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Plant Breeding and Biotechnology

Plant Breeding and Biotechnology

Crop cultivation has a long history in human civilization, with records showing the cultivation of cereal plants beginning at around 8000 BC. This early plant breeding was developed primarily to provide a steady supply of food.

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Controlled Environment Ecosystem: A Cutting-Edge Technology in Speed Breeding.

Avinash Sharma¹, Mainu Hazarika¹, Punabati Heisnam²

¹Faculty of Agricultural Sciences, Arunachal University of Studies, Namsai, Arunachal Pradesh 792103, India.

|July 15, 2024

Summary

This summary is machine-generated.

Controlled environment agriculture accelerates crop breeding using speed breeding techniques, significantly reducing generation times and improving crop traits. This method overcomes traditional breeding limitations for enhanced food security.

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Area of Science:

Agricultural Science
Plant Breeding
Controlled Environment Agriculture

Background:

Traditional crop breeding faces limitations like long cycles (10-15 years) and environmental unpredictability.
Controlled environment agriculture (CEA) offers a stable platform for advanced plant cultivation.
Hidden hunger and malnutrition necessitate faster development of biofortified crops.

Purpose of the Study:

To evaluate the efficacy of speed breeding within controlled environment ecosystems.
To compare breeding cycle durations between traditional and controlled methods.
To explore the role of AI and machine learning in optimizing CEA for crop improvement.

Main Methods:

Integration of speed breeding techniques within controlled environment chambers.
Utilizing artificial intelligence (AI) for phenotypic screening and trait analysis.
Employing mechanistic and machine learning models for resource use efficiency regulation.

Main Results:

Speed breeding reduces breeding cycles to 2-3 years for F1-F6 generations, compared to 10-15 years for traditional methods.
Generation times for speed breeding vary by crop, from 4 in rice to 10 in Arabidopsis annually.
AI and machine learning models optimize nutrient and water use efficiency, and environmental parameters.

Conclusions:

Speed breeding in controlled environments significantly accelerates crop improvement and trait development.
AI and machine learning enhance the precision and efficiency of controlled environment agriculture.
Maintaining optimal environmental conditions is critical for the success of speed breeding in CEA.