2020 IEEE International Workshop on Metrology for Agriculture and Forestry (MetroAgriFor)最新文献_第7页

Evaluation of Virtual Methods for Training Neural Networks in Agricultural Applications 虚拟神经网络训练方法在农业中的应用评价

2020 IEEE International Workshop on Metrology for Agriculture and Forestry (MetroAgriFor) Pub Date : 2020-11-04 DOI: 10.1109/MetroAgriFor50201.2020.9277632

Jorge Luis Jiménez Aparicio, Jörn Thieling, J. Roßmann, Markus Robert, Rüdiger Bosdorf

{"title":"Evaluation of Virtual Methods for Training Neural Networks in Agricultural Applications","authors":"Jorge Luis Jiménez Aparicio, Jörn Thieling, J. Roßmann, Markus Robert, Rüdiger Bosdorf","doi":"10.1109/MetroAgriFor50201.2020.9277632","DOIUrl":"https://doi.org/10.1109/MetroAgriFor50201.2020.9277632","url":null,"abstract":"Improvement of productivity and efficiency in the agriculture sector points towards the necessity of developing autonomous vehicles. Here, the detection and classification of objects plays a major role, which can be achieved by using camera sensors and convolutional neural networks (CNNs). In order to train CNNs to perform correctly, good datasets are required. However, especially for the case of agriculture, datasets that contain relevant objects and are labeled (i.e. annotated with ground truth information) are not only scarce but also difficult to generate as this entails a high cost in resources and human labor. Therefore, we propose a different approach: using 3D simulation technology to generate relevant simulated sensor data which are implicitly labeled and a cost efficient solution to train neural networks. In this contribution, we assess the viability of training a CNN with simulated sensor data by comparing the achieved performance to a network trained with real sensor data. In addition, we evaluate the benefits of combining simulated data with real data for training CNNs, including complementary as well as Transfer Learning approaches. Finally, we show that using simulated sensor data for training CNNs is viable yet less accurate than using comparable real datasets and propose ways to improve simulations in this regard. To this end, we analyze various simulation factors in terms of their impact on the CNN performance and introduce further benefits of using simulated scenarios in general.","PeriodicalId":124961,"journal":{"name":"2020 IEEE International Workshop on Metrology for Agriculture and Forestry (MetroAgriFor)","volume":"10 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":"127961424","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

AI at the Edge: a Smart Gateway for Greenhouse Air Temperature Forecasting 边缘的人工智能:温室气温预报的智能网关

2020 IEEE International Workshop on Metrology for Agriculture and Forestry (MetroAgriFor) Pub Date : 2020-11-04 DOI: 10.1109/MetroAgriFor50201.2020.9277553

Gaia Codeluppi, Antonio Cilfone, Luca Davoli, G. Ferrari

{"title":"AI at the Edge: a Smart Gateway for Greenhouse Air Temperature Forecasting","authors":"Gaia Codeluppi, Antonio Cilfone, Luca Davoli, G. Ferrari","doi":"10.1109/MetroAgriFor50201.2020.9277553","DOIUrl":"https://doi.org/10.1109/MetroAgriFor50201.2020.9277553","url":null,"abstract":"Controlling and forecasting environmental variables (e.g., air temperature) is usually a key and complex part in a greenhouse management architecture. Indeed, a greenhouse inner micro-climate, which is the result of an extensive set of inter-related environmental variables influenced by external weather conditions, has to be tightly monitored, regulated, and, some-times, forecast. Nowadays, Wireless Sensor Networks (WSNs) and Machine Learning (ML) are two of the most successful technologies to deal with this challenge. In this paper, we discuss how a Smart Gateway (GW), acting as a collector for sensor data coming from a WSN installed in a greenhouse, could be enriched with a Neural Network (NN)-based prediction model allowing to forecast a greenhouse’s inner air temperature. In the case of missing sensor data coming from the WSN, the proposed prediction algorithm, fed with meteorological open data (gathered from the DarkSky repository), is run on the GW in order to predict the missing values. Despite the model is especially designed to be lightweight and executable by a device with constrained capabilities, it can be adopted either at Cloud or at GW level to forecast future air temperature’s values, in order to support the management of a greenhouse. Experimental results show that the NN-based prediction algorithm can forecast greenhouse air temperature with a Root Mean Square Error (RMSE) of 1.50 °C, a Mean Absolute Percentage Error (MAPE) of 4.91%, and a R2 score of 0.965.","PeriodicalId":124961,"journal":{"name":"2020 IEEE International Workshop on Metrology for Agriculture and Forestry (MetroAgriFor)","volume":"35 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":"129858595","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: 11

Sensitivity of the agro-hydrological model CRITERIA-1D to the Leaf Area Index parameter 农业水文模型criterion - 1d对叶面积指数参数的敏感性

2020 IEEE International Workshop on Metrology for Agriculture and Forestry (MetroAgriFor) Pub Date : 2020-11-04 DOI: 10.1109/MetroAgriFor50201.2020.9277614

Tamara Ricchi, V. Alagna, G. Villani, F. Tomei, A. Toscano, G. Baroni

{"title":"Sensitivity of the agro-hydrological model CRITERIA-1D to the Leaf Area Index parameter","authors":"Tamara Ricchi, V. Alagna, G. Villani, F. Tomei, A. Toscano, G. Baroni","doi":"10.1109/MetroAgriFor50201.2020.9277614","DOIUrl":"https://doi.org/10.1109/MetroAgriFor50201.2020.9277614","url":null,"abstract":"Water availability is strongly variable in space and time, also due to the climate change. Agriculture is a sector specially affected by the water scarcity problem considering that is one of the main users. Irrigation scheduling simulation models play an important role in this context by estimating plant water requirements and supporting best water management practices. Representative model parameters and input data are however fundamental to achieve good model performances. The objective of this work was to assess the sensitivity of the agro-hydrological model CRITERIA-1D to the leaf area index (LAI) parameter, commonly used to characterize the plant status and to represent its developing stages. The model has been set up using, on the one hand, literature LAIMAX and LAIMIN values and, on the other hand, ground measured values, obtained by means of a ceptometer. Results show significant differences between the irrigation water requirements estimated between the two scenarios. For this reason, the study underlines the need to adopt accurate crop parameters and to integrate real-time crop measurements for the estimation of the irrigation water requirement. Smaller differences are quantified, however, when looking at the deep percolation estimated by the model highlighting the importance of considering multiple outputs for a comprehensive assessment of the model.","PeriodicalId":124961,"journal":{"name":"2020 IEEE International Workshop on Metrology for Agriculture and Forestry (MetroAgriFor)","volume":"251 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":"132680394","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

Energy-neutral weather stations for precision agriculture: challenges and approaches 精准农业的能源中性气象站:挑战和方法

2020 IEEE International Workshop on Metrology for Agriculture and Forestry (MetroAgriFor) Pub Date : 2020-11-04 DOI: 10.1109/MetroAgriFor50201.2020.9277565

Padma Balaji Leelavinodhan, Fabio Antonelli, M. Vecchio, A. Maestrini

引用次数: 3

Understanding the tradeoffs of LoRaWAN for IoT-based Smart Irrigation 了解LoRaWAN对基于物联网的智能灌溉的权衡

2020 IEEE International Workshop on Metrology for Agriculture and Forestry (MetroAgriFor) Pub Date : 2020-11-04 DOI: 10.1109/MetroAgriFor50201.2020.9277566

Bruno Queté, Alexandre Heideker, Ivan D. Zyrianoff, Dener Ottolini, J. H. Kleinschmidt, J. Soininen, C. Kamienski

引用次数: 8

Methodology for Plant Specific Cultivation through a Plant Identification pipeline 通过植物鉴定管道进行植物特异性培养的方法

2020 IEEE International Workshop on Metrology for Agriculture and Forestry (MetroAgriFor) Pub Date : 2020-11-04 DOI: 10.1109/MetroAgriFor50201.2020.9277567

Matteo Pantano, T. Kamps, Solomon Pizzocaro, Giorgio Pantano, M. Corno, S. Savaresi

引用次数: 2

Impedimetric label - free immunosensor for rapid detection of Ochratoxin A in beer and wine 啤酒和葡萄酒中赭曲霉毒素A快速检测的无阻标免疫传感器

2020 IEEE International Workshop on Metrology for Agriculture and Forestry (MetroAgriFor) Pub Date : 2020-11-04 DOI: 10.1109/MetroAgriFor50201.2020.9277594

Francesca Malvano, D. Albanese, R. Pilloton

引用次数: 1

A mobile thermal-RGB imaging tool for mapping crop water stress of grapevines 用于绘制葡萄作物水分胁迫的移动热rgb成像工具

2020 IEEE International Workshop on Metrology for Agriculture and Forestry (MetroAgriFor) Pub Date : 2020-11-04 DOI: 10.1109/MetroAgriFor50201.2020.9277545

Basavaraj R. Amogi, A. Chandel, L. Khot, P. Jacoby

{"title":"A mobile thermal-RGB imaging tool for mapping crop water stress of grapevines","authors":"Basavaraj R. Amogi, A. Chandel, L. Khot, P. Jacoby","doi":"10.1109/MetroAgriFor50201.2020.9277545","DOIUrl":"https://doi.org/10.1109/MetroAgriFor50201.2020.9277545","url":null,"abstract":"This study evaluates a tablet computer-enabled miniature thermal-RGB imager to map the crop water stress index (CWSI) in grapevines. Imagery data were acquired in a commercial grapevine block irrigated at four different rates (100, 80, 60, and 40% of replacement evapotranspiration [ET]). The latter three were the subsurface irrigation treatments. A custom image analysis algorithm was developed and CWSI was estimated using empirical baseline equations. RGB imaging refined the segmentation of the target canopy from background objects. Canopy-air temperature difference had significant and strong relationships with the vapor pressure deficits as per local conditions (R2: 0.72–0.85, p < 0.05). CWSI estimates for grapevines irrigated at 40% of ET were consistently the highest and were followed by for those irrigated at 60, 80, and 100% of ET rates. Study findings highlight the potential to develop a smartphone-enabled imaging and integrated application for real-time crop water stress assessment under field conditions. Our future efforts will refine these computing algorithms and develop a smartphone application for growers to assess tree-level crop water demands for site-specific irrigation management.","PeriodicalId":124961,"journal":{"name":"2020 IEEE International Workshop on Metrology for Agriculture and Forestry (MetroAgriFor)","volume":"3 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":"125970352","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