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| Filter results5 paper(s) found. |
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1. Spectral Discrimination Of Early Dchinochloa Crasgalli And Echinochloa Crusgalli In Corn And Soybean By Using Support Vector MachinesThe key to realize precise chemical application is weed identification. This paper introduces a kind of multi-classification mode based on Support Vector Machines (SVM) and one-against-one-algorithm for weed seedlings (Dchinochloa crasgalli, and Echinochloa crusgalli) in corn and soybean fields. A handheld FieldSpec® 3 Spectroradiometer manufactured by ASD Inc., in USA was used to measure the spectroscopic data of the canopies of the seedlings of corn, soybean,... W. Deng, G. Wu |
2. Precision Nutrient Management For Enhancing The Yield Of Groundnut In Peninsular IndiaGroundnut is an important oil seed crop grown in an area of around 8 lakh hectares in Karnataka state of India under rainfed conditions. In these situations farmers applied inadequate fertilizer without knowing the initial nutrient status of the soil which resulted in low nutrient use efficiency that intern lead to low productivity of groundnut in these areas. Soil fertility deterioration due to... M. Giriyappa, T. Sheshadri, D. Hanumanthappa, M. Shankar, S.B. Salimath, T. Rudramuni, N. Raju, N. Devakumar, G. Mallikaarjuna, M.T. Malagi, S. Jangandi |
3. 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 |
4. 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 |
5. 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 |