Proceedings
Authors
| Filter results8 paper(s) found. |
|---|
1. Using Soil Attributes To Model Sugar Cane Quality ParametersThe crop area of sugar cane production in Brazil has increased substantially in the last few years, especially to meet the global bioethanol demand. Such increasing production should take place not only in new sugar cane crop areas but mainly with the goal of improving the quality of raw material like sugar content (Pol). Hence, models that can describe the behaviour of the quality parameters of sugar cane may be important to understand the effects of the soil attributes on those parameters. The... F.A. Rodrigues jr., P.S. Magalhães, H.C. Franco, D.G. Cerri |
2. Winter Wheat Growth Uniformity Monitoring Through Remote Sensed Images... X. Song, C. Zhao, L. Chen, W. Huang, B. Cui |
3. Basic Tests Of pH And EC Probes For Automatic Real Time Nutrient Control In Protected Crop ProductionResearch on greenhouse and plant factory has been actively conducting to provide a stable growth environment. In plant factory, EC concentration (EC) and acidity (pH) of nutrient have a significant impact on physiological and morphological of plant. Therefore, EC and pH are important element for automatic control of nutrient solution. In this study, performance pH and EC sensors was evaluated for the responsiveness, accuracy and displacement. This study includes development of environmental... Y. Choo, S. Chung, Y. Huh, Y. Kim, S. Jang, K. Jung |
4. In-field Plant Phenotyping Using Multi-view Reconstruction: an Investigation in EggplantRapid methods for plant phenotyping are a growing need in agricultural research to help accelerate improvements in crop performance in order to facilitate more efficient utilization of plant genome sequences and the corresponding advancements in associated methods of genetic improvement. Manual plant phenotyping is time-consuming, laborious, frequently subjective, and often destructive. There is a need for building field-deployable systems with advanced sensors that have both high-speed and high-performance... T. Nguyen, D. Slaughter, B. Townsley, L. Carriedo, J. Maloof, N. Sinha |
5. Surplus Science and a Non-linear Model for the Development of Precision Agriculture TechnologyThe advent of ‘big data technologies’ such as hyperspectral imaging means that Precision Agriculture (PA) developers now have access to superabundant and highly heterogeneous data. The authors explore the limitations of the classic science model in this situation and propose a new non-linear process that is not based on the premise of controlled data scarcity. The study followed a science team tasked with developing highly advanced hyperspectral techniques for a ‘low... M.Z. Cushnahan, I.J. Yule, B.A. Wood, R. Wilson |
6. Technological Improvement on Sugar Cane Yield MonitorThis paper presents the technological improvement on sugar cane yield monitor. The system designed employs load cells as an instrument for weighing billets, set up on the side conveyor of the harvester before the sugar cane billets are dropped into a field transport wagon. This data, along with the information gathered by GPS installed on the harvester, enabled the elaboration of a digital yield map using GIS. In order to improve the yield monitor a re-design of the first prototype was accomplished.... D.G. Cerri, G.R. Gray, P.S. Magalhães |
7. Exploring Tractor Mounted Hyperspectral System Ability to Detect Sudden Death Syndrome Infection and Assess Yield in SoybeanPre-visual detection of crop disease is critical for both food and economic security. The sudden death syndrome (SDS) in soybeans, caused by Fusarium virguliforme (Fv), induces 100 million US$ crop loss, per year, in the US alone. Field-based spectroscopic remote sensing offers a method to enable timely detection, but still requires appropriate instrumentation and testing. Soybean plants were measured at canopy level over a course of a growing season to assess the capacity of spectral measurements... I. Herrmann, S. Vosberg, P. Ravindran, A. Singh, P. Townsend, S. Conley |
8. Weed Detection Among Crops by Convolutional Neural Networks with Sliding WindowsOne of the primary objectives in the field of precision agriculture is weed detection. Detecting and expunging weeds in the initial stages of crop growth with deep learning technique can minimize the usage of herbicides and maximize the crop yield for the farmers. This paper proposes a sliding window approach for the detection of weed regions using convolutional neural networks. The proposed approach involves two processes: (1) Image extraction and labelling, (2) building and training our neural... K. Kantipudi, C. Lai, C. Min, R.C. Chiang |