2020 IEEE International Workshop on Metrology for Agriculture and Forestry (MetroAgriFor)最新文献_第2页

A Smart Monitoring System for Self-sufficient Integrated Multi-Trophic AquaPonic 自给式综合多营养水培智能监测系统

2020 IEEE International Workshop on Metrology for Agriculture and Forestry (MetroAgriFor) Pub Date : 2020-11-04 DOI: 10.1109/MetroAgriFor50201.2020.9277639

A. Barbaresi, C. Bibbiani, Marco Bovo, Steafano Benni, Enrica Santolini, P. Tassinari, Miki Agrusti, D. Torreggiani

引用次数: 3

Application of nitrification inhibitor on soil to reduce NH3 and N2O emission after slurry spreading 在土壤上施用硝化抑制剂以减少浆料铺布后NH3和N2O的排放

2020 IEEE International Workshop on Metrology for Agriculture and Forestry (MetroAgriFor) Pub Date : 2020-11-04 DOI: 10.1109/MetroAgriFor50201.2020.9277570

J. Maffia, L. Rolle, Simone Pelissetti, Francesco Vocino, M. Dzikowski, Matteo Ceruti, E. Dinuccio

{"title":"Application of nitrification inhibitor on soil to reduce NH3 and N2O emission after slurry spreading","authors":"J. Maffia, L. Rolle, Simone Pelissetti, Francesco Vocino, M. Dzikowski, Matteo Ceruti, E. Dinuccio","doi":"10.1109/MetroAgriFor50201.2020.9277570","DOIUrl":"https://doi.org/10.1109/MetroAgriFor50201.2020.9277570","url":null,"abstract":"Manure spreading is one of the main source of ammonia (NH<inf>3</inf>) emissions in the livestock sector, which is responsible of the 75 % of anthropogenic NH<inf>3</inf> losses. For liquid manure, the most effective distribution technique to abate NH<inf>3</inf> emissions is direct injection, which allows for a NH<inf>3</inf> emission abatement up to 90 %. Nonetheless, direct injection has been shown to potentially increase, under certain environmental conditions, nitrous oxide (N<inf>2</inf>O) emissions from soil after slurry spreading. The aim of this study is to assess the effect of the commercial nitrification inhibitor N-LockTM (CORTEVATM agriscience) on NH<inf>3</inf> and N<inf>2</inf>O emissions after spreading of two different slurry types. The product was tested in a field trial on two different soils (loam and sandy-loam) and in combination with two different types of manure (cattle slurry and digestate). The N-LockTM product appears to have a good potential for N<inf>2</inf>O emission reduction from fields after slurry spreading with direct injection techniques. Nonetheless, proper emission abatements (up to 79 %) were obtained only in one of the two soils included in the study and N-LockTM efficiency differed depending also on slurry type.","PeriodicalId":124961,"journal":{"name":"2020 IEEE International Workshop on Metrology for Agriculture and Forestry (MetroAgriFor)","volume":"24 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":"116762787","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

Addition of powdery sulfur to pig slurry to reduce NH3 and GHG emissions after mechanical separation 在猪浆中加入粉状硫，减少机械分离后NH3和GHG的排放

2020 IEEE International Workshop on Metrology for Agriculture and Forestry (MetroAgriFor) Pub Date : 2020-11-04 DOI: 10.1109/MetroAgriFor50201.2020.9277551

J. Maffia, F. Gioelli, L. Rolle, G. Airoldi, P. Balsari, E. Dinuccio

引用次数: 0

Convolutional Neural Networks for Detection of Storage Disorders on ‘Abbé Fétel’ pears 基于卷积神经网络的“abbabre fsamtel”梨储藏障碍检测

2020 IEEE International Workshop on Metrology for Agriculture and Forestry (MetroAgriFor) Pub Date : 2020-11-04 DOI: 10.1109/MetroAgriFor50201.2020.9277561

A. Bonora, Eleonora Trevisani, K. Bresilla, L. Corelli Grappadelli, G. Bortolotti, L. Manfrini

{"title":"Convolutional Neural Networks for Detection of Storage Disorders on ‘Abbé Fétel’ pears","authors":"A. Bonora, Eleonora Trevisani, K. Bresilla, L. Corelli Grappadelli, G. Bortolotti, L. Manfrini","doi":"10.1109/MetroAgriFor50201.2020.9277561","DOIUrl":"https://doi.org/10.1109/MetroAgriFor50201.2020.9277561","url":null,"abstract":"Image processing has recently been adopted for fruit damage detection in post-harvest operations. Through the implementation of hard-coded feature extraction algorithms, high accuracy has been found. The present study tested the fast and operational convolution neural networks with “YOLO v3” architecture using the online platform Supervise.ly to detect on pear fruit ‘Abbé Fétel’ physiological disorders such as superficial scald. Two different models were trained: I) one to detect the individual pear fruits within the batches; II) one to detect superficial scald or senescence scald on pear skin. Preliminary statistics show that the model to count the fruit inside the batches reaches an accuracy of 64.70% with a 0.5 of Intersection of Units. The second one has less accuracy (up to 20% of true positive) but maintains a good level of average precision (0.6) with different confidence thresholds (0.4 and 0.2). Further research is needed to improve the accuracy of both models and to map quality pre- and post-harvest. These results will help the packing house to manage fruit batches and to ensure good fruit quality for consumers.","PeriodicalId":124961,"journal":{"name":"2020 IEEE International Workshop on Metrology for Agriculture and Forestry (MetroAgriFor)","volume":"29 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":"125553869","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

Smart and cheap scale for estimating live-fish biomass in offshore aquaculture 用于估算近海水产养殖中活鱼生物量的智能廉价秤

2020 IEEE International Workshop on Metrology for Agriculture and Forestry (MetroAgriFor) Pub Date : 2020-11-04 DOI: 10.1109/MetroAgriFor50201.2020.9277662

E. Damiano, C. Bibbiani, B. Fronte, A. Di Lieto

引用次数: 3

In-field Vis/NIR hyperspectral imaging to measure soluble solids content of wine grape berries during ripening 现场可见/近红外高光谱成像技术测量酿酒葡萄果实成熟过程中可溶性固形物含量

2020 IEEE International Workshop on Metrology for Agriculture and Forestry (MetroAgriFor) Pub Date : 2020-11-04 DOI: 10.1109/MetroAgriFor50201.2020.9277621

A. Benelli, C. Cevoli, A. Fabbri

{"title":"In-field Vis/NIR hyperspectral imaging to measure soluble solids content of wine grape berries during ripening","authors":"A. Benelli, C. Cevoli, A. Fabbri","doi":"10.1109/MetroAgriFor50201.2020.9277621","DOIUrl":"https://doi.org/10.1109/MetroAgriFor50201.2020.9277621","url":null,"abstract":"Monitoring the quality attributes of grapes is a practice that allows to check the grapes’ state of ripeness and to decide when it is appropriate to proceed with the harvest. In the present study, a non-destructive method based on hyperspectral imaging (HSI) technology was developed. Analyses were carried out directly in the field using a Vis/NIR (400–1000 nm) hyperspectral camera (HSC) between the rows of ‘Sangiovese’ (Vitis vinifera L.) vineyard destined for wine production. One vineyard row was analyzed on 13 different days. During the trials, 33 berries were collected and the soluble solids content (SSC) expressed in terms of °Brix (°Bx) was measured by a portable digital refractometer. The mean spectra of the selected berries were extracted from each hyperspectral (HS) image. The pre-treated mean spectra were used to predict the SSC of the berries by means of partial least squares (PLS) regression, obtaining a value of R2 = 0.75 in cross-validation, with RMSECV = 0.84 °Bx. The present study shows the potential of the use of HSI technology directly in the field through proximal measurements under natural light conditions for the prediction of the SSC quality attribute of grapes.","PeriodicalId":124961,"journal":{"name":"2020 IEEE International Workshop on Metrology for Agriculture and Forestry (MetroAgriFor)","volume":"27 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":"116022165","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: 2

Release of ammonia, particulate matter and nitrogen oxides during the Covid-19 quarantine: what is the role of livestock activitiesƒ 2019冠状病毒病隔离期间氨、颗粒物和氮氧化物的释放:牲畜活动的作用是什么

2020 IEEE International Workshop on Metrology for Agriculture and Forestry (MetroAgriFor) Pub Date : 2020-11-04 DOI: 10.1109/MetroAgriFor50201.2020.9277575

D. Lovarelli, C. Conti, A. Finzi, J. Bacenetti, M. Guarino

{"title":"Release of ammonia, particulate matter and nitrogen oxides during the Covid-19 quarantine: what is the role of livestock activitiesƒ","authors":"D. Lovarelli, C. Conti, A. Finzi, J. Bacenetti, M. Guarino","doi":"10.1109/MetroAgriFor50201.2020.9277575","DOIUrl":"https://doi.org/10.1109/MetroAgriFor50201.2020.9277575","url":null,"abstract":"Several gases contribute to air pollution and most of all to the formation of secondary particulate matter (PM2.5), which is recognized as a source of severe risk to human health. Even if huge steps forward have been done worldwide, traffic, industrial activities, and the energy sector are mostly responsible for the release of NOx and SOx, while the agricultural sector is mainly responsible for the emission of NH3 deriving from the barn, the manure storage, management and final field application. In this study, the emission of PM2.5, NOx and NH3 is analyzed in the main provinces of the Lombardy region in which livestock activities are carried out, comparing emissions of 2016-2019 and those of 2020 during the lockdown determined by the spread of Covid-19 disease. The aim is to understand if and how a change in air emissions can be identified. The results show that PM2.5 and NOx reduced, most of all in urban areas, whereas NH3 maintained the same trend of previous years. From the statistical analysis emerges also that NH3 has a different behavior respect to PM2.5 and NOx, these latter being much more correlated between each other than NH3. However, further studies should be carried out on a bigger spatial and temporal scale.","PeriodicalId":124961,"journal":{"name":"2020 IEEE International Workshop on Metrology for Agriculture and Forestry (MetroAgriFor)","volume":"17 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":"121214495","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

A Nearest Neighbors based Data Filter for Fog Computing in IoT Smart Agriculture 基于最近邻的物联网智能农业雾计算数据过滤器

2020 IEEE International Workshop on Metrology for Agriculture and Forestry (MetroAgriFor) Pub Date : 2020-11-04 DOI: 10.1109/MetroAgriFor50201.2020.9277661

F. M. Ribeiro, R. Prati, Reinaldo A. C. Bianchi, C. Kamienski

{"title":"A Nearest Neighbors based Data Filter for Fog Computing in IoT Smart Agriculture","authors":"F. M. Ribeiro, R. Prati, Reinaldo A. C. Bianchi, C. Kamienski","doi":"10.1109/MetroAgriFor50201.2020.9277661","DOIUrl":"https://doi.org/10.1109/MetroAgriFor50201.2020.9277661","url":null,"abstract":"In smart agriculture, the Internet of Things (IoT) makes it possible to analyze and manage agricultural yield to increase productivity, reduce wasted resources, and decrease irrigation costs. In IoT systems, if data management is entirely performed in the cloud, the system may not work correctly due to connectivity problems, which is common in some remote regions where the agribusiness thrives. A fog computing solution enables the IoT system to process data faster and deal with intermittent connectivity. However, a high number of packets sent from the fog to the cloud can cause link congestion with mostly useless data traffic. Dealing with fog data filtering is a challenge because it requires knowing which data is essential to send to the cloud. This paper proposes an approach to collect and store data in a smart agriculture environment and two different methods filtering data in the fog. We designed an experiment for each filtering method, using a real dataset containing temperature and humidity values. In both experiments, the fog filters the data using the k-Nearest-Neighbors (kNN) algorithm, which classifies data into categories according to their value ranges. In the first experiment, the fog classifies the data and generates an output of the number of data categories. In the second experiment, data is classified and also compressed based on the previously obtained categories using the runlength encoding (RLE) technique to preserve the data time series nature. Our results show that data filtering reduces the amount of data sent by the fog to the cloud.","PeriodicalId":124961,"journal":{"name":"2020 IEEE International Workshop on Metrology for Agriculture and Forestry (MetroAgriFor)","volume":"536 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":"127308970","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: 14

Enhancing Soil Measurements with a Multi-Depth Sensor for IoT-based Smart Irrigation 基于物联网智能灌溉的多深度传感器增强土壤测量

2020 IEEE International Workshop on Metrology for Agriculture and Forestry (MetroAgriFor) Pub Date : 2020-11-04 DOI: 10.1109/MetroAgriFor50201.2020.9277562

A. Torre-Neto, Jeferson Rodrigues Cotrim, João Henrique Kleinschmidt, C. Kamienski, Marcos Cezar Visoli

引用次数: 3

Development of new system and methodology for the assessment of stressed and missing plants in vineyards: preliminary study 开发新的系统和方法来评估葡萄园中受胁迫和缺失的植物:初步研究

2020 IEEE International Workshop on Metrology for Agriculture and Forestry (MetroAgriFor) Pub Date : 2020-11-04 DOI: 10.1109/MetroAgriFor50201.2020.9277549

G. Daglio, D. Zampieri, R. Gallo, F. Mazzetto

引用次数: 0