Evaluation of Various Machine Learning Algorithms for Seed Prediction Based on the Soil Nutrient Quantification

The current condition of the soil is an important consideration in crop yield projections. An analysis of the soil’s nutrient content can help farmers and soil analysts obtain a higher yield of seedlings suited to the cultivation process by facilitating the necessary preparations. This study presents a number of machine learning techniques that have been implemented in order to predict seedlings based on soil nutrient measurements. The Department of Agriculture Department in South Tamil Nadu provided the data that was used in this research experimental design. This data includes soil nutrient level samples from a variety of districts located throughout the South Tamil Nadu region for a selection of districts. The evaluation of crop yields is becoming an increasingly important area of research, including machine learning. The challenge of accurately predicting yields is an extremely significant one in the agricultural industry. Any farmer worth his salt is going to want to know how much yield he may anticipate receiving from his next harvest. In the past, predictions of yield were made by factoring in the years of experience that farmers had gained working with a certain crop and field. On the basis of the data that is now available, the prediction of the yield is a significant problem that has not yet been resolved. The implementation of methods that are driven by machine learning is the approach that is going to be most successful in achieving this objective. Five supervised machine learning methods were used to analyze the study’s data: DT, NB, SVM, RF, and LR are codes for the following: Follow these ML classification techniques to plainly categorize the results. Regression is a form of supervised learning algorithm used to predict a continuous output variable given one or more predictors or features, also known as input variables. Experiments have been carried out in order to discover the method that is the most accurate in terms of seed prediction for the purpose of continuing the farming process. In light of the findings of the experiments, it has been hypothesized that the machine learning technique known as random forest, which was among those used in the course of the research, will prove to be the most accurate method for seed prediction at a percentage level of 99.9%.