Recommendation of Paddy Seed Prediction Using Generative Model

Abstract

India's agriculture sector serves a significant role. It is essential to update ideas and methods in order to maintain standards in order to keep up with global warming and other demographic and materialistic issues that keep piling up. Being the top producer of a range of crops throughout the world has made it necessary to update ideas and methods in recent years. Predictive systems are a crucial instrument for management and decision-making in every productive industry. In agriculture, it is particularly valuable to have advance knowledge of a farm's profit potential. Consequently, depending on the time of year when this information is available, crucial decisions can be made that impact the farm's financial stability. The objective of this study is to develop a model for predicting Seed and pertinent characteristics in advance that is easily accessible and usable by farmers through a web application. This includes more sophisticated methods that have been derived from machine learning technologies and algorithms such as generative models and random classifiers. The fundamental motivation behind this work is to facilitate prediction using a machine learning model. The Seed suggestion model provides assistance in resolving a variety of innovative and frequently disregarded challenges that the younger generation of farmers is currently facing. Research on yield estimation in agriculture is beneficial for farmers as it helps them minimize crop loss and maximize profits by obtaining the best prices for their increased yield production. Prioritizing crop-sowing techniques in agricultural research also improves the well-being of the land, farmers, and others.