Proceedings
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| Filter results10 paper(s) found. |
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1. Modeling Canopy Light Interception For Estimating Yield In Almond And Walnut TreesA knowledge of spatio-temporal variability in potential yield is essential for site-specific nutrient management in crop production. The objectives of this project were to develop a model for photosynthetically active radiation (PAR) intercepted by almond and walnut trees based on data obtained from respective tree(s) and estimate potential crop yield in individual trees or in blocks of five trees. This project uses proximally sensed PAR interception data measured using a lightbar... R. Dhillon, S. Upadhyaya, J. Roach, K. Crawford, B. lampinen, S. Metcalf, F. Rojo |
2. An Inexpensive Aerial Platform For Precise Remote Sensing Of Almond And Walnut Canopy TemperatureCurrent irrigation practices depend largely on imprecise applications of water over fields with varying degrees of heterogeneity. In most cases, the amount of water applied over a given field is determined by the amount the most water-stressed part of the field needs. This equates to over-watering most of the field in order to satisfy the needs of one part of the field. This approach not only wastes resources, but can have a detrimental effect on the value of that crop. A system to... K. Crawford, S. Upadhyaya, R. Dhillon, F. Rojo, J. Roach |
3. Comparison Between High Resolution Spectral Indices and SPAD Meter Estimates of Nitrogen Deficiency in CornLow altitude remote sensing provides an ideal platform for monitoring time sensitive nitrogen status in crops. Research is needed however to understand the interaction between crop growth stage, spatial resolution and spectral indices derived from low altitude remote sensing. A TetraCam camera equipped with six bands including the red edge and near infrared (NIR) was used to investigate corn nitrogen dynamics. Remote sensing data were collected during the 2013 and 2014 growing seasons at four... D. Mulla, A. Laacouri, D. Kaiser |
4. A Case Study Comparing Machine Learning and Vegetation Indices for Assessing Corn Nitrogen Status in an Agricultural Field in MinnesotaCompact hyperspectral sensors compatible with UAV platforms are becoming more readily available. These sensors provide reflectance in narrow spectral bands while covering a wide range of the electromagnetic spectrum. However, because of the narrow spectral bands and wide spectral range, hyperspectral data analysis can benefit greatly from data mining and machine learning techniques to leverage its power. In this study, rainfed corn was grown during the 2017 growing season using four nitrogen treatments... A. Laacouri, T. Nigon, D. Mulla, C. Yang |
5. Predicted Nitrate-N Loads for Fall, Spring, and VRN Fertilizer Application in Southern MinnesotaNitrate-N from agricultural fields is a source of pollution to fresh and marine waters via subsurface tile drainage. Sensor-based technologies that allow for in-season monitoring of crop nitrogen requirements may represent a way to reduce nitrate-N loadings to surface waters by allowing for fertilizer application on a more precise spatial and temporal resolution. However, little research has been done to determine its effectiveness in reducing nitrate-N losses. In this study,... G.L. Wilson, D.J. Mulla, J. Galzki, A. Laacouri, J. Vetsch |
6. Can Optimization Associated with On-Farm Experimentation Using Site-Specific Technologies Improve Producer Management Decisions?Crop production input decisions have become increasingly difficult due to uncertainty in global markets, input costs, commodity prices, and price premiums. We hypothesize that if producers had better knowledge of market prices, spatial variability in crop response, and weather conditions that drive crop response to inputs, they could more cost-effectively make profit-maximizing input decisions. Understanding the drivers of variability in crop response and designing accompanying management strategies... B.D. Maxwell, A. Bekkerman, N. Silverman, R. Payn, J. Sheppard, C. Izurieta, P. Davis, P.B. Hegedus |
7. Machine Learning Approach to Study the Effect of Weather and Proposed Climate Change Scenarios on Variability in the Ohio Corn and Soybean YieldClimate is one of the primary factors that affects agricultural production. Climate change and extreme weather events have raised concerns about its effect on crop yields. Climate change patterns affect the crop yield in many ways including the length of the growing season, planting and harvest time windows, precipitation amount and frequency, and the growing degree days. It is important to analyze the effect of climate change on yield variability for a better understanding of the effect... R. Dhillon, G. Takoo |
8. Evaluating the Potential of In-season Spatial Prediction of Corn Yield and Responses to Nitrogen by Combining Crop Growth Modeling, Satellite Remote Sensing and Machine LearningNitrogen (N) is a critical yield-limiting factor for corn (Zea mays L.). However, over-application of N fertilizers is a common problem in the US Midwest, leading to many environmental problems. It is crucial to develop efficient precision N management (PNM) strategies to improve corn N management. Different PNM strategies have been developed using proximal and remote sensing, crop growth modeling and machine learning. These strategies have both advantages and disadvantages. There is... X. Zhen, Y. Miao, K. Mizuta, S. Folle, J. Lu, R.P. Negrini, G. Feng, Y. Huang |
9. Have Your Steak and Eat It Too: Precision Beef Management to Simultaneously Reduce Ech4 and Increase ProfitAchieving carbon net zero is a clear priority, with beef farmers under significant scrutiny from food system stakeholders. Tools are available to assess greenhouse gas emissions (GHGe), yet adoption is low, and producers are not currently financially incentivised to change management practices. This study used cattle performance data from a commercial beef operation to model the optimal age and weight at slaughter to maximise profit and reduce enteric methane (eCH4) emissions at the... K. Behrendt, J. Capper, L. Ford, E.W. Harris |
10. Sugarcane Yield Mapping Using an On-board Volumetric SensorFew alternatives are available to the sugarcane sector for monitoring crop productivity. However, in recent years, research has been dedicated to developing methods ranging from estimation based on engine parameters to using sensors and artificial intelligence. This study aims to present a new tool for monitoring productivity applied to sugarcane cultivation, which utilizes a volumetric optical sensor, in contrast to other methods already used for this measurement, and is recently being introduced... G. Balboa, J.C. Masnello, F. De oliveira moreira, R. Canal filho, E.R. Da silva, J.P. Molin |