Using Machine Learning Techniques, A New Method for Predicting Crop Yield

Abstract

Agribusiness is regarded as an essential industry worldwide since there are so many problems to handle while evaluating crops based on environmental factors. This is putting the agricultural nations to the test. Using the most recent innovations, many businesses are reducing manual labor by using IOT-based services and mechanical technology. The majority of the time, such tactics are useful for situations involving reduced physical labor, but not to the extent that was anticipated. In this work, the highest yield is predicted using the most recent machine learning (ML) innovations and KNN grouping computation. Crop production is expected to depend on topsoil and climate parameters. In general, factual models were used to guide the yield analysis and village regeneration projections. However, these quantifiable models have become questionable due to the drastically changing global environment. It becomes acceptable that we turn to alternative, less complicated techniques going forward.