A Novel Approaches to Crop Variety Development for Sustainable Agriculture using Hybrid Machine Learning Model

Abstract

The growing global population and changing climate need sustainable agriculture practices and new crop varieties. This study proposes a hybrid machine learning technique that combines linear regression and logistic regression to identify and extract unique traits from a broad range of crop species. The system integrates these qualities to create new crop types with a variety of benefits. The system begins by collecting detailed data on multiple crop species, including production potential, drought resilience, disease tolerance, nutritional content, and adaptation to different climates. Preprocessing ensures data quality. The hybrid machine learning algorithm uses the revised dataset. Linear regression identifies variables with strong connections with the required attributes, making it easier to find good candidates for the new crop variety. Logistic regression analyzes categorical data, including disease resistance genes and nutritional variables that affect crop quality. The method efficiently identifies and ranks the best characteristics across varied crop species by combining regression model findings. The traits have been strategically combined to create a pioneering cultivar that optimizes crop productivity, robustness, nutritional composition, and versatility while reducing vulnerability to pathogenic diseases and ecological challenges. The innovative crop variety is tested in various agricultural situations to determine the system's effectiveness. The topic under inquiry is compared to current kinds in terms of yield, quality, and sustainability. This unique hybrid machine learning technology may combine the best qualities from multiple sources to develop cutting-edge agricultural cultivars. The system's speed and accuracy in identifying beneficial traits promotes sustainable agriculture by reducing monospecific breeding and increasing crop resilience to environmental challenges. This work advances global food security and resource-efficient crop varieties.