Details

Autor(en) / Beteiligte

• Roopa, V.
• Shyni, C. Emilin

Titel

Data Driven Approach For Farm Re-Modeling Using Prediction Analytics

Ist Teil von

• 2019 International Conference on Computational Intelligence and Knowledge Economy (ICCIKE), 2019, p.209-214

Ort / Verlag

IEEE

Erscheinungsjahr

2019

Quelle

IEL

Beschreibungen/Notizen

• The technological advancements in IT has made agriculture industry move towards a sustainable growth. Farming may not sound like an issue we need to care about, but the agriculture industry is in dire need of refinement. Sustaining in agriculture is challenging because of reducing water levels, climate change and shrinking amount of arable land. These after effects can be made approachable using prediction methods. The foremost idea of the prototype is to monitor the large crop area and thereby maintaining it using the sensor enabled IoT technology. The Autopilot drones fly once or twice a week and captured images from the whole farm are also stored. Heat map a feature function of the entire farm which is used to extrapolate the data is analyzed. With this, the data driven approach for re- modeling of the farm is done using Zero- R algorithm and Naive-Bayes algorithm. Visualization and statistical predictions of the data is done using Tableau tool and further analysis using R & Python. The major benefits of providing this real time data analytic prediction help farmers to remodel traditional farming and focus to improve crop quality, potential to increase yields and to overcome uncertainty by this prediction analytic makeover to the agriculture industry.