Transitioning from Traditional to Integrated Crop Management: Advancing Cotton Production Practices

Cotton, a critical crop for the global agricultural economy, faces growing challenges due to traditional farming practices that rely heavily on chemical inputs, inefficient resource usage, and environmental degradation. This study explores the transition from Traditional Crop Management (TCM) to Integrated Crop Management (ICM) as a sustainable alternative, leveraging artificial intelligence (AI) and machine learning (ML) technologies. ICM incorporates a holistic approach, integrating precision agriculture, biological pest control, optimized resource usage, and soil health management practices. By utilizing AI and ML models to analyze data from experimental fields under similar environmental conditions, this research evaluates the economic, agronomic, and environmental impacts of transitioning to ICM in cotton farming. Key metrics such as crop yield, resource usage, soil health, and biodiversity are assessed, with AI-driven insights providing actionable recommendations for improving sustainability. The findings aim to support farmers and policymakers in making informed decisions to promote sustainable cotton production, addressing the environmental and economic challenges of the industry.