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Filter results4 paper(s) found. |
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1. A Novel Hyperspectral Feature Extraction Algorithm Based On Waveform Resolving For Raisin ClassificationNear infrared hyperspectral imaging technology was adopted in the paper to determine the variety of raisins produced in Xinjiang Uygur Autonomous Region, China. There are 2 varieties of raisins taking part in the research and the wavelengths of the hyperspectral images are from 900nm to 1700nm. A novel waveform resolving method was proposed in the paper to reduce the hyperspectral data and extract features. The waveform resolving method compresses the original hyperspectral data for one pixel... Y. Zhao, X. Xu, Y. Shao, Y. He, Q. Li |
2. Almond Canopy Detection and Segmentation Using Remote Sensing Data DronesThe development of Unmanned Aerial System (UAV) makes it possible to take high resolution images of trees easily. These images could help better manage the orchard. However, more research is necessary to extract useful information from these images. For example, irrigation schedule and yield prediction both rely on accurate measurement of canopy size. In this paper, a workflow is proposed to count trees and measure the canopy size of each individual tree. The performances of three different methods... T. Zhao, M. Cisneros, Y. Chen, Q. Yang, Y. Zhang |
3. Effect of Irrigation Scheduling Technique and Fertility Level on Corn Yield and Nitrogen MovementFlorida has more first magnitude springs that anywhere in the world. Most of these are located in north Florida where agricultural production is the primary basis for the economy. Irrigated corn has become a popular part of the crop rotation in recent years. This project is a study of a corn and peanut rotation investigating Best Management Practices (BMPs) of nitrogen fertility level (336, 246, 157 kg/ha) and irrigation strategies as follows: (i) GROW, mimicking grower’s practices,... M. Dukes, M. Zamora, D. Rowland |
4. Evaluation of an Artificial Neural Network Approach for Prediction of Corn and Soybean YieldThe ability to predict crop yield during the growing season is important for crop income, insurance projections and for evaluating food security. Yet, modeling crop yield is challenging because of the complexity of the relationships between crop growth and the interrelated predictor variables. Artificial neural networks (ANNs) are useful for such complex systems as they can capture non-linear relationships of data without explicitly knowing the underlying processes. In this study, an ANN-based... A. Kross, G. Kaur, E. Znoj, D. Callegari, M. Sunohara, H. Mcnairn, D. Lapen, H. Rudy, L. Van vliet |