Optimizing Crop Traits with ANN and Genetic Innovations for Sustainable Agriculture.

Sustainable agriculture requires new strategies
to tackle difficulties such as rising food demand, environmental
pressures, and resource constraints. This study introduces a
sophisticated approach that combines artificial neural networks
(ANNs) with genetic advancements to enhance crop
characteristics. The system attains enhanced accuracy (98.1%),
F1 score (0.97), and reduced RMSE (0.012) by utilising highdimensional genetic, phenotypic, and environmental data,
surpassing baseline models. The technique integrates ANN
predictions with gene-editing frameworks, guaranteeing
accurate interventions and effective trait optimisation. The
results demonstrate considerable improvements in forecast
accuracy and resource efficiency compared to current
techniques, creating a scalable, data-driven framework to
promote sustainable agriculture practices worldwide