Proceedings
Authors
| Filter results3 paper(s) found. |
|---|
1. Using Late-season Uncalibrated Digital Aerial Imagery For Predicting Corn Nitrogen Status Within FieldsUsing uncalibrated digital aerial imagery (DAI) for diagnosing in-season nitrogen (N) deficiencies of corn (Zea mays L.) is challenging because of the dynamic nature of corn growth and the difficulty of obtaining timely imagery. Digital aerial imagery taken later during the growing season is more accurate in identifying areas deficient in N. Even so, the quantitative use of late-season DAI across many fields is still limited because the imagery is not truly calibrated. This study... P.M. Kyveryga, T.M. Blackmer, R. Pearson |
2. From Data to Decisions - Ag Technologies Provide New Opportunities and Challenges with On-Farm ResearchU.S. farmers are challenged to increase crop production while achieving greater resource use efficiency. The Nebraska On-Farm Research Network (NOFRN), enables farmers to answer critical production, profitability, and sustainability questions with their own fields and equipment. The NOFRN is sponsored by the University of Nebraska – Lincoln Extension and derives from two separate on-farm research efforts, the earliest originating in 1990. Over the course of the last 29 years,... L. Thompson, K. Glewen, N. Mueller, J. Luck |
3. 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 |