Regression-Based Approach for Paddy Crop Assists for Atmospheric Data

Classification and analysis are the two major factors for the real-time automation systems. In the sector of farming, the cultivation of different paddy crops depends on the soil nature and the weather. We need to analyze the humidity level in the area to predict the type of paddy that can be cultivated. In this proposed work, a novel model of feature prediction and classification algorithm to estimate the humidity level of soil and its atmospheric temperature to analyze the type of crop that can be cultivate in the land. This is to classify the different types of rice paddy crop which is better to plant in the farming land in nature. Regression-based categorization algorithms are employed in this procedure to examine the temperature and moisture of the land. The dataset comprises collections of temperature and moisture readings from diverse data samples taken over a land moisture readings from diverse data samples taken over a land. The extracts of temperature and moisture from different day patterns are examined and framed as the pattern for the provided dataset using the feature analysis method. Regression-based categorization algorithms are employed in this procedure to examine the temperature and moisture content of the land. The dataset comprises collections of temperature and humidity readings from diverse data samples taken over a land. The extracts of temperature and humidity from different day patterns are examined and framed as the pattern for the provided dataset using the feature analysis method. The data pattern is then classified using a regression technique in order to forecast the class of paddy crop based on the dataset's attributes. The measurement of the data represents different varieties of paddy based on the atmosphere and other characteristics as a consequence of the categorization result. The kind of classification model aids in agricultural planting and guards against crop damage brought on by excessive heat or water. The contrast between the suggested work results and those of other cutting-edge data categorization techniques is shown in the result analysis.