Authors
| Filter results13 paper(s) found. |
|---|
1. Towards a Multi-Source Record Keeping System for Agricultural Product TraceabilityAgricultural production record keeping is the basis of traceability system. To resolve the problem including single method of information acquisition, weak ability of real-time monitoring and low credibility of history information in agricultural production process, the... C. Sun, Z. Ji, J. Qian, M. Li, L. Zhao, W. Li, C. Zhou, X. Du, J. Xie, T. Wu, L. Qu, L. Hao, X. Yang |
2. Thermography as Sensor for Downy Mildew on RosesDowny mildew caused by Peronospora sparsa is considered one of the most important diseases affecting cut roses under glass in the tropic. Under favorable... E. Oerke, H. Dehne, U. Steiner, S. Gómez |
3. 3-Dimension Reconstruction Of Cactus Using Multispectral ImagesUsing 3D reconstruction result to investigate plant morphology has been a focus of virtual plant. And multispectral imaging has proved to carried biological information in quite a lot work. This paper present a idea to investigate chlorophyll spatial variability of cactus using a bunch of multispectral images. 46 multispectral images are taken at equally distributed angles surrounding the tree and have over 80% overlap. Structure from motion approach has been used... F. Liu, Y. He, Y. Zhang, L. Tan, Y. Zhang, L. Jiang |
4. Application of Semantic Sensor Web in AgricultureIn July 2013, heavy rainstorms across the Midwestern region of the US caused many rivers to breach their banks. Residents of Valley Park, a small town along the Meramec River, Missouri, had to decide whether to rely on a newly constructed levee or abandon their homes for higher ground. Although the levee held, many chose the latter option and fled their homes; it was a chaotic situation that might have been avoided through access to better situational knowledge... Y. Zhang, T. Chen |
5. Spatial-temporal Evaluation of Plant Phenotypic Traits Via Imagery Collected by Unmanned Aerial Systems (UAS)Unmanned aerial systems (UAS) and a stereovision approach were implemented to generate a 3D reconstruction of the top of the canopy. The 3D reconstruction or CSM (crop surface model) was utilized to evaluate biophysical parameters for both spatial- and temporal-scales. The main goal of the project was to evaluate sUAVs technology to assist plant height and biomass estimation. The main outcome of this process was to utilize CSMs to gain insights in the spatial-temporal dynamic of plants within... S. Varela, G. Balboa, V. Prasad, T. Griffin, I. Ciampitti, A. Ferguson |
6. Spatial-temporal Evaluation of Plant Phenotypic Traits Via Imagery Collected by Unmanned Aerial Systems (UAS)Unmanned aerial systems (UAS) and a stereovision approach were implemented to generate a 3D reconstruction of the top of the canopy. The 3D reconstruction or CSM (crop surface model) was utilized to evaluate biophysical parameters for both spatial- and temporal-scales. The main goal of the project was to evaluate sUAVs technology to assist plant height and biomass estimation. The main outcome of this process was to utilize CSMs to gain insights in the spatial-temporal dynamic of plants within... S. Varela, G. Balboa, V. Prasad, T. Griffin, I. Ciampitti, A. Ferguson |
7. On Farm Studies to Determine Seeding Rate in CornSeeding rate (SDR) is one of the most critical production practices impacting productivity and economic return for corn (Zea mays L.) By changing SDRs in different zones within a field, herein termed as site-specific management, better economic results can be produced as the outcome of reducing SDRs in low productivity areas and increasing SDRs under high-yielding environments, relative to the uniform SDR management performed by the producer. The aim of this study was to analyze yield responses... G. Balboa, S. Varela, I. Ciampitti, S. Duncan, T. Maxwell, D. Shoups, A. Sharda |
8. Real-time Gauge Wheel Load Variability on Planter with Downforce Control During Field OperationDownforce control allows planters to maintain gauge wheel load across a range of soil resistance within a field. Downforce control is typically set for a target seed depth and either set to manually or automatically control the gauge wheel load. This technology uses load cells to actively regulate downforce on individual row units by monitoring target load on the gauge wheels. However, no studies have been conducted to evaluate the variability in gauge wheel load observed during planter operation... A. Sharda, S. Badua, D. Flippo, I. Ciampitti, T.W. Griffin |
9. Rationale for and Benefits of a Community for On-Farm Data SharingMost data sets for evaluating crop production practices have too few locations and years to create reliable probabilities from predictive analytical analyses for the success of the practices. Yield monitors on combines have the potential to enable networks of farmers in collaboration with scientists and farm advisors to collect sufficient data for calculation of more reliable guidelines for crop production showing the probabilities that new or existing practices will improve the efficiency of... T. Morris, N. Tremblay, P.M. Kyveryga, D.E. Clay, S. Murrell, I. Ciampitti, L. Thompson, D. Mueller, J. Seger |
10. Influence of Planter Downforce Setting and Ground Speed on Seeding Depth and Plant Spacing Uniformity of CornUniform seed placement improves seed-to-soil contact and requires proper selection of downforce control across varying field conditions. At faster ground speeds, downforce changes and it becomes critical to select the level of planter downforce settings to achieve the desired consistency of seed placement during planting. The objective of this study was to assess the effect of ground speed and downforce setting on seeding depth and plant spacing and to evaluate the relationship of ground speed... A. Sharda, S. Badua, I. Ciampitti, R. Strasser, T.W. Griffin |
11. Spotweeds: a Multiclass UASs Acquired Weed Image Dataset to Facilitate Site-specific Aerial Spraying Application Using Deep LearningUnmanned aerial systems (UASs)-based spot spraying application is considered a boon in Precision Agriculture (PA). Because of spot spraying, the amount of herbicide usage has reduced significantly resulting in less water contamination or crop plant injury. In the last demi-decade, Deep Learning (DL) has displayed tremendous potential to accomplish the task of identifying weeds for spot spraying application. Also, most of the ground-based weed management technologies have relied on DL techniques... N. Rai, Y. Zhang, J. Quanbeck, A. Christensen, X. Sun |
12. Analytical and Technological Advancements for Soybean Quality Mapping and Economic DifferentiationIn the past, measuring soybean protein and oil content required the collection of soybean seed samples and laboratory analyses. Modern on-the-go near-infrared (NIR) sensing technologies during the harvest and proximal remote sensing (aerial and satellite imagery) before harvest time can be used to provide an early estimate of seed quality levels, benchmark in-season predictions with at-harvest final seed quality and enable seed differentiation for farmers leading to better marketing strategies. Recent... A. Prestholt, C. Hernandez, I. Ciampitti , P. Kyveryga |
13. In-season Diagnosis of Winter Wheat Nitrogen Status Based on Rapidscan Sensor Using Machine Learning Coupled with Weather DataNitrogen nutrient index (NNI) is widely used as a good indicator to evaluate the N status of crops in precision farming. However, interannual variation in weather may affect vegetation indices from sensors used to estimate NNI and reduce the accuracy of N diagnostic models. Machine learning has been applied to precision N management with unique advantages in various variables analysis and processing. The objective of this study is to improve the N status diagnostic model for winter wheat by combining... J. Lu, Z. Chen, Y. Miao, Y. Li, Y. Zhang, X. Zhao, M. Jia |