Proceedings
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| Filter results6 paper(s) found. |
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1. Assessing the Potential of an Algorithm Based On Mean Climatic Data to Predict Wheat YieldIn crop yield prediction, the unobserved future weather remains the key point of predictions. Since weather forecasts are limited in time, a large amount of information may come from the analysis of past weather data. Mean data over the past years and stochastically generated data are two possible ways to compensate the lack of future data. This research aims to demonstrate that it is possible to predict... F. Vancutsem, V. Leemans, S. Ferrandis vallterra, B. Bodson, J. Destain, M. Destain, B. Dumont |
2. A Model For Wheat Yield Prediction Based On Real-time Monitoring Of Environmental Factors... B. Dumont, F. Vancutsem, J. Destain, B. Bodson, F. Lebeau, M. Destain |
3. Pa Adoption By A Korean Rice Farming Group: Case Study Of Pyeongtaek CityResearch on precision agriculture (PA) has been conducted in Korea for about 10 years since 1999. Most of the research was focused on rice paddy fields that were flooded, flat, and small sized (e.g., 30 m x 100 m). Accomplishment during the period includes investigation on spatial variability in soil, crop growth, and yield properties, application of imported sensors and variable rate applicators, and development of Korean version of these sensors... S. Chung, H. Yoo, S. Hong |
4. Predicting Below and Above Ground Peanut Biomass and Maturity Using Multi-target RegressionPeanut growth and maturity prediction can help farmers and breeding programs improving crop management. Remote sensing images collected by satellites and drones make possible and accurate crop monitoring. Today, empirical relations between crop biomass and spectral reflectance could be used for prediction of single variables such as aboveground crop biomass, pod weight (PW), or peanut maturity. Robust algorithms such as multioutput regression (MTR) implemented through multioutput random forest... M.F. Oliveira, F.M. Carneiro, M. Thurmond, M.D. Del val, L.P. Oliveira, B. Ortiz, A. Sanz-saez, D. Tedesco |
5. Coupling Machine Learning Algorithms and GIS for Crop Yield Predictions Based on Remote Sensing Imagery and Topographic IndicesIn-season yield prediction can support crop management decisions helping farmers achieve their yield goals. The use of remote sensing to predict yield it is an alternative for non-destructive yield assessment but coupling auxiliary data such as topography features could help increase the accuracy of yield estimation. Predictive algorithms that can effectively identify, process and predict yield at field scale base on remote sensing and topography still needed. Machine learning could be an alternative... M.F. Oliveira, G.T. Morata, B. Ortiz, R.P. Silva, A. Jimenez |
6. Use of Crop and Drought Spectral Indices to Support Harvest Decisions of Peanut Fields in AlabamaHarvest efficiency expressed in quantity and quality of peanut fields could increase if farmers are provided with tools to support harvest decisions. Peanut farmers still rely on a visual and empiric method to assess the right time of peanut maturity but this method does not account for within-field variability of crop growth and maturity. The integration of spectral vegetation indices to assess drought, soil moisture, and crop growth to predict peanut maturity can help farmers strengthen decisions... M.F. Oliveira, B.V. Ortiz, E. Hanyabui, J.B. Costa souza, A. Sanz-saez, S. Luns hatum de almeida , C. Pilcon, G. Vellidis |