A Novel Approach to Improve Crop Yield Using Machine Learning

Agriculture is considered as the mainstay and source of revenue in India which contributes to 54. Accounting for 6 % of the population and close to 14% of the total GDP of the country. However, it has undergone a lot of challenges that hampers its functionality in this research despite being central in this study. One major constraint is that the type of soil and climate varies greatly from one region to another in India and they are also always changing due to Global Warming. This difference complicates the search for uniform and reliable product yields making it very hard for farmers to achieve high productivity. To solve these complex problems, this study proposes the development of a new solution: an elementary site identification model. Improve on the precise and timely decision-making process in relation to agriculture. It has a great potential in various fields, as it is able to identify strict correlation with a large amount of data without needing it to be pointed out by someone. When these techniques are applied to agricultural data they will present an appropriate model for crop selection in terms of type of soil, climate and production history of crops. Apparently, intelligence offers them proper information in line with their circumstances prevailing at the time they are working on. Farmers will be able to practice better decision making with regards to which crops to grow, at what season, resource utilization and improved yields from the recommendations coming out of the data. Through this objective, the undefined nature of crop production is reduced and the productivity of input like water, fertilizers and pesticides is optimized. This makes farming more lucrative reducing the chances of its decline or shrinkage hence promoting its sustainability as well as rapid growth. Altogether, this research will go a long way in impacting Indian agriculture positively by providing solutions whenever such issues come up in the course of pursuing bigger objectives.