Details

Autor(en) / Beteiligte

• Raja, M. Senthil
• Swaroop, B. Jyothi
• Peera, K. Kasim
• Godfrey Winster, S
• Pushpalatha, M
• Baskar, M
• Kishore Anthuvan Sahayaraj, K

Titel

Crop recommendation using machine learning

Ist Teil von

• AIP Conference Proceedings, 2024, Vol.3075 (1)

Ort / Verlag

Melville: American Institute of Physics

Erscheinungsjahr

2024

Beschreibungen/Notizen

• Using a crop yield prediction model, farmers may make better choices about when to plant the crops and the different kinds of crops to grow depending on environmental parameters to achieve greater yield. The advanced ensemble regression used for crop yield prediction model uses phenotypic parameters including precipitation, solar radiation, and also maximum and minimum temperature, etc. to forecast crop production. The dataset for corn and soybean crops contains information on yield performance from 38 years that was gathered in 105 differentsites. On the basis of feature selection, a comparison between correlation and mutual information is done. Most relevant characteristics to agricultural output were found, and via sharing of information, crop yield estimates were improved. Advanced ensemble regression on the basisof mutual information that we suggested is utilised in the prediction.