Crop selection

Abstract

Agriculture has proven to be the most effective and efficient economic activity in many developing countries, contributing to economic growth. However, it faces numerous challenges that hinder productivity. Improving productivity necessitates both efficient approaches for selecting suitable crops for cultivation and adherence to the technical itineraries of crops. While the technical itineraries for most crops are well-known, choosing the best crops before commencing agricultural activities remains challenging for farmers, primarily due to the many factors and uncertainties involved. Various research studies have proposed techniques to assist farmers at this early stage. This paper examines the specific methods employed, the different factors at play, the sources and nature of data, and the overall performance achieved in each study. The outcomes of this research reveal trends and provide insights into potential future work in crop selection and rotation. These findings can contribute to developing improved crop selection systems by proposing suitable techniques and identifying crucial parameters. • In-depth review of techniques: On various methods like genetic algorithms, expert systems, and machine learning, aiding farmers in making optimal crop decisions. • Holistic factor analysis: It emphasizes the interplay of environmental, economic, social, and governmental factors for sustainable agriculture. • Extensive Review: On covers 56 papers, detecting trends in parameter choices and performance and underscoring data's crucial role in crop selection. • Technological advances: Emphasis on the increasing use of AI and ML, promising user-friendly tools to transform agricultural decision-making. • Future research focuses: Into new parameters for practical applications, addressing climate challenges, and formulating supportive guidelines for sustainable plant selection.