Seeding Area Prediction in Agriculture Using Machine Learning

Abstract

Hyperspectral image and information processing techniques and applications have shown their utility in improving agricultural productivity and practices by giving farmers and crop managers valuable information on the elements affecting crop condition and growth. These techniques are increasingly being used in agricultural applications such as agricultural production, crop yield forecasting, crop disease monitoring, and monitoring of agricultural land utilization, water, and soil conditions The project is organized to provide a thorough overview of representative studies in order to provide guidance on important research approaches in agricultural production using big data, machine learning, and deep learning, with a focus on architectures or designs, information processing, and predictive analysis with hyperspectral data.