AN EFFICIENT FEATURE SELECTION AND CLASSIFICATION FOR THE CROP FIELD IDENTIFICATION: A HYBRIDIZED WRAPPER BASED APPROACH (Turkish Journal of Computer and Mathematics Education (TURCOMAT), 2022)
Agricultural stakeholders are concerned about the anticipated crop productionbefore the harvest. Many countries throughout the world employ computational technique'sfor predicting yield ahead of harvest to assess a country's food security and issue warningsabout impending food shortages. This is a common method that aids strategy planners anddecision-makers, particularly in rural economies. Crop statistical models have been used totrack crop development and forecast production. The only inputs available at the field levelwill yield a prediction for a narrow region; remote sensing observations cover a broad area.They may be repeated at regular intervals, allowing for large-scale crop modelling. Cropyield prediction study necessitates a variety of production parameters and algorithms. Somealgorithms are used to determine the optimum feature subset for improved prediction, whileothers are used to determine prediction. The proposed Correlation based Sequential ForwardFeature Selection (CSFFS) is compared with the existing feature selection approaches. Theclassification with proposed feature selection attains effective accuracy in crop prediction.