Regression-based Smart Agriculture Prediction

Smart agriculture has come to be seen as a potential method for boosting agricultural output and sustainability. The study primarily focuses on the application of regression algorithms for predictive analysis in smart agriculture with reference to crop yield prediction. This study's main objective is to develop a reliable regression model that estimates crop yields based on a number of agricultural and environmental factors. This is done by accumulating previous agricultural information, such as weather patterns, soil properties, irrigation methods, and cropspecific traits, via smart sensors positioned throughout agricultural areas. In the study, a variety of regression approaches are investigated, including support vector regression (SVR), random forest regression, linear regression, polynomial regression, and more. The dataset is analyzed before using these methods to build prediction models. To validate the model's accuracy and generalizability, the dataset is split into training and testing sets, and cross-validation techniques are employed. Regression algorithms are highly reliable at predicting agricultural yields, according to the results. Random forest regression outperforms all other methods due to its capability to handle non-linearity and capture nuanced relationships between input characteristics and crop yields. However, the findings from other regression algorithms are equally promising, suggesting that they may be useful in specific situations. Additionally, the study evaluates how different input variables impact the model's efficacy. It draws attention to crucial elements including temperature, precipitation, soil moisture, and nutrient levels that have a significant impact on crop yields. Farmers may select the ideal crops, irrigation schedules, and fertilizer applications with the aid of an understanding of these connections