Effective Use of a Variable Speed Blower Fan on a Mechanical Wild Blueberry Harvester

2018 ◽  
Vol 34 (5) ◽  
pp. 831-840
Author(s):  
Karen Esau ◽  
Travis Esau ◽  
Qamar Zaman ◽  
Aitazaz Farooque ◽  
Arnold Schumann
Keyword(s):  
Real Time ◽  
Management Practices ◽  
Plant Density ◽  
Terminal Velocity ◽  
Variable Speed ◽  
Effective Use ◽  
Wild Blueberry ◽  
Storage Bins

Abstract. The management of wild blueberry fields is continuously improving and plant density/leaf foliage have increased. The result of improved management practices has led to an increased amount of debris being collected while harvesting. Many commercial harvester units contain a single blower fan to remove debris before the fruit enters the storage bins. Processing facilities are suggesting that producers reduce the excess debris that is being collected in the bins. Keywords: Anemometer, Debris, Optimization, Processing, Real-time, Terminal velocity.

scholarly journals An Automated Cost-effective System for Real-time Slope Mapping in Commercial Wild Blueberry Fields

2010 ◽  
Vol 20 (2) ◽  
pp. 431-437 ◽  
Author(s):  
Qamar Uz Zaman ◽  
Arnold Walter Schumann ◽  
David Charles Percival
Keyword(s):  
Real Time ◽  
Management Practices ◽  
Low Cost ◽  
Cost Effective ◽  
Vehicle Speed ◽  
Eastern Canada ◽  
Site Specific ◽  
Management Plans ◽  
Effective System ◽  
Wild Blueberry

The development of site-specific agriculture has increased the need for knowledge regarding within-field variability in factors such as soil/plant characteristics and topography that influence wild blueberry (Vaccinium angustifolium) production. Surface soil properties are the first type of information most frequently used by blueberry producers in developing management plans. Topographic features are not yet routinely used to guide within-field management. The majority of blueberry fields in eastern Canada have gentle to severe topography. An automated slope measurement and mapping system (SMMS) consisting of low-cost accelerometers used as tilt sensors, differential global positioning system (DGPS), and laptop and custom software was developed. The SMMS was mounted on an all-terrain vehicle for real-time slope measurement and mapping. Six commercial wild blueberry fields were surveyed in central Nova Scotia to evaluate the performance of SMMS. The automatically sensed slopes (SS) were also compared with manually measured slopes (MS) at 20 randomly selected points in each field to examine the accuracy of SMMS. The SMMS measured slope reliably in the selected fields with root mean square error ranging from 0.12 to 0.56 degrees and correlations of SS with MS of R2 = 0.95 to 0.99. The selected fields had substantial variation in slope (ranging from 0.8 to 31.0 degrees). Therefore, the use of low-cost and reliable accelerometers with a DGPS is a better option than expensive real-time kinematic DGPS for developing cost-effective SMMS to quantify and map slopes (real-time) for planning site-specific management practices in commercial fields. The SS maps or real-time SMMS could also be used to adjust vehicle speed at particularly steep slopes.

scholarly journals Comparison of Forecasting Models for Real-Time Monitoring of Water Quality Parameters Based on Hybrid Deep Learning Neural Networks

Water ◽  
2021 ◽  
Vol 13 (11) ◽  
pp. 1547
Author(s):  
Jian Sha ◽  
Xue Li ◽  
Man Zhang ◽  
Zhong-Liang Wang
Keyword(s):  
Neural Network ◽  
Water Quality ◽  
Deep Learning ◽  
Real Time ◽  
Best Management Practices ◽  
Input Data ◽  
Management Practices ◽  
Quality Parameters ◽  
Water Quality Parameters ◽  
Real Time Monitoring

Accurate real-time water quality prediction is of great significance for local environmental managers to deal with upcoming events and emergencies to develop best management practices. In this study, the performances in real-time water quality forecasting based on different deep learning (DL) models with different input data pre-processing methods were compared. There were three popular DL models concerned, including the convolutional neural network (CNN), long short-term memory neural network (LSTM), and hybrid CNN–LSTM. Two types of input data were applied, including the original one-dimensional time series and the two-dimensional grey image based on the complete ensemble empirical mode decomposition algorithm with adaptive noise (CEEMDAN) decomposition. Each type of input data was used in each DL model to forecast the real-time monitoring water quality parameters of dissolved oxygen (DO) and total nitrogen (TN). The results showed that (1) the performances of CNN–LSTM were superior to the standalone model CNN and LSTM; (2) the models used CEEMDAN-based input data performed much better than the models used the original input data, while the improvements for non-periodic parameter TN were much greater than that for periodic parameter DO; and (3) the model accuracies gradually decreased with the increase of prediction steps, while the original input data decayed faster than the CEEMDAN-based input data and the non-periodic parameter TN decayed faster than the periodic parameter DO. Overall, the input data preprocessed by the CEEMDAN method could effectively improve the forecasting performances of deep learning models, and this improvement was especially significant for non-periodic parameters of TN.

Assessment of sowing dates and plant densities using CSM-CROPGRO-Soybean for soybean maturity groups in low latitude

2021 ◽  
pp. 1-14
Author(s):  
L. S. Sampaio ◽  
R. Battisti ◽  
M. A. Lana ◽  
K. J. Boote
Keyword(s):  
Management Practices ◽  
Field Experiments ◽  
Plant Density ◽  
Crop Model ◽  
Management Scenarios ◽  
Low Latitude ◽  
Area Index ◽  
Sowing Dates ◽  
Plant Densities ◽  
Maturity Groups

Abstract Crop models can be used to explain yield variations associated with management practices, environment and genotype. This study aimed to assess the effect of plant densities using CSM-CROPGRO-Soybean for low latitudes. The crop model was calibrated and evaluated using data from field experiments, including plant densities (10, 20, 30 and 40 plants per m2), maturity groups (MG 7.7 and 8.8) and sowing dates (calibration: 06 Jan., 19 Jan., 16 Feb. 2018; and evaluation: 19 Jan. 2019). The model simulated phenology with a bias lower than 2 days for calibration and 7 days for evaluation. Relative root mean square error for the maximum leaf area index varied from 12.2 to 31.3%; while that for grain yield varied between 3 and 32%. The calibrated model was used to simulate different management scenarios across six sites located in the low latitude, considering 33 growing seasons. Simulations showed a higher yield for 40 pl per m2, as expected, but with greater yield gain increments occurring at low plant density going from 10 to 20 pl per m2. In Santarém, Brazil, MG 8.8 sown on 21 Feb. had a median yield of 2658, 3197, 3442 and 3583 kg/ha, respectively, for 10, 20, 30 and 40 pl per m2, resulting in a relative increase of 20, 8 and 4% for each additional 10 pl per m2. Overall, the crop model had adequate performance, indicating a minimum recommended plant density of 20 pl per m2, while sowing dates and maturity groups showed different yield level and pattern across sites in function of the local climate.

scholarly journals Enhancing Freeway Safety through Intervening in Traffic Flow Dynamics Based on Variable Speed Limit Control

2018 ◽  
Vol 2018 ◽  
pp. 1-10 ◽  
Author(s):  
Jinming You ◽  
Shouen Fang ◽  
Lanfang Zhang ◽  
John Taplin ◽  
Jingqiu Guo
Keyword(s):  
Real Time ◽  
Traffic Flow ◽  
New Technologies ◽  
Flow Dynamics ◽  
Crash Risk ◽  
Speed Limit ◽  
Variable Speed ◽  
Variable Speed Limit ◽  
Traffic Conditions ◽  
Safety Research

New technologies and traffic data sources provide great potential to extend advanced strategies in freeway safety research. The High Definition Monitoring System (HDMS) data contribute comprehensive and precise individual vehicle information. This paper proposes an innovative Variable Speed Limit (VSL) based approach to manage crash risks by intervening in traffic flow dynamics on freeways using HDMS data. We first conducted an empirical analysis on real-time crash risk estimation using a binary logistic regression model. Then, intensive microscopic simulations based on AIMSUN were carried out to explore the effects of various intervention strategies with respect to a 3-lane freeway stretch in China. Different speed limits with distinct compliance rates under specified traffic conditions have been simulated. By taking into account the trade-off between safety benefits and delay in travel time, the speed limit strategies were optimized under various traffic conditions and the model with gradient feedback produces more satisfactory performance in controlling real-time crash risks. Last, the results were integrated into lane management strategies. This research can provide new ideas and methods to reveal the freeway crash risk evolution and active traffic management.

Influence of Weed Management Practices and Crop Rotation on Glyphosate-Resistant Horseweed (Conyza canadensis) Population Dynamics and Crop Yield-Years III and IV

Weed Science ◽  
2009 ◽  
Vol 57 (4) ◽  
pp. 417-426 ◽  
Author(s):  
Vince M. Davis ◽  
Kevin D. Gibson ◽  
Thomas T. Bauman ◽  
Stephen C. Weller ◽  
William G. Johnson
Keyword(s):  
Population Dynamics ◽  
Crop Rotation ◽  
Crop Yield ◽  
Cover Crops ◽  
Weed Management ◽  
Management Practices ◽  
Plant Density ◽  
Crop Yields ◽  
Management Systems ◽  
No Till

Horseweed is an increasingly common and problematic weed in no-till soybean production in the eastern cornbelt due to the frequent occurrence of biotypes resistant to glyphosate. The objective of this study was to determine the influence of crop rotation, winter wheat cover crops (WWCC), residual non-glyphosate herbicides, and preplant application timing on the population dynamics of glyphosate-resistant (GR) horseweed and crop yield. A field study was conducted from 2003 to 2007 in a no-till field located at a site that contained a moderate infestation of GR horseweed (approximately 1 plant m−2). The experiment was a split-plot design with crop rotation (soybean–corn or soybean–soybean) as main plots and management systems as subplots. Management systems were evaluated by quantifying in-field horseweed plant density, seedbank density, and crop yield. Horseweed densities were collected at the time of postemergence applications, 1 mo after postemergence (MAP) applications, and at the time of crop harvest or 4 MAP. Viable seedbank densities were also evaluated from soil samples collected in the fall following seed rain. Soybean–corn crop rotation reduced in-field and seedbank horseweed densities vs. continuous soybean in the third and fourth yr of this experiment. Preplant herbicides applied in the spring were more effective at reducing horseweed plant densities than when applied in the previous fall. Spring-applied, residual herbicide systems were the most effective at reducing season-long in-field horseweed densities and protecting crop yields since the growth habit of horseweed in this region is primarily as a summer annual. Management systems also influenced the GR and glyphosate-susceptible (GS) biotype population structure after 4 yr of management. The most dramatic shift was from the initial GR : GS ratio of 3 : 1 to a ratio of 1 : 6 after 4 yr of residual preplant herbicide use followed by non-glyphosate postemergence herbicides.

scholarly journals Real time controlled sustainable urban drainage systems in dense urban areas

2019 ◽  
Vol 69 (3) ◽  
pp. 238-247 ◽  
Author(s):  
Nils Kändler ◽  
Ivar Annus ◽  
Anatoli Vassiljev ◽  
Raido Puust
Keyword(s):  
Urban Development ◽  
Real Time ◽  
Best Management Practices ◽  
Urban Areas ◽  
Management Practices ◽  
Low Impact Development ◽  
Economic Feasibility ◽  
Control Measures ◽  
Real Time Control ◽  
Urban Catchments

Abstract Stormwater runoff from urban catchments is affected by the changing climate and rapid urban development. Intensity of rainstorms is expected to increase in Northern Europe, and sealing off surfaces reduces natural stormwater management. Both trends increase stormwater peak runoff volume that urban stormwater systems (UDS) have to tackle. Pipeline systems have typically limited capacity, therefore measures must be foreseen to reduce runoff from new developed areas to existing UDS in order to avoid surcharge. There are several solutions available to tackle this challenge, e.g. low impact development (LID), best management practices (BMP) or stormwater real time control measures (RTC). In our study, a new concept of a smart in-line storage system is developed and evaluated on the background of traditional in-line and off-line detention solutions. The system is operated by real time controlled actuators with an ability to predict rainfall dynamics. This solution does not need an advanced and expensive centralised control system; it is easy to implement and install. The concept has been successfully tested in a 12.5 ha urban development area in Tallinn, the Estonian capital. Our analysis results show a significant potential and economic feasibility in the reduction of peak flow from dense urban areas with limited free construction space.

scholarly journals Financial Analysis of Water Management Practices in Cranberry Production in Quebec, Canada

2021 ◽  
Vol 10 (4) ◽  
pp. 13
Author(s):  
Ana-Maria Bogdan ◽  
Suren Kulshreshtha ◽  
Jean Caron
Keyword(s):  
Real Time ◽  
Management Practices ◽  
Net Present Value ◽  
Financial Analysis ◽  
Global Scale ◽  
Sensitivity Analyses ◽  
Economic Returns ◽  
Bottom Line ◽  
Agricultural Producers ◽  
Climate Change Effects

At a global scale, Canada is the second largest cranberry producer, with Quebec being the largest producing region within Canada. Efficient water use in agricultural production has long been a topic of outmost importance to agricultural producers, and governing bodies. The immediacy of climate change effects sped up the need to find solutions that conserve water. One such promising technology is irrigation using real-time tensiometers, which provides rapidly critical irrigation needs information to producers. Adoption of improved technologies by farmers is dependent on the effect it has on the farms’ bottom line. In this study, we examine the financial performance of real-time tensiometer based irrigation, and compare it to evaporation needs based irrigation (baseline), in the context of a Quebec-based cranberry farm. Our findings show that irrigating using real-time tensiometers technology generated higher economic returns. With a net present value of $96,847, this technology increased returns by nearly 53% compared to the baseline technology. Subsequent sensitivity analyses confirmed the robustness of these findings, even when changing important farming parameters.

scholarly journals Predicting Traffic Dynamics with Driver Response Model for Proactive Variable Speed Limit Control Algorithm

2018 ◽  
Vol 2018 ◽  
pp. 1-10
Author(s):  
Huixuan Ye ◽  
Lili Tu ◽  
Jie Fang
Keyword(s):  
Real Time ◽  
Traffic Flow ◽  
Control Algorithm ◽  
Field Data ◽  
Speed Limit ◽  
Response Model ◽  
Variable Speed ◽  
Variable Speed Limit ◽  
Driver Response ◽  
Traffic State

Variable Speed Limit (VSL) control contributes to potential crash risk reduction by suggesting a suitable dynamic speed limit to achieve more stable and uniform traffic flow. In recent studies, researchers adopted macroscopic traffic flow models and perform prediction-based optimal VSL control. The response of drivers to the advised VSL is one of the most critical parameters in VSL-controlled speed dynamics modeling, which significantly affects the accuracy of traffic state prediction as well as the control reliability and performance. Nevertheless, the variations of driver responses were not explicitly modeled. Thus, in this research, the authors proposed a dynamic driver response model to formulate how the drivers respond to the advised VSL during various traffic conditions. The model was established and calibrated using field data to quantitatively analyze the dynamics of drivers’ desired speed regarding the advised VSL and current traffic state variables. A proactive VSL control algorithm incorporating the established driver response model was designed and implemented in field-data-based simulation study. The design proactive control algorithm modifies VSL in real-time according to the traffic state prediction results, aiming to reduce potential crash risks over the experiment site. By taking into account the real-time driver response variations, the VSL-controlled traffic state dynamics was more accurately predicted. The experimental results illustrated that the proposed control algorithm effectively reduces the crash probabilities in the traffic network.

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