Here are some of the approaches that can be considered:

• Accelerated Gene Discovery for Drought Tolerance: Researchers have identified the PgDREB2A gene in pearl millet as being crucial for drought tolerance. AI and machine learning algorithms can analyze genomic data from different pearl millet varieties to pinpoint variations in this gene that confer superior drought resistance, allowing breeders to select for these variations more efficiently.

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• Predicting Hybrid Performance: The International Crops Research Institute for the Semi-Arid Tropics (ICRISAT) has reportedly used neural networks trained on over 17,000 pearl millet accessions to predict hybrid performance with up to 89% accuracy. This significantly reduces the need for extensive field trials. https://oar.icrisat.org/873/1/RA_00110.pdf
• Phenomic Prediction with Drones: Imagine drones equipped with specialized cameras flying over millet fields, collecting data on plant health, growth, and stress responses. Deep learning models can then analyze this drone imagery to identify individual plants with superior traits, like stronger root systems or better nutrient uptake, allowing breeders to select them in real-time. This is being explored to assess crop performance under various environmental stresses.

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• Biofortification Optimization: AI models are being developed to predict optimal crossing combinations for iron and zinc biofortification in millets. This means identifying parent plants whose offspring are most likely to have higher levels of these essential micronutrients while maintaining good yield.

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