ARTIFICIAL INTELLIGENCE-DRIVEN ANALYSIS OF GENETIC ENGINEERING IN INDIAN AGRICULTURE: SUCCESS RATES, FAILURES, AND EMERGING TRENDS

Genetic engineering has become a key driver of agricultural innovation in India, contributing to improved crop
productivity, pest resistance, and environmental sustainability. With the integration of Artificial Intelligence (AI), the analysis and optimization of genetically engineered crops have advanced significantly, enabling datadriven insights into performance, adoption, and impact. This study presents an AI-driven evaluation of genetic engineering applications in Indian agriculture, focusing on success rates, failures, and emerging trends.
Successful implementations such as Bt cotton demonstrate enhanced yield and reduced pesticide dependency,
while other genetically modified initiatives have encountered limitations due to regulatory constraints,
ecological concerns, and socio-economic factors. Machine learning and data analytics approaches are
increasingly being used to assess crop performance, predict outcomes, and support decision-making in
biotechnology research. The findings highlight that although genetic engineering holds substantial promise for
sustainable agriculture in India, its effectiveness depends on the integration of AI-based predictive systems,
robust policy frameworks, and farmer-centric adoption strategies.