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Lan, Y
Subba Rao, A
Jimenez, A
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Authors
Srinivasa Rao, C
Rao, K
Magen, H
Venkateswarlu, B
Subba Rao, A
Lan, Y
Xue, X
Oliveira, M.F
Morata, G.T
Ortiz, B
Silva, R.P
Jimenez, A
Topics
Precision Nutrient Management
Precision Aerial Application
Big Data, Data Mining and Deep Learning
Type
Poster
Year
2012
2022
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Filter results3 paper(s) found.

1. Categorization of Districts Based on Nonexchangeable Potassium: Generation GIS Maps and Implications in Efficient K Fertility Management in Indian Agriculture

Recommendations of K fertilizer are made based on available (exchangeable + water soluble) K status only  in India and other despite of  substantial contribution of nonexchangeable fraction of soil K to crop K uptake. Present paper examines the information generated in the last 30 years on the status of nonexchangeable K in Indian soils, categorization of Indian soils based on exchangeable and nonexchangeable K fractions and making K recommendations. Data for both K fractions of different... C. Srinivasa rao, K. Rao, H. Magen, B. Venkateswarlu, A. Subba rao

2. Ultra-low Altitude and Low Spraying Technology Research in Paddy

  Aerial application has characteristics of low-volume, small droplet, and possibility of drift. To control rice planthopper, leaf roller and blast, the research aimed at screening agrichemicals and determining the feasibility of using high concentration of conventional dosage for aerial application. The results showed that... Y. Lan, X. Xue

3. Coupling Machine Learning Algorithms and GIS for Crop Yield Predictions Based on Remote Sensing Imagery and Topographic Indices

In-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