Proceedings
Authors
| Filter results10 paper(s) found. |
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1. Study on Water Distribution Measurement in Sand Using Sound Vibration... T. Sugimoto, T. Shirakawa, M. Sano, M. Ohaba, S. Shibusawa, Y. Nakagawa |
2. Transient Water Flow Model in a Soil-Plant System for Subsurface Precision IrrigationThe 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 |
3. Water Distribution Response in a Soil-Root System for Subsurface Precision IrrigationA 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 |
4. Adaptive Control of Capillary Water Flow Under Modified Subsurface Irrigation Based on a SPAC ModelSoil 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 |
5. Rhizosphere Moisture Modulation By Water Head Precision ControlAbstract: A digital irrigation microcomputer system, designed to modulate rhizosphere moisture using a... M. Ohaba, S. Shibusawa |
6. Study On Plant Health Condition Monitoring Using Acoustic Radiation ForceIn recent years, irrigation method using the negative pressure difference attracts attention from the point of view of water saving. In addition, it is proved that this technique is effective in upbringing of the plant as well as saving of water. By measuring water distribution of soil, active irrigation control will be performed In our previous study, we confirmed that the resonance frequency of a leaf is influenced by the water stress to the plant. Thus the vibration measurement... Y. Nakagawa, M. Sano, T. Shirakawa, K. Yamagishi, T. Sugihara, M. Ohaba, S. Shibusawa, T. Sugimoto |
7. Data Clustering Tools for Understanding Spatial Heterogeneity in Crop Production by Integrating Proximal Soil Sensing and Remote Sensing DataRemote sensing (RS) and proximal soil sensing (PSS) technologies offer an advanced array of methods for obtaining soil property information and determining soil variability for precision agriculture. A large amount of data collected using these sensors may provide essential information for precision or site-specific management in a production field. In this paper, we introduced a new clustering technique was introduced and compared with existing clustering tools for determining relatively homogeneous... M. Saifuzzaman, V.I. Adamchuk, H. Huang, W. Ji, N. Rabe, A. Biswas |
8. Analysis of Soil Properties Predictability Using Different On-the-Go Soil Mapping SystemsUnderstanding the spatial variability of soil chemical and physical attributes allows for the optimization of the profitability of nutrient and water management for crop development. Considering the advantages and accessibility of various types of multi-sensor platforms capable of acquiring large sensing data pertaining to soil information across a landscape, this study compares data obtained using four common soil mapping systems: 1) topography obtained using a real-time kinematic (RTK) global... H. Huang, V. Adamchuk, A. Biswas, W. Ji, S. Lauzon |
9. Cyberinfrastructure for Machine Learning Applications in Agriculture: Experiences, Analysis, and VisionAdvancements in machine learning algorithms and GPU computational speeds over the last decade have led to remarkable progress in the capabilities of machine learning. This progress has been so much that, in many domains, including agriculture, access to sufficiently diverse and high-quality datasets has become a limiting factor. While many agricultural use cases appear feasible with current compute resources and machine learning algorithms, the lack of software infrastructure for collecting,... L. Waltz, S. Khanal, S. Katari, C. Hong, A. Anup, J. Colbert, A. Potlapally, T. Dill, C. Porter, J. Engle, C. Stewart, H. Subramoni, R. Machiraju, O. Ortez, L. Lindsey, A. Nandi |
10. Crop and Water Monitoring Networks with Low-cost, Internet of Things TechnologyMaking meaningful changes in agroecosystems often requires the ability to monitor many environmental parameters to accurately identify potential areas for improvement in water quality and crop production. Increasingly, research questions are requiring larger and larger monitoring networks to draw applicable insights for both researchers and producers. However, acquiring enough sensors to address a particular research question is often cost-prohibitive, making it harder to draw meaningful conclusions... A.J. Brown, E. Deleon, E. Wardle |