<![CDATA[We are well recognised that the vast majority of Indians work in agriculture. Most farmers always grow the same thing, always use the same amount of fertilizer, and always plant what the people want. Recently, there have been many breakthroughs in the use of machine learning in many fields of study and business. Thus, we intend to establish a framework for the application of machine learning in agriculture for the benefit of farmers. India's economy relies heavily on the nation's agricultural output. Agriculture, then, has the potential to serve as the backbone of the Philippine economy. Choosing the right crop every time is crucial when making agricultural plans. Researchers have utilised machine learning to explore agricultural issues such as crop yield, weather prediction, soil categorization, and crop labelling. Our Indian economy really needs the agricultural sector to undergo significant reforms. Simple applications of machine learning systems in agriculture have the potential to significantly enhance this industry. In addition to the considerable role played by improvements in farming machinery and technology, functional information about many subjects also plays an important role. The main idea of this study is to use the crop selection approach in order to address numerous issues in farming. This increases the wealth of India by increasing crop yields to their maximum potential. In our study, we use the method Random Forest (RF) to estimate a crop and then evaluate its performance relative to that of competing techniques]]>
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