was performed, obtaining mean intensity values of 28.9%, 29.1%, and 44.3% for leaves, branches and trunks, and apples, respectively. These results suggested that the intensity parameter can be useful to detect apples. A four-step fruit detection algorithm was developed to localize and estimate the height (HLiDAR) and diameter (DLiDAR) of fruits. A mean detection success of 92.5% was obtained in relation to the total amount of fruits on the defoliated trees during the stages of fruit development. A mean correlation of R2 = 0.83 was obtained for Hmanual and HLiDAR, whereas a less pronounced relation was observed between DLiDAR and Dmanual (R2 = 0.62) during fruit development. The mean detection success was decreased to 70.5% when the fruit detection algorithm was applied in the foliated trees. From the experimental results, it can be concluded that LiDAR-based technology and, particularly, its intensity information has potential for remote apple detection and 3D localisation.","PeriodicalId":124961,"journal":{"name":"2020 IEEE International Workshop on Metrology for Agriculture and Forestry (MetroAgriFor)","volume":"44 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115174189","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Towards the optimization of a scintillator-based neutron detector for large non-invasive soil moisture estimation 基于闪烁体的非侵入式土壤水分估算中子探测器的优化研究

2020 IEEE International Workshop on Metrology for Agriculture and Forestry (MetroAgriFor) Pub Date : 2020-11-04 DOI: 10.1109/MetroAgriFor50201.2020.9277582

L. Stevanato, M. Polo, M. Lunardon, F. Marinello, S. Moretto, G. Baroni

引用次数: 3

A Smart Monitoring System for a Future Smarter Dairy Farming 未来智能奶牛养殖的智能监控系统

2020 IEEE International Workshop on Metrology for Agriculture and Forestry (MetroAgriFor) Pub Date : 2020-11-04 DOI: 10.1109/MetroAgriFor50201.2020.9277547

Marco Bovo, S. Benni, A. Barbaresi, Enrica Santolini, Miki Agrusti, D. Torreggiani, P. Tassinari

{"title":"A Smart Monitoring System for a Future Smarter Dairy Farming","authors":"Marco Bovo, S. Benni, A. Barbaresi, Enrica Santolini, Miki Agrusti, D. Torreggiani, P. Tassinari","doi":"10.1109/MetroAgriFor50201.2020.9277547","DOIUrl":"https://doi.org/10.1109/MetroAgriFor50201.2020.9277547","url":null,"abstract":"The study presents the objectives, methodologies and technical features of a monitoring system designed in the context of a national research project proposing innovative technical solutions for smart dairy farming in order to increase the herd productivity. The main objective of the project was to improve environment conditions and animal welfare of dairy cows and consequently enhance reproduction and production. To this regard, the system described here has a fundamental role operating as a tool for a more efficient management of the herd and of the herd housing. In fact, in the last years, animal control techniques and farm environmental monitoring have been developed, with increasing intensity, through the integration of Information Communication Technology (ICT) systems and models for the analysis and interpretation of the data collected. In this field, the Smart Monitoring System (SMS) described here could be effectively implemented in order to increase the animal welfare but at the same time increase the animal production. The integration of different type of sensors, different type of data and different type of models is at the base of the modern techniques for herd management and of the tools aiming to increase the dairy sector sustainability. The system integrates specifically designed sensor networks in a customized system architecture. It allows the acquisition of big data time series about physical and environmental conditions of the facilities hosting the cow herds starting from the collection of both indoor and outdoor environmental features, enabling a smart control of the facility, a diagnosis of the operating conditions, and early alerts in case of anomalies. The system has been developed to work in several environmental situations and is able to make the data remotely available in real time in a cloud.","PeriodicalId":124961,"journal":{"name":"2020 IEEE International Workshop on Metrology for Agriculture and Forestry (MetroAgriFor)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126263180","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 4

Ammonia stripping from buffalo manure digestate for future nitrogen upcycling into bio-based products 从水牛粪便消化液中提取氨，用于未来的氮升级循环生产生物基产品

2020 IEEE International Workshop on Metrology for Agriculture and Forestry (MetroAgriFor) Pub Date : 2020-11-04 DOI: 10.1109/MetroAgriFor50201.2020.9277623

S. Matassa, S. Papirio, G. Esposito, F. Pirozzi

引用次数: 2

Autonomous Robotic Platform for Precision Orchard Management: Architecture and Software Perspective 精确果园管理的自主机器人平台:架构和软件视角

2020 IEEE International Workshop on Metrology for Agriculture and Forestry (MetroAgriFor) Pub Date : 2020-11-04 DOI: 10.1109/MetroAgriFor50201.2020.9277555

D. Mengoli, Roberto Tazzari, L. Marconi

引用次数: 11

Sensor-fusion and deep neural networks for autonomous UAV navigation within orchards 传感器融合和深度神经网络用于果园内自主无人机导航

2020 IEEE International Workshop on Metrology for Agriculture and Forestry (MetroAgriFor) Pub Date : 2020-11-04 DOI: 10.1109/MetroAgriFor50201.2020.9277568

Kushtrim Breslla, G. Bortolotti, A. Boini, G. Perulli, B. Morandi, L. C. Grappadelli, L. Manfrini

{"title":"Sensor-fusion and deep neural networks for autonomous UAV navigation within orchards","authors":"Kushtrim Breslla, G. Bortolotti, A. Boini, G. Perulli, B. Morandi, L. C. Grappadelli, L. Manfrini","doi":"10.1109/MetroAgriFor50201.2020.9277568","DOIUrl":"https://doi.org/10.1109/MetroAgriFor50201.2020.9277568","url":null,"abstract":"With the increase of population in the world, the demand for quality food is increasing too. In recent years, increasing demand and environmental factors have heavily influenced the agricultural production. Automation and robotics for fruit and vegetable production/monitoring have become the new standard. This paper discusses an autonomous Unmanned Aerial Vehicle (UAV) able to navigate through rows orchard rows. The UAV is comprised of a flight controller (AP stack), a microcontroller for analog reading of different sensors, and an On-Board Computer (OBC). Pictures are taken through a camera and streamed through WiFi to a Ground Control Computer (GCC) running a convolution neural network model. Based on prior training, the model outputs three directions: RIGHT, LEFT and STRAIGHT. A moving average of multiple frames per second is extracted and sent to a build-in Proportional-IntegralDerivative (PID) controller on the UAV. After error correction from this feedback, controller sends the direction to the flight controller using MAVLink protocol’s radio channel overrides, thus performing autonomous navigation.","PeriodicalId":124961,"journal":{"name":"2020 IEEE International Workshop on Metrology for Agriculture and Forestry (MetroAgriFor)","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126447764","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Future rainfall scenarios for the assessment of water availability in Italy 未来降雨情景对意大利可用水量的评估

2020 IEEE International Workshop on Metrology for Agriculture and Forestry (MetroAgriFor) Pub Date : 2020-11-04 DOI: 10.1109/MetroAgriFor50201.2020.9277599

R. Padulano, Giuseppe Francesco Cesare Lama, G. Rianna, M. Santini, M. Mancini, M. Stojiljković

引用次数: 8

Discriminating irrigation and rainfall with proximal gamma-ray spectroscopy 用近端伽玛射线能谱鉴别灌溉和降雨

2020 IEEE International Workshop on Metrology for Agriculture and Forestry (MetroAgriFor) Pub Date : 2020-11-04 DOI: 10.1109/MetroAgriFor50201.2020.9277556

A. Serafini, M. Albéri, Enrico Chiarelli, M. Montuschi, Kassandra Giulia Cristina Raptis, V. Strati, F. Mantovani

引用次数: 1