Controlled Environment Ecosystem: A Cutting-Edge Technology in Speed Breeding
Avinash Sharma, Mainu Hazarika, Punabati Heisnam, Himanshu Pandey, Vadakkumcheri Akathoottu Subrahmanian Nampoothiri Devadas, Ajith Kumar Kesavan, Praveen Kumar, Devendra Singh, Amit Vashishth, Rani Jha, Varucha Misra, Rajeev Kumar
Abstract
annually within controlled environment ecosystems. Artificial intelligence leverages neural networks and algorithm models to screen phenotypic traits and assess their role in diverse crop species. Moreover, in controlled environment systems, mechanistic models combined with machine learning effectively regulate stable nutrient use efficiency, water use efficiency, photosynthetic assimilation product, metabolic use efficiency, climatic factors, greenhouse gas emissions, carbon sequestration, and carbon footprints. However, any negligence, even minor, in maintaining optimal photoperiodism, temperature, humidity, and controlling pests or diseases can lead to the deterioration of crop trials and speed breeding techniques within the controlled environment system. Further comparative studies are imperative to comprehend and justify the efficacy of climate management techniques in controlled environment ecosystems compared to natural environments, with or without soil.