Convolutional Neural Network in the Recognition of Spatial Images of Sugarcane Crops in the Troncal Region of the Coast of Ecuador

Cevallos, J.P. Cobena; Villagomez, Atiencia; Andryshchenko, I.S.

doi:10.1016/j.procs.2019.02.001

Cited by 11 publications

(4 citation statements)

References 9 publications

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“…This building knowledge base can be automatically enriched using new machine learning techniques, thus enhancing the inference engine. Furthermore, better building image processing could be obtained by using new generation deep convolutional neural networks like those described in Cevallos et al (2019) and Mayya et al (2016). Image pre-processing to remove noise and unwanted features could also be useful to enhance contour closure and building component results (see, for example, Vinay et al, 2018).…”

Section: -Conclusion Discussion and Future Workmentioning

confidence: 99%

Automatic generation of building information models from digitized plans

Doukari

Greenwood

2020

Automation in Construction

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This paper proposes a new approach to creating Building Information (BIM) models of existing buildings from digitized images. This automatic approach is based on three main steps. The first involves extracting the useful information automatically from rasterized plans by using image processing techniques that include segmentation, filtering, dilation, erosion, and contour detection. This information feeds the knowledge base of an expert system for BIM model generation. In the second step, using the knowledge base of the expert system, the information required to inform the BIM model can be deduced. The range of information thus obtainable can be extended beyond the examples given. The paper concludes with a discussion of the final stage: the automatic generation of an Industry Foundation Classes (IFC) information model with all the desired geometric, physical and technical information. This can be accomplished by using one of the available open-source application program interfaces (APIs). This stage is currently work-in-progress and will be the subject of a future publication.

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Section: -Conclusion Discussion and Future Workmentioning

confidence: 99%

Automatic generation of building information models from digitized plans

Doukari

Greenwood

2020

Automation in Construction

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“…Numerous papers deal with the application of segmentation, Machine Learning, and Data Mining methods to estimate the sugarcane state using classification maps [28][29][30][31]. The simplest approaches involve classification of the sugarcane state and stages of its growth (including the harvested areas), i.e., with the use of statistic methods (such as a maximum likelihood estimation) or Object-Based Image Analysis (OBIA) [32].…”

Section: Introductionmentioning

confidence: 99%

Monitoring of Sugarcane Harvest in Brazil Based on Optical and SAR Data

Kavats¹,

Khramov²,

Sergieieva³

et al. 2020

Remote Sensing

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The algorithms for determining sugarcane harvest dates are proposed; the algorithms allow the ability to monitor large areas and are based on the publicly available Synthetic Aperture Radar (SAR) and optical satellite data. Algorithm 1 uses the NDVI (Normalized Difference Vegetation Index) time series derived from Sentinel-2 data. Sharp and continuous decrease in the NDVI values is the main sign of sugarcane harvest. The NDVI time series allows the ability to determine most harvest dates. The best estimates of the sugarcane areas harvested per month have been obtained from March to August 2018 when cloudy pixel percentage is less than 45% of the image area. Algorithm 2 of the harvest monitoring uses the coherence time series derived from Sentinel-1 Single Look Complex (SLC) images and optical satellite data. Low coherence, demonstrating sharp growth upon the harvest completion, corresponds to the harvest period. The NDVI time series trends were used to refine the algorithm. It is supposed that the descending NDVI trend corresponds to harvest. The algorithms were used to identify the harvest dates and calculate the harvested areas of the reference sample of 574 sugarcane parcels with a total area of 3745 ha in the state of São Paulo, Brazil. The harvested areas identified by visual interpretation coincide with the optical-data algorithm (algorithm 1) by 97%; the coincidence with the algorithm based on SAR and optical data (algorithm 2) is 90%. The main practical applications of the algorithms are harvest monitoring and identification of the harvested fields to estimate the harvested area.

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“…The use of data mining to assess the performance of classifiers in the sugarcane sector is quite broad. It ranges from classifiers for mapping sugarcane planting [30] to deep learning techniques for the detection of sugarcane diseases [31] and classification of crop yield characteristics with neural networks used both in the recognition and in the grading of satellite images of sugarcane plantations [32].The present study aimed to assess and classify energy-environmental efficiency levels to reduce greenhouse gas emissions in the production, commercialization, and use of biofuels certified by the Brazilian National Biofuel Policy (RenovaBio).…”

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Classifying the Level of Energy-Environmental Efficiency Rating of Brazilian Ethanol

et al. 2020

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The present study aimed to assess and classify energy-environmental efficiency levels to reduce greenhouse gas emissions in the production, commercialization, and use of biofuels certified by the Brazilian National Biofuel Policy (RenovaBio). The parameters of the level of energy-environmental efficiency were standardized and categorized according to the Energy-Environmental Efficiency Rating (E-EER). The rating scale varied between lower efficiency (D) and high efficiency + (highest efficiency A+). The classification method with the J48 decision tree and naive Bayes algorithms was used to predict the models. The classification of the E-EER scores using a decision tree using the J48 algorithm and Bayesian classifiers using the naive Bayes algorithm produced decision tree models efficient at estimating the efficiency level of Brazilian ethanol producers and importers certified by the RenovaBio. The rules generated by the models can assess the level classes (efficiency scores) according to the scale discretized into high efficiency (Classification A), average efficiency (Classification B), and standard efficiency (Classification C). These results might generate an ethanol energy-environmental efficiency label for the end consumers and resellers of the product, to assist in making a purchase decision concerning its performance. The best classification model was naive Bayes, compared to the J48 decision tree. The classification of the Energy Efficiency Note levels using the naive Bayes algorithm produced a model capable of estimating the efficiency level of Brazilian ethanol to create labels.The production of biofuel from sugarcane is supported by the National Agency of Petroleum, Natural Gas and Biofuels' (ANP) sustainable development programs to achieve goals to reduce greenhouse gas emissions. Therefore, annual national targets were developed by the Brazilian National Biofuel Policy (RenovaBio) [12,15]. These goals are established in units of Decarbonization Credits (CBios) calculated from annual mandatory targets defined individually for each producer and distributor according to their participation in the fossil fuel market [12,16,[18][19][20][21].The certification of biofuel production assigns different marks to each biofuel producer and importer, in a value inversely related to the carbon amount of biofuel produced. The note reflects the individual contribution of each producing agent to mitigate a specific amount of GHG concerning its fossil substitute (in tons of CO 2 equivalent). The total emission is compared with that of the equivalent fossil fuel (gasoline, for ethanol), resulting in a final score (Energy-Environmental Efficiency Rating), characterizing the mitigation of emissions. This note generates CBios for biofuel producers and importers; with the decarbonization of the Brazilian energy matrix, there is a mechanism for the commercialization of these CBios linked to the carbon intensity of biofuels [2,12,16,18].The incentive to reduce pollutant emissions in the biofuel chain goes far beyond the use o...

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Convolutional Neural Network in the Recognition of Spatial Images of Sugarcane Crops in the Troncal Region of the Coast of Ecuador

Cited by 11 publications

References 9 publications

Automatic generation of building information models from digitized plans

Automatic generation of building information models from digitized plans

Monitoring of Sugarcane Harvest in Brazil Based on Optical and SAR Data

Classifying the Level of Energy-Environmental Efficiency Rating of Brazilian Ethanol

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