Competent approach to predict the crop yield based on soil condition using CNN algorithm

Abstract

The quality of data collected from sensors greatly influences the performance of the modeling processes using ML algorithms. These elements (e.g., BD, ML) have been used extensively to improve all aspects of rice production processes in agriculture, ushering in a new era of rice smart farming or rice precision agriculture by transforming old rice farming practices into a new era of rice smart farming or rice precision agriculture. A review of the most recent studies on intelligent data processing technology in agriculture, with a focus on agricultural production forecasting. Machine learning is a useful decision-making tool for predicting crop yields, as well as for deciding what crops to plant and what to do during the crop's growth season. To extract and synthesize the techniques and features that have been employed in agricultural production prediction research, a systematic literature review (SLR) was conducted. According to analysis, the most used features are temperature, rainfall, and soil type, and the most applied algorithm is Artificial Neural Networks in these models.