Proceedings
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| Filter results5 paper(s) found. |
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1. Optical Sensors To Predict Nitrogen Demand By SugarcaneThe low effectiveness of nitrogen (N) from fertilizer is a substantial concern in worldwide which has been threatening the sustainability of sugarcane production. The increment of nitrogen use efficiency (NUE) by sugarcane genotypes associated to the best practices of fertilizer management and nutritional diagnosis methods have higher potential to reduce environment impacts of nitrogen fertilization. Due to the difficult to determine N status in soil test as well as there is not... O.T. Kolln, G.M. Sanches, J. Rossi Neto, S.G. Castro, E. Mariano, R. Otto, R. Inamasu, P.S. Magalhães, O.A. Braunbeck, H.C. Franco |
2. Soil and Crop Factors to Site-specific Nitrogen Management on Sugarcane FieldsNitrogen (N) is one of the most widely used fertilizers in crops and the most harmful to the environment. The increase fertilizers consumption, mainly N sources (one of the most widely fertilizer used in sugarcane fields), is one of the main factors underlying the sustainability of the entire production process. Currently, N recommendations in sugarcane are based only on the expected yield. However, there is little agronomic support for nitrogen (N) recommendations based on expected yield, despite... G.M. Sanches, R. Otto, F.R. Pereira |
3. Assessing the Distribution Uniformity of Broadcast-interseeded Cover Crops at Different Crop Stages by an Unmanned Aerial VehicleDrones can now carry larger payloads and have become more affordable, making them a viable option to use for broadcast-interseeding cover crops in the fall, prior to main crop harvest. This strategy has become popular in Ohio over the past two years. However, this new strategy arose quickly with a limited understanding of field performance of the drone’s distribution uniformity under different parameters such as rates, swath widths, speeds, or cash crop type. Therefore, the objective of... A.D. Thomas, J.P. Fulton, S. Khanal, O. Ortez, G. Mcglinch |
4. A Growth Stage Centric Approach to Field Scale Corn Yield Estimation by Leveraging Machine Learning Methods from Multimodal DataField scale yield estimation is labor-intensive, typically limited to a few samples in a given field, and often happens too late to inform any in-season agronomic treatments. In this study, we used meteorological data including growing degree days (GDD), photosynthetic active radiation (PAR), and rolling average of rainfall combined with hybrid relative maturity, organic matter, and weekly growth stage information from three small-plot research locations... L. Waltz, S. Katari, S. Khanal, T. Dill, C. Porter, O. Ortez, L. Lindsey, A. Nandi |
5. 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 |