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Adams, C
Kwon, H
Abdollahi, J.M
White, S.N
Hagymássy, Z
Maja, J.J
Karn, R
Gal, A
Kizer, E
Zillmann, E
Kodaira, M
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Authors
Tumenjargal, E
Badarch, L
Ham, W
Kwon, H
Tumenjargal, E
Badarch, L
Ham, W
Kwon, H
Arzani, H.P
Azimi, M.S
Kaboli, S.D
Mirdavodi, H.M
Borhani, M.M
Abdollahi, J.M
Farahpour, M.D
Kanda, R
Kodaira, M
Shibusawa, S
Shibusawa, S
Ninomiya, K
Kodaira, M
Zainal Abidin, M.B
Shibusawa, S
Ohaba, M
Li, Q
Kodaira, M
Khalid, M.B
Shibusawa, S
Ohaba, M
Zainal Abidin, M.B
Kodaira, M
Li, Q
Ohaba, M
Zainal Abidin, M.B
Li, Q
Shibusawa, S
Kodaira, M
Osato, K
Fusamura, R
Shibusawa, S
Kodaira, M
Sulastri, N
Shibusawa, S
Kodaira, M
Kodaira, M
NAGAMI, Y
Shibusawa, S
KANDA, R
Kaho, T
Kodaira, M
Shibusawa, S
Kodaira, M
Shibusawa, S
Ninomiya, K
Kodaira, M
Shibusawa, S
Shibusawa, S
Kodaira, M
Kana, I
Baharom, S.N
Umeda, H
Shibusawa, S
Li, Q
Usui, K
Kodaira, M
Shibusawa, S
Umeda, H
Usui, K
Kodaira, M
Li, Q
Kizer, E
Upadhyaya, S.K
Rojo, F
Ozmen, S
Ko-Madden, C
Zhang, Q
Frotscher, K.J
Schacht, R
Smith, L
Zillmann, E
Kodaira, M
Shibusawa, S
Li, Q
Sugihara, T
Kodaira, M
Shibusawa, S
Nándor, C
Rátonyi, T
Harsányi, E
Ragán, P
Hagymássy, Z
Nagy, J
Vántus, A
Adams, C
Coates, A
Hennessy, P.J
Esau, T.J
Schumann, A.W
Farooque, A.A
Zaman, Q.U
White, S.N
Karn, R
Gu, H
Adedeji, O
Guo, W
Adedeji, O.I
Ghimire, B.P
Gu, H
Karn, R
Lin, Z
Guo, W
Maja, J.J
Abenina, M
Cutulle, M
Melgar, J
Liu, H
Jha, G
Nazrul, F
Nocco, M
Pagé Fortin, M
Whitaker, B
Diaz, D
Gal, A
Schmidt, R
Dey, S
Karn, R
Adedeji, O
Ghimire, B.P
Abdalla, A
Sheng, V
Ritchie, G
Guo, W
Adedeji, O
Guo, W
Alwaseela, H
Ghimire, B
Wieber, E
Karn, R
Ghimire, B
Karn, R
Adedeji, O
Ritchie, G
Guo, W
Ghimire, B
Karn, R
Adedeji, O
Guo, W
Adedeji, O
Karn, R
Ghimire, B.P
Guo, W
Wieber, E.N
Topics
Engineering Technologies and Advances
Guidance, Robotics, Automation, and GPS Systems
Spatial Variability in Crop, Soil and Natural Resources
Proximal Sensing in Precision Agriculture
Modeling and Geo-statistics
Spatial Variability in Crop, Soil and Natural Resources
Engineering Technologies and Advances
Proximal Sensing in Precision Agriculture
Sensor Application in Managing In-season CropVariability
Spatial Variability in Crop, Soil and Natural Resources
Proximal Sensing in Precision Agriculture
Remote Sensing Applications in Precision Agriculture
Proximal and Remote Sensing of Soil and Crop (including Phenotyping)
Drainage Optimization and Variable Rate Irrigation
Precision Dairy and Livestock Management
Applications of Unmanned Aerial Systems
Big Data, Data Mining and Deep Learning
Applications of Unmanned Aerial Systems
Proximal and Remote Sensing of Soil and Crop (including Phenotyping)
Weather and Models for Precision Agriculture
Precision Agriculture and Global Food Security
Drainage Optimization and Variable Rate Irrigation
Decision Support Systems
Precision Agriculture for Sustainability and Environmental Protection
Type
Poster
Oral
Year
2012
2010
2014
2016
2018
2022
2024
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Filter results33 paper(s) found.

1. On The Go Soil Sensor For Soil Ec Mapping

This paper describes spatial variation maps of soil electrical conductivity (EC) obtained by both spectroscopic and capacitance methods using on the go soil sensor ( a real-time soil sensor -RTSS) SAS 1000, commercialized by Shibuya Kogyo Co. The experiments were conducted over a 2 year period on an experimental Hokkaido farm with an alluvial soil type. The comparison in soil EC records between the spectroscopy and the capacitance were also discussed. The spectroscopic approach used the soil... N. Sulastri, S. Shibusawa, M. Kodaira

2. The Soil P2O5 Mapping Using The Real Time Soil Sensor

    Many researches related to P­2O5 measurement using Vis-NIR spectroscopy have been performed in laboratory. There are not so many researches to perform on-the-go measurement of P­2O5. One of the researches which performed... M. Kodaira, Y. Nagami, S. Shibusawa, R. Kanda

3. Prediction Of Soil Moisture Content And Penetration Resistance Using Real-time Soil Meter

A real-time soil compaction meter that refers to the air injection subsoiler, is developed.  The final goal is to predict standarized soil compaction that is converted from soil moisture content, working resistance and working speed.  This experiment confirmed performance of predicting the soil moisture content and of measuring the working resistance was conducted.  The equipments of the meter are a working resistance measurement device received from the soil and a spectroscope... T. Kaho, M. Kodaira, S. Shibusawa

4. Dozen Parameters Soil Mapping Using The Real-time Soil Sensor

 A Real-time soil sensor (RTSS) can be predicted soil parameters using near-infrared underground soil reflectance sensor in commercial farms. ... M. Kodaira, S. Shibusawa, K. Ninomiya

5. Implementation of ECU For Agricultural Machines Based On IsoAgLib Open Source

In this paper work, we consider implementation of electronic control unit (ECU) for agricultural machineries. Software implementation is based on IsoAgLib library developed by OSB&IT Engineering Company. We modify IsoAgLib and upgrade it for our target system. The IsoAgLib is an object oriented C++ library that has the communication services and management systems according to the ISO 11783 standard. This library allows building ISOBUS compatible equipment without the protocols implementation... E. Tumenjargal, L. Badarch, W. Ham, H. Kwon

6. Design and Implementation of Virtual Terminal Based On ISO11783 Standard for Agricultural Tractors

The modern agricultural machinery most common use of the embedded electronic and remote sensing technology demands adoption of the Precision Agriculture (PA). One of the common devices is the Virtual Terminal (VT) for tractor. The VT’s functions and terminology are described in the ISO11783 standard. This work presents the control system design and implementation of the VT and some Electronic Control Units (ECU) for agricultural vehicles based on the ISO 11783 standard. The VT development... E. Tumenjargal, L. Badarch, W. Ham, H. Kwon

7. Application of RS, GPS & GIS in a National Monitoring System for Accurate Range Assessment

Sustainable use of rangelands requires information on vegetation cover and its changes through time, condition trend and the effect of climate as well as management practices. The main objective of this research was showing variation of vegetation parameters,... H.P. Arzani, M.S. Azimi, S.D. kaboli, H.M. mirdavodi, M.M. Borhani, J.M. Abdollahi, M.D. farahpour

8. Measuring Error on Working Depth of Real-time Soil Sensor

This paper described about the measuring error on working depth of the Real-time soil sensor (RTSS). It is necessary for accurately evaluating to observe the variation on the working depth, because the RTSS run in various real field conditions, such as soft or hard and even or uneven, and the RTSS has various using objective. In this paper, the RTSS run on asphalt with steps while the three-point hitch was free and position-controlled. In position-controlled, the measuring depth that is the... R. Kanda, M. Kodaira, S. Shibusawa

9. Nineteen-Soil-Parameter Calibration Models and Mapping for Upland Fields Using the Real-Time Soil Sensor

In precision agriculture, rapid, non-destructive, cost-effective and convenient soil analysis techniques are needed for soil management, crop quality control using fertilizer, manure and compost, and variable-rate input for soil... S. Shibusawa, K. Ninomiya, M. Kodaira

10. Transient Water Flow Model in a Soil-Plant System for Subsurface Precision Irrigation

The spatial variability of plant-water characteristic in the soil is still unclear. This limits the attempt to model the soil-plant-atmosphere system with this factor. Understanding the non-steady water flow along the soil-plant component is essential to understand their spatial variability.... M.B. Zainal abidin, S. Shibusawa, M. Ohaba, Q. Li, M. Kodaira, M.B. Khalid

11. Water Distribution Response in a Soil-Root System for Subsurface Precision Irrigation

A subsurface capillary irrigation system with a water source buried in a soil has been developed for precision irrigation. This system has advantages in the efficient irrigation to save much water and the real time measurement of evapotranspiration of plants. Creating this new subsurface capillary... S. Shibusawa, M. Ohaba, M.B. Zainal abidin, M. Kodaira, Q. Li

12. Adaptive Control of Capillary Water Flow Under Modified Subsurface Irrigation Based on a SPAC Model

Soil moisture in a rhizosphere of a tomato is controlled adaptively based on a simple soil-plant-atmosphere continuum (SPAC) model. The water flow from a soil through a plant to the atmosphere is governed by the analogous rule of the SPAC model. In our experiment, we assume that plant transpiration is only affected by the water-potential of air when the soil moisture... M. Ohaba, M.B. zainal abidin, Q. Li, S. Shibusawa, M. Kodaira, K. Osato

13. An Approach to Making Non-Smell Composting System : Case Study in Fuchu

The project to form ... R. Fusamura, S. Shibusawa, M. Kodaira

14. Soil Mapping And Modeling On Twenty-Five Ingredients Using A Real-Time Soil Sensor

Visible and near-infrared spectroscopy is an effective measurement method for estimating many soil ingredients at once. In precision agriculture, rapid, non-destructive, cost-effective and convenient soil analysis techniques are needed for soil management, crop quality control using fertilizer, manure and compost, and variable-rate input for soil variability in a field. We obtained Twenty-five calibration models based on Vis-NIR (305 - 1700 nm) underground soil reflectance... M. Kodaira, S. Shibusawa

15. Comparison Of Calibration Models Developed For A Visible-Near Infrared Real-Time Soil Sensor

The visible-near infrared (Vis-NIR) based real-time soil sensor (RTSS) is found to be a great tool for determining distribution of various soil properties for precision agriculture purposes. However, the developed calibration models applied on the collected spectra for prediction of soil properties were site-specific (local). This is found to be less practical since the RTSS needs to be calibrated separately for every field. General calibration approach is expected to minimize... S. Shibusawa, M. Kodaira, I. Kana, S.N. Baharom

16. 3D Map in the Depth Direction of Field for Precision Agriculture

 By a change in eating habits with economic development and the global population growth, we have been faced with the need for increased food production again. In order to solve the food problem in the future, the introduction of agriculture organization is progressing in emerging countries as well as developed countries. However, the occurrence of natural disasters and abnormal weather, which is becoming a worldwide problem at present, is further weakening the crops of farm... H. Umeda, S. Shibusawa, Q. Li, K. Usui, M. Kodaira

17. Using A Potable Spectroradiometer For In-Situ Measurement Of Soil Properties In A Slope Citrus Field

     In precision agriculture, rapid, non-destructive, cost-effective and convenient soil analysis techniques are needed for crop and soil management. However, the spatial variability of soil properties is consider to be high cost and time consuming to characterize using traditional soil analysis method. To achieve cost and time reduction, the potential benefits of in-situ measurement of soil spectra have been recognized.     ... S. Shibusawa, H. Umeda, K. Usui, M. Kodaira, Q. Li

18. Proximal Sensing of Leaf Temperature and Microclimatic Variables to Implement Precision Irrigation in Almond and Grape Crops

Irrigation decisions based on traditional soil moisture sensing often leads to uncertainty regarding the true amount of water available to the plant. Plant based sensing of water stress decreases this uncertainty. In specialty crops grown in California’s Central Valley, precision deficit irrigation based on plant water stress could be used to decrease water use and increase water use efficiency by supplying the necessary quantity of water only when it is needed by the plant. However, there... E. Kizer, S.K. Upadhyaya, F. Rojo, S. Ozmen, C. Ko-madden, Q. Zhang

19. Planet Labs' Monitoring Solution in Support of Precision Agriculture Practices

Satellite imagery is particularly useful for efficiently monitoring very large areas and providing regular feedback on the status and productivity of agricultural fields. These data are now widely used in precision farming; however, many challenges to making optimal use of this technology remain, such as easy access to data, management and exploitation of large datasets with deep time series, and sharing of the data and derived analytics with users. Providing satellite imagery through a cloud... K.J. Frotscher, R. Schacht, L. Smith, E. Zillmann

20. Two-Layer Multiple Soil-Property Mapping Measured with a Real-Time Soil Sensor

We obtained calibration models for 32 soil properties based on Vis-NIR (350 - 1700 nm) underground soil diffuse reflectance spectra collected using a real-time soil sensor (SAS3000) with a DGPS system, in order to generate soil property maps. We have previously demonstrated one-layer soil maps for soil management decision making by growers; however, for effective crop management, growers often wish to obtain complex layer information for their fields. Thus, in the present study, we measured two-layer... M. Kodaira, S. Shibusawa

21. Water Use Efficiency of Precision Irrigation System Under Critical Water-Saving Condition

Non-transpiration water loss is often neglected when evaluating water use efficiency (WUE) of precision irrigation system, due to the difficulties in determining water loss from the root zone. The objective of this study is to investigate the feasibility of a new water saving approach by controlling soil water retention around root zone during the plant growth. We grew two tomato cultivars (Anemo, Japanese variety) in an environmental controlled growth chamber, with previously oven dried and sieved... Q. Li, T. Sugihara, M. Kodaira, S. Shibusawa

22. The Spread of Precision Livestock Farming Technology at Dairy Farms in East Hungary

During the survey, 25 dairy farms were examined in East Hungary in Hajdú-Bihar (H-B) County between 2017 and 2018 by methodical observation and oral interviews with the farm managers, about the spread of Precision Livestock Farming (PLF) technologies. Among Holstein Friesian dairy farms in the County 60% were questioned, and the representativity was above 47 percent ins each size category. Nine precision farming equipment were examined on the farms: milking robot or robotic carousel milking... C. Nándor, T. Rátonyi, E. Harsányi, P. Ragán, Z. Hagymássy, J. Nagy, A. Vántus

23. Using UAV Imagery for Crop Analytics

UAV imagery was collected in April and July of 2017 over a grape vineyard in California’s San Joaquin Valley. Using spectral signatures, a landcover classification was performed to isolate table grapes from the background vegetation and soil. A novel vegetation index was developed based off the unique spectral characteristics of the yellowing effects of chlorosis within the table grape vines. Spatial statistics were run only on the pixels containing grape plants, and a relative vegetation... C. Adams, A. Coates

24. Meta Deep Learning Using Minimal Training Images for Weed Classification in Wild Blueberry

Deep learning convolutional neural networks (CNNs) have gained popularity in recent years for their ability to classify images with high levels of accuracy. In agriculture, they have been applied for disease identification, crop growth monitoring, animal behaviour tracking, and weed classification. Datasets traditionally consisting of thousands of images of each desired target are required to train CNNs. A recent survey of Nova Scotia wild blueberry (Vaccinium angustifolium Ait.) fields,... P.J. Hennessy, T.J. Esau, A.W. Schumann, A.A. Farooque, Q.U. Zaman, S.N. White

25. Evaluation of Unmanned Aerial Vehicle Images in Estimating Cotton Nitrogen Content

Estimating crop nitrogen content is a critical step for optimizing nitrogen fertilizer application. The objective of this study was to evaluate the application of UAV images in estimating cotton (Gossypium hirsutum L.) N content. This study was conducted in a dryland cotton field in Garza County, Texas, in 2020. The experiment was implemented as a randomized complete block design with three N rates of 0, 34, and 67 kg N ha-1. A RedEdge multispectral sensor was used to acquire... R. Karn, H. Gu, O. Adedeji, W. Guo

26. Estimation of Cotton Biomass Using Unmanned Aerial Systems and Satellite-based Remote Sensing

Satellite and unmanned aerial system (UAS) images are effective in monitoring crop growth at various spatial, temporal, and spectral scales. The objective of the study was to estimate cotton biomass at different growth stages using vegetation indices (VIs) derived from UAS and satellite images. This research was conducted in a cotton field in Hale County, Texas, in 2021. Data collected include 54 plant samples at different locations for three dates of the growing season. Multispectral images from... O.I. Adedeji, B.P. Ghimire, H. Gu, R. Karn, Z. Lin, W. Guo

27. Snap-shot Hyperspectral Camera for Potassium Prediction of Peach Trees Using Multivariate Analysis

Hyperspectral imaging (HSI) is an emerging technology being utilized in agriculture. This system could be used to monitor the overall health of plants or pest disease detection. As sensing technology advances, measuring nutrient levels and disease detection also progresses. This study aimed to predict the levels of potassium (K) content in peach leaves with the new snapshot hyperspectral camera. The study was conducted at the Clemson University Musser Fruit Research Farm (Seneca, SC, USA, 34.61... J.J. Maja, M. Abenina, M. Cutulle, J. Melgar, H. Liu

28. Prediction of Field-scale Evapotranspiration Using Process Based Modeling and Geostatistical Time-series Interpolation

Irrigation scheduling depends on the combination of evaporative demand from the atmosphere, spatial and temporal heterogeneity in soil properties and changes in crop canopy during a growing season. This on-farm trial is based on data collected in 72-acre processing tomato field in Central Valley of California. The Multiband Spectrometric Arable Mark 2 sensors at three different locations in the field. Multispectral and thermal imagery provided by Ceres Imaging were collected eight times during... G. Jha, F. Nazrul, M. Nocco, M. Pagé fortin, B. Whitaker, D. Diaz, A. Gal, R. Schmidt

29. Within Field Cotton Yield Prediction Using Temporal Satellite Imagery Combined with Deep Learning

Crop yield prediction at the field scale plays a pivotal role in enhancing agricultural management, a vital component in addressing global food security challenges. Regional or county-level data, while valuable for broader agricultural planning, often lacks the precision required by farmers for effective and timely field management. The primary obstacle in utilizing satellite imagery to forecast crop yields at the field level lies in its low temporal and spatial resolutions. This study aims to... R. Karn, O. Adedeji, B.P. Ghimire, A. Abdalla, V. Sheng, G. Ritchie, W. Guo

30. Assessing Precision Water Management in Cotton Using Unmanned Aerial Systems and Satellite Remote Sensing

The goal of this study was to improve agricultural sustainability and water use efficiency by allocating the right amount of water at the right place and time within the field. The objectives were to assess the effect of variable rate irrigation (VRI) on cotton growth and yield and evaluate the application of satellites and Unmanned aerial systems (UAS) in capturing the spatial and temporal patterns of cotton growth response to irrigation. Irrigation treatments with six replications of three different... O. Adedeji, W. Guo, H. Alwaseela, B. Ghimire, E. Wieber, R. Karn

31. Simulating Climate Change Impacts on Cotton Yield in the Texas High Plains

Crop yield prediction enables stakeholders to plan farming practices and marketing. Crop models can predict crop yield based on cropping system and practices, soil, and other environmental factors. These models are being used for decision support in agriculture in a variety of ways. Cultivar selection, water and nutrient input optimization, planting date selection, climate change analysis and yield prediction are some of the promising area of applications of the models in field level farm management.... B. Ghimire, R. Karn, O. Adedeji, G. Ritchie, W. Guo

32. Predicting Within-field Cotton Yield Variability Using DSSAT for Decision Support in Precision Agriculture

The quantification of spatial and temporal variability of cotton (Gossypium hirsutum L.)  yield provides critical information for optimizing resources, especially water, in the Southern High Plains (SHP), Texas, with a diminishing water supply. The within-field yield variation is mostly influenced by the properties of soil and their interaction with water and nutrients. The objective of this study was to predict within-field cotton yield variability using a crop growth model... B. Ghimire, R. Karn, O. Adedeji, W. Guo

33. Evaluating the Impact of Irrigation Rate, Timing, and Maturity-based Cotton Cultivars on Yield and Fiber Quality in West Texas

In West Texas, effective irrigation is crucial for sustainable cotton production given the water scarcity from the declining Ogallala aquifer and erratic rainfall patterns. A three-year study (2020-2022) investigated irrigation rate and timing effects on early to mid-season cotton maturity groups. Five treatments, including rainfed (W1 or LLL) and variations in irrigation rates at growth stages (P1 to P4), were applied. Evaluation involved six to seven cotton cultivars from four maturity groups,... O. Adedeji, R. Karn, B.P. Ghimire, W. Guo, E.N. Wieber