Soil compaction by machine traffic and least limiting water range related to soybean yield

The research aimed to evaluate machine traffic effect on soil compaction and the least limiting water range related to soybean cultivar yields, during two years, in a Haplustox soil. The six treatments were related to tractor (11 Mg weight) passes by the same place: T0, no compaction; and T1*, 1; T1, 1; T2, 2; T4, 4 and T6, 6. In the treatment T1*, the compaction occurred when soil was dried, in 2003/2004, and with a 4 Mg tractor in 2004/2005. Soybean yield was evaluated in relation to soil compaction during two agricultural years in completely randomized design (compaction levels); however, in the second year, there was a factorial scheme (compaction levels, with and without irrigation), with four replicates represented by 9 m² plots. In the first year, soybean [Glycine max (L.) Merr.] cultivar IAC Foscarim 31 was cultivated without irrigation; and in the second year, IAC Foscarim 31 and MG/BR 46 (Conquista) cultivars were cultivated with and without irrigation. Machine traffic causes compaction and reduces soybean yield for soil penetration resistance between 1.64 to 2.35 MPa, and bulk density between 1.50 to 1.53 Mg m-3. Soil bulk density from which soybean cultivar yields decrease is lower than the critical one reached at least limiting water range (LLWR =/ 0).

Soil compaction by machine traffic and least limiting water range related to soybean yield

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

EFFECTS OF COMPACTION DUE TO MACHINERY TRAFFIC ON SOIL PORE CONFIGURATION

Soil compaction has been recognized as a severe problem in mechanized agriculture and has an influence on many soil properties and processes. Yet, there are few studies on the long-term effects of soil compaction, and the development of soil compaction has been shown through a limited number of soil parameters. The objectives of this study were to evaluate the persistence of soil compaction effects (three traffic treatments: T0, without traffic; T3, three tractor passes; and T5, five tractor passes) on pore system configuration, through static and dynamic determinations; and to determine changes in soil pore orientation due to soil compaction through measurement of hydraulic conductivity of saturated soil in samples taken vertically and horizontally. Traffic led to persistent changes in all the dynamic indicators studied (saturated hydraulic conductivity, K0; effective macro- and mesoporosity, εma and εme), with significantly lower values of K0, εma, and εme in the T5 treatment. The static indicators of bulk density (BD), derived total porosity (TP), and total macroporosity (θma) did not vary significantly among the treatments. This means that machine traffic did not produce persistent changes on these variables after two years. However, the orientation of the soil pore system was modified by traffic. Even in T0, there were greater changes in K0 measured in the samples taken vertically than horizontally, which was more related to the presence of vertical biopores, and to isotropy of K0 in the treatments with machine traffic. Overall, the results showed that dynamic indicators are more sensitive to the effects of compaction and that, in the future, static indicators should not be used as compaction indicators without being complemented by dynamic indicators.

Traffic soil compaction of an oxisol related to soybean development and yield

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Impacto do tráfego de máquinas na qualidade física do solo e produtividade de milho em Argissolo

Este estudo teve o objetivo de avaliar o impacto do tráfego de máquinas na qualidade física do solo e produtividade de milho em Argissolo. O delineamento experimental foi inteiramente casualizado, com seis tratamentos e oito repetições representadas por parcelas de 14 m². Os tratamentos foram: T0) sem tráfego; T1*) uma passada de trator de 3,0 t, uma ao lado da outra; T1) uma passada; T2) duas passadas; T4) quatro passadas; T8) oito passadas de um trator de 8,0 t. Utilizou-se o milho (Zea mays L.) híbrido master, que foi avaliado quanto à produtividade de grãos. No solo, foram avaliados a porosidade, a densidade do solo, a resistência à penetração, o intervalo hídrico ótimo e a densidade do solo relativa. O tráfego de máquinas compactou o solo até 0,25 m de profundidade e reduziu a produtividade de milho em até 22%. O intervalo hídrico ótimo diminuiu com o aumento do tráfego de máquinas indicando decréscimo da qualidade física do solo para o milho. A densidade do solo relativa limitante à produtividade de milho é de 0,89.

O tráfego de máquinas agrícolas e as propriedades físicas, hídricas e mecânicas de um Latossolo dos Cerrados

No Brasil, o tráfego contínuo e inadequado de rodados de máquinas e a ação da soleira dos implementos sobre áreas agrícolas na região dos Cerrados têm provocado alterações dos atributos físicos e mecânicos dos solos. Com este entendimento, este estudo teve por objetivo avaliar a influência do rodado traseiro e da soleira de implementos agrícolas, usualmente utilizados na região dos Cerrados, sobre a compressibilidade de um Latossolo Vermelho distrófico típico. Os ensaios foram realizados em parcelas preparadas com arado de discos, arado de aivecas, grade aradora e semeadora/adubadora, que, desde novembro de 1994 até à data da amostragem, novembro de 1999, estiveram sob o sistema plantio direto. As alterações na compressibilidade foram avaliadas pela quantificação das pressões de preconsolidação, assim como por algumas propriedades físicas e hídricas (densidade do solo, porosidade e condutividade hidráulica do solo saturado) no momento do preparo e depois da colheita. No momento do preparo, logo depois da passada do rodado e antes que o implemento mobilizasse o solo, o solo foi avaliado em superfície (SP - 0,00 a 0,05 m) e na profundidade média de trabalho (PMT - 0,24 a 0,27 m). A influência da soleira dos implementos foi avaliada logo depois do corte, na profundidade de trabalho abaixo da soleira de cada implemento (PT-SI). Depois da colheita, as amostragens foram repetidas, o que permitiu verificar a influência dos tráfegos subseqüentes. A intensidade de tráfego do rodado e a ação da soleira dos implementos alteraram a compressibilidade, a densidade do solo, a porosidade e condutividade hidráulica do solo saturado do Latossolo Vermelho distrófico nas profundidades: superficial (SP), profundidade média de trabalho (PMT) e profundidade de corte dos implementos (PT-SI). de maneira geral, a passada do rodado traseiro aumentou os valores de pressão de preconsolidação (sigmap) do solo na superfície (SP), enquanto o tráfego subseqüente, necessário ao cultivo, elevou esses valores em subsuperfície, tanto na PMP como em PT-SI. A passada do rodado elevou e reduziu a densidade do solo (Ds) e a macroporosidade, respectivamente, tendo sido o arado de aivecas o implemento que mais modificou essas propriedades. As soleiras dos órgãos ativos do arado de discos e da grade aradora foram as que mais elevaram a pressão de preconsolidação (sigmap), consolidando, portanto, a estrutura do solo na profundidade PT-SI. Com exceção da semeadora/adubadora, a soleira dos órgãos ativos dos demais implementos avaliados reduziram significativamente a condutividade hidráulica do solo saturado (Ks) na profundidade PT-SI, tendo o arado de aivecas se mostrado o implemento mais impactante, seguido da grade aradora e arado de discos.

Avaliação dos atributos físicos e da compressibilidade de um nitossolo vermelho distroférrico sob sistemas de plantio direto, preparo convencional e mata nativa

Pós-graduação em Agronomia (Energia na Agricultura) - FCA

Traffic assignment. Predictive curves and their application, including machine procedures developed in the Detroit traffic study.

Cover title.

Soil compaction and eucalyptus growth in response to forwarder traffic intensity and load

During timber exploitation in forest stands harvesting machines pass repeatedly along the same track and can cause soil compaction, which leads to soil erosion and restricted tree root growth. The level of soil compaction depends on the number of passes and weight of the wood load. This paper aimed to evaluate soil compaction and eucalyptus growth as affected by the number of passes and wood load of a forwarder. The study was carried out in Santa Maria de Itabira county, Minas Gerais State - Brazil, on a seven-year-old eucalyptus stand planted on an Oxisol. The trees were felled by chainsaw and manually removed. Plots of 144 m² (four rows 12 m long in a 3 x 2 m spacing) were then marked off for the conduction of two trials. The first tested the traffic intensity of a forwarder which weighed 11,900 kg and carried 12 m³ wood (density of 480 kg m-3) and passed 2, 4, and 8 times along the same track. In the second trial, the forwarder carried loads of 4, 8, and 12 m³ of wood, and the machine was driven four times along the same track. In each plot, the passes affected four rows. Eucalyptus was planted in 30 x 30 x 30 cm holes on the compacted tracks. The soil in the area is clayey (470 clay and 440 g kg-1 sand content) and at depths of 0-5 cm and 5-10 cm, respectively, soil organic carbon was 406 and 272 g kg-1 and the moisture content during the trial 248 and 249 g kg-1. These layers were assessed for soil bulk density and water-stable aggregates. The infiltration rate was measured by a cylinder infiltrometer. After 441 days the measurements were repeated, with additional analyses of: soil organic carbon, total nitrogen, N-NH4+, N-NO3-, porosity, and penetration resistance. Tree height, stem diameter, and stem dry matter were measured. Forwarder traffic increased soil compaction, resistance to penetration and microporosity while it reduced the geometric mean diameter, total porosity, macroporosity and infiltration rate. Stem dry matter yield and tree height were not affected by soil compaction. Two passes of the forwarder were enough to cause the disturbances at the highest levels. The compaction effects were still persistent 441 days after forwarder traffic.

Automatic traffic sign inventory- and condition analysis

This thesis researches automatic traffic sign inventory and condition analysis using machine vision and pattern recognition methods. Automatic traffic sign inventory and condition analysis can be used to more efficient road maintenance, improving the maintenance processes, and to enable intelligent driving systems. Automatic traffic sign detection and classification has been researched before from the viewpoint of self-driving vehicles, driver assistance systems, and the use of signs in mapping services. Machine vision based inventory of traffic signs consists of detection, classification, localization, and condition analysis of traffic signs. The produced machine vision system performance is estimated with three datasets, from which two of have been been collected for this thesis. Based on the experiments almost all traffic signs can be detected, classified, and located and their condition analysed. In future, the inventory system performance has to be verified in challenging conditions and the system has to be pilot tested.

Evaluation of Machine Learning Classifiers for Mobile Network Intrusion Detection Systems

Mobile malwares are increasing with the growing number of Mobile users. Mobile malwares can perform several operations which lead to cybersecurity threats such as, stealing financial or personal information, installing malicious applications, sending premium SMS, creating backdoors, keylogging and crypto-ransomware attacks. Knowing the fact that there are many illegitimate Applications available on the App stores, most of the mobile users remain careless about the security of their Mobile devices and become the potential victim of these threats. Previous studies have shown that not every antivirus is capable of detecting all the threats; due to the fact that Mobile malwares use advance techniques to avoid detection. A Network-based IDS at the operator side will bring an extra layer of security to the subscribers and can detect many advanced threats by analyzing their traffic patterns. Machine Learning(ML) will provide the ability to these systems to detect unknown threats for which signatures are not yet known. This research is focused on the evaluation of Machine Learning classifiers in Network-based Intrusion detection systems for Mobile Networks. In this study, different techniques of Network-based intrusion detection with their advantages, disadvantages and state of the art in Hybrid solutions are discussed. Finally, a ML based NIDS is proposed which will work as a subsystem, to Network-based IDS deployed by Mobile Operators, that can help in detecting unknown threats and reducing false positives. In this research, several ML classifiers were implemented and evaluated. This study is focused on Android-based malwares, as Android is the most popular OS among users, hence most targeted by cyber criminals. Supervised ML algorithms based classifiers were built using the dataset which contained the labeled instances of relevant features. These features were extracted from the traffic generated by samples of several malware families and benign applications. These classifiers were able to detect malicious traffic patterns with the TPR upto 99.6% during Cross-validation test. Also, several experiments were conducted to detect unknown malware traffic and to detect false positives. These classifiers were able to detect unknown threats with the Accuracy of 97.5%. These classifiers could be integrated with current NIDS', which use signatures, statistical or knowledge-based techniques to detect malicious traffic. Technique to integrate the output from ML classifier with traditional NIDS is discussed and proposed for future work.

Advances in Model-Based Traffic Vision

Model based vision allows use of prior knowledge of the shape and appearance of specific objects to be used in the interpretation of a visual scene; it provides a powerful and natural way to enforce the view consistency constraint. A model based vision system has been developed within ESPRIT VIEWS: P2152 which is able to classify and track moving objects (cars and other vehicles) in complex, cluttered traffic scenes. The fundamental basis of the method has been previously reported. This paper presents recent developments which have extended the scope of the system to include (i) multiple cameras, (ii) variable camera geometry, and (iii) articulated objects. All three enhancements have easily been accommodated within the original model-based approach

Road and Traffic Signs Recognition using Vector Machines

Intelligent Transportation System (ITS) is a system that builds a safe, effective and integrated transportation environment based on advanced technologies. Road signs detection and recognition is an important part of ITS, which offer ways to collect the real time traffic data for processing at a central facility.This project is to implement a road sign recognition model based on AI and image analysis technologies, which applies a machine learning method, Support Vector Machines, to recognize road signs. We focus on recognizing seven categories of road sign shapes and five categories of speed limit signs. Two kinds of features, binary image and Zernike moments, are used for representing the data to the SVM for training and test. We compared and analyzed the performances of SVM recognition model using different features and different kernels. Moreover, the performances using different recognition models, SVM and Fuzzy ARTMAP, are observed.

Machine vision for the automatic classification of images acquired from Non-destructive tests

This project is based on Artificial Intelligence (A.I) and Digital Image processing (I.P) for automatic condition monitoring of sleepers in the railway track. Rail inspection is a very important task in railway maintenance for traffic safety issues and in preventing dangerous situations. Monitoring railway track infrastructure is an important aspect in which the periodical inspection of rail rolling plane is required.Up to the present days the inspection of the railroad is operated manually by trained personnel. A human operator walks along the railway track searching for sleeper anomalies. This monitoring way is not more acceptable for its slowness and subjectivity. Hence, it is desired to automate such intuitive human skills for the development of more robust and reliable testing methods. Images of wooden sleepers have been used as data for my project. The aim of this project is to present a vision based technique for inspecting railway sleepers (wooden planks under the railway track) by automatic interpretation of Non Destructive Test (NDT) data using A.I. techniques in determining the results of inspection.

Road Sign Recognition based onInvariant Features using SupportVector Machine

Since last two decades researches have been working on developing systems that can assistsdrivers in the best way possible and make driving safe. Computer vision has played a crucialpart in design of these systems. With the introduction of vision techniques variousautonomous and robust real-time traffic automation systems have been designed such asTraffic monitoring, Traffic related parameter estimation and intelligent vehicles. Among theseautomatic detection and recognition of road signs has became an interesting research topic.The system can assist drivers about signs they don’t recognize before passing them.Aim of this research project is to present an Intelligent Road Sign Recognition System basedon state-of-the-art technique, the Support Vector Machine. The project is an extension to thework done at ITS research Platform at Dalarna University [25]. Focus of this research work ison the recognition of road signs under analysis. When classifying an image its location, sizeand orientation in the image plane are its irrelevant features and one way to get rid of thisambiguity is to extract those features which are invariant under the above mentionedtransformation. These invariant features are then used in Support Vector Machine forclassification. Support Vector Machine is a supervised learning machine that solves problemin higher dimension with the help of Kernel functions and is best know for classificationproblems.