2020 IEEE International Workshop on Metrology for Agriculture and Forestry (MetroAgriFor)最新文献_第4页

Dimension fitting of wheat spikes in dense 3D point clouds based on the adaptive k-means algorithm with dynamic perspectives 基于动态视角自适应k-means算法的密集三维点云小麦穗维数拟合

2020 IEEE International Workshop on Metrology for Agriculture and Forestry (MetroAgriFor) Pub Date : 2020-11-04 DOI: 10.1109/MetroAgriFor50201.2020.9277611

Fuli Wang, V. Mohan, A. Thompson, Richard Dudley

引用次数: 3

Reinforcement Learning for Connected Autonomous Vehicle Localization via UAVs 基于无人机的联网自动驾驶车辆定位强化学习

2020 IEEE International Workshop on Metrology for Agriculture and Forestry (MetroAgriFor) Pub Date : 2020-11-04 DOI: 10.1109/MetroAgriFor50201.2020.9277630

Enrico Testi, Elia Favarelli, A. Giorgetti

{"title":"Reinforcement Learning for Connected Autonomous Vehicle Localization via UAVs","authors":"Enrico Testi, Elia Favarelli, A. Giorgetti","doi":"10.1109/MetroAgriFor50201.2020.9277630","DOIUrl":"https://doi.org/10.1109/MetroAgriFor50201.2020.9277630","url":null,"abstract":"In precision farming, a very promising scenario is represented by a connected and autonomous vehicle (CAV) moving in a cultivated field and collecting high-resolution videos and hyperspectral images, requiring both localization and broadband communication. An effective approach to provide both localization and wideband communication exploits unmanned aerial vehicles (UAVs) that may act as relays to ensure seamless connectivity with a base station (BS). In this paper, we propose a reinforcement learning (RL)-based algorithm to find the best spatial configuration of a swarm of UAVs to localize a CAV in an unknown environment and assist the communication with a BS. The UAVs cooperate to estimate the position of the CAV exploiting only the received signal strength (RSS). A reward function, based on the distance between the UAVs and the CAV, and the estimated geometric diluition of precision (GDOP), is designed. Numerical results show how the proposed multi-agent Q-learning allows the UAVs to reach low root mean square error (RMSE) in the target localization, even without previous knowledge about the environment.","PeriodicalId":124961,"journal":{"name":"2020 IEEE International Workshop on Metrology for Agriculture and Forestry (MetroAgriFor)","volume":"116 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":"133666280","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: 7

Integrating UAV and satellite data to assess the effects of agroforestry on microclimate in Dodoma region, Tanzania 整合无人机和卫星数据评估农林业对坦桑尼亚Dodoma地区小气候的影响

2020 IEEE International Workshop on Metrology for Agriculture and Forestry (MetroAgriFor) Pub Date : 2020-11-04 DOI: 10.1109/MetroAgriFor50201.2020.9277643

Villani Lorenzo, Castelli Giulio, Sambalino Francesco, A. Oliveira, Lucas Allan, B. Elena

{"title":"Integrating UAV and satellite data to assess the effects of agroforestry on microclimate in Dodoma region, Tanzania","authors":"Villani Lorenzo, Castelli Giulio, Sambalino Francesco, A. Oliveira, Lucas Allan, B. Elena","doi":"10.1109/MetroAgriFor50201.2020.9277643","DOIUrl":"https://doi.org/10.1109/MetroAgriFor50201.2020.9277643","url":null,"abstract":"The present study investigates the microclimatic effect of trees in agroforestry systems in Dodoma region, Tanzania. A pixel-based analysis of Land Surface Temperature (LST) and Tree Canopy Cover (TCC) was performed on aerial imagery obtained with an Unmanned Aerial Vehicle (UAV) and on satellite data. UAV orthomosaic maps and satellite scenes were elaborated with a suite of Geographical Information Systems and the resulting data were statistically analyzed through linear regression, the Kruskall-Wallis test and the Dunn test. Results showed that the TCC of the surveyed areas was 5,1%, and a significant decrease in LST of 1,32 °C (p<0,01) was only found in areas with the highest TCC, during the late dry season. From these preliminary analyses, we suggest that a threshold of 10% TCC should be reached to have an ameliorated microclimate, in terms of temperature, in this agroecological zone. Considering the average plant phenotype characteristic to the area, it corresponds to a tree density of 50 trees per hectare.","PeriodicalId":124961,"journal":{"name":"2020 IEEE International Workshop on Metrology for Agriculture and Forestry (MetroAgriFor)","volume":"74 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":"124430381","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

Low cost center pivot irrigation monitoring systems based on IoT and LoRaWAN technologies 基于物联网和LoRaWAN技术的低成本中心支点灌溉监控系统

2020 IEEE International Workshop on Metrology for Agriculture and Forestry (MetroAgriFor) Pub Date : 2020-11-04 DOI: 10.1109/MetroAgriFor50201.2020.9277548

Diego Mateos Matilla, Á. L. Murciego, Diego Manuel Jiménez Bravo, André Sales Mendes, Valderi Reis Quietinho Leithardt

{"title":"Low cost center pivot irrigation monitoring systems based on IoT and LoRaWAN technologies","authors":"Diego Mateos Matilla, Á. L. Murciego, Diego Manuel Jiménez Bravo, André Sales Mendes, Valderi Reis Quietinho Leithardt","doi":"10.1109/MetroAgriFor50201.2020.9277548","DOIUrl":"https://doi.org/10.1109/MetroAgriFor50201.2020.9277548","url":null,"abstract":"An increase in the irrigation demand has been projected in Europe, especially in the countries with the highest proportion of irrigated agricultural areas, such as Italy and Spain. This has led to advances in the field of irrigation systems, as center pivot systems which have facilitated the labor of farmers because of their automation. However, the monitoring of these systems together with the early detection of problems in their operation has become a key aspect during a farmer campaign. To answer this need, different solutions have been proposed from the ICT area and the Precision Agriculture field. In this work, the main communication and sensor technologies used for monitoring irrigation with pivots are analyzed. Two low-cost systems based on IoT technologies, GPS and LoRaWAN are proposed for the monitoring of this type of irrigation systems. Finally, the results obtained after its installation in two maize crops are presented and the conclusions obtained after the use of both systems are discussed.","PeriodicalId":124961,"journal":{"name":"2020 IEEE International Workshop on Metrology for Agriculture and Forestry (MetroAgriFor)","volume":"61 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":"128478796","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: 5

A Low-Cost and High-Accuracy Non-Invasive System for the Monitoring of Fruit Growth 一种低成本、高精度、无创的水果生长监测系统

2020 IEEE International Workshop on Metrology for Agriculture and Forestry (MetroAgriFor) Pub Date : 2020-11-04 DOI: 10.1109/MetroAgriFor50201.2020.9277571

Lorenzo Mistral Peppi, Matteo Zauli, L. Manfrini, P. Traverso, L. Corelli Grappadelli, L. Marchi

引用次数: 2

pH strip reader for beer samples based on image analysis 基于图像分析的啤酒样品pH条阅读器

2020 IEEE International Workshop on Metrology for Agriculture and Forestry (MetroAgriFor) Pub Date : 2020-11-04 DOI: 10.1109/MetroAgriFor50201.2020.9277591

S. Dello Iacono, Adriana Erra, A. Pietrosanto, D. Di Caro, C. Liguori

引用次数: 0

[MetroAgriFor 2020 Front cover] [MetroAgriFor 2020封面]

2020 IEEE International Workshop on Metrology for Agriculture and Forestry (MetroAgriFor) Pub Date : 2020-11-04 DOI: 10.1109/metroagrifor50201.2020.9277583

引用次数: 0

Tomato diseases Classification Based on VGG and Transfer Learning 基于VGG和迁移学习的番茄病害分类

2020 IEEE International Workshop on Metrology for Agriculture and Forestry (MetroAgriFor) Pub Date : 2020-11-04 DOI: 10.1109/MetroAgriFor50201.2020.9277626

Lerina Aversano, M. Bernardi, Marta Cimitile, Martina Iammarino, Stefano Rondinella

引用次数: 11

Improving GHG flux monitoring in agricultural soil through the AGRESTIC prototype: a focus on the assessment of data quality 通过AGRESTIC原型改进农业土壤温室气体通量监测:侧重于数据质量评估

2020 IEEE International Workshop on Metrology for Agriculture and Forestry (MetroAgriFor) Pub Date : 2020-11-04 DOI: 10.1109/MetroAgriFor50201.2020.9277649

I. Volpi, S. Bosco, D. Guidotti, Michele Mammini, S. Neri, G. Virgili, P. Meriggi, A. Mantino, P. Laville, G. Ragaglini

引用次数: 0

Water spray detection for smart irrigation systems with Mask R-CNN and UAV footage 用于智能灌溉系统的水雾检测，带有面罩R-CNN和无人机镜头

2020 IEEE International Workshop on Metrology for Agriculture and Forestry (MetroAgriFor) Pub Date : 2020-11-04 DOI: 10.1109/MetroAgriFor50201.2020.9277542

Caio K. G. Albuquerque, Sergio Polimante, A. Torre-Neto, R. Prati

{"title":"Water spray detection for smart irrigation systems with Mask R-CNN and UAV footage","authors":"Caio K. G. Albuquerque, Sergio Polimante, A. Torre-Neto, R. Prati","doi":"10.1109/MetroAgriFor50201.2020.9277542","DOIUrl":"https://doi.org/10.1109/MetroAgriFor50201.2020.9277542","url":null,"abstract":"While the world’s population rises, demand for food grows accordingly. Smart agriculture emerges as a viable solution to increase the quality and efficiency of crops. Irrigation plays an essential role in the grade and productivity of harvests, while also being a crucial factor in the cost-effectiveness of food production. Smart irrigation uses technology to improve watering, such as the Internet of Things (IoT) applications and Machine Learning algorithms. The correct functioning of irrigation nozzles is critical to ensure that the hydration plan is deployed correctly to the crop field. This paper presents a Machine Learning algorithm that can automatically recognize water from aerial footage of irrigation systems. This automatic recognition can help in the irrigation system inspection, potentially reducing time and cost in system maintenance. Initial results show that it is possible to identify water on image frames captured by an Unmanned Aerial Vehicle (UAV) using the Mask R-CNN Neural Network. The goal is to identify malfunctioning irrigation systems that can lead to under or overwatering, compromising the irrigation plan’s correct implementation.","PeriodicalId":124961,"journal":{"name":"2020 IEEE International Workshop on Metrology for Agriculture and Forestry (MetroAgriFor)","volume":"177 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":"133467409","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: 12