Wheat Yield Prediction in India Using Principal Component Analysis-Multivariate Adaptive Regression Splines (PCA-MARS)

نویسندگان

چکیده

Crop yield forecasting is becoming more essential in the current scenario when food security must be assured, despite problems posed by an increasingly globalized community and other environmental challenges such as climate change natural disasters. Several factors influence crop prediction, which has complex non-linear relationships. Hence, to study these relationships, machine learning methodologies have been adopted from conventional statistical methods. With wheat being a primary staple Indian community, ensuring country’s crucial. In this paper, we prediction of for India overall top wheat-producing states with comparison. To accomplish this, use Multivariate Adaptive Regression Splines (MARS) after extracting main features Principal Component Analysis (PCA) considering parameters area under cultivation production years 1962–2018. The performance evaluated error analyses RMSE, MAE, R2. best-fitted MARS model chosen using cross-validation user-defined parameter optimization. We find that well suited whole states. A comparative result obtained on between states, wherein state Rajasthan better than major This research will emphasize importance improved government decision-making increased knowledge robust among farmers various

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ژورنال

عنوان ژورنال: AgriEngineering

سال: 2022

ISSN: ['2624-7402']

DOI: https://doi.org/10.3390/agriengineering4020030