A Survey on Crop Yielding Methods in Various Technologies

Abstract

Agriculture is India's main occupation, with the majority of the Indian population relying on it to help the economy grow. Farmers rely on crop growth and crop yield to determine their future, and most farmers have adapted these methods to improve crop growth through various methods and approaches such as data mining, machine learning, and deep learning. In this paper, a review of several crop yielding methods to improve efficiency is performed, such as data mining approaches like K-Means, KNN, ID3, C4.5, Linear regression, SVT, and others, as well as machine learning and deep learning approaches like NLP, CNN, RNN, and LSTM. With these approaches, crop yielding has been improved, and many research papers in crop yielding have been published.