The integration of artificial intelligence (AI) in agriculture represents a paradigm shift toward precision farming, particularly for monitoring crop growth stages. This paper focuses on pomegranate (Punica granatum L.), a drought-resistant fruit crop with substantial economic value in subtropical regions. Traditional monitoring relies on manual observations, which are inefficient and error prone. Drawing from a botanical perspective, this study explores accessible AI applications such as image recognition via mobile devices—to automate the identification of pomegranate growth phases. Conducted over three growing seasons (2022–2024) in a 10-hectare orchard in California, the research involved collaboration with AI specialists to develop user-friendly tools requiring no technical expertise. Results show AI achieving 88% accuracy in stage classification, leading to 25% improvements in resource efficiency. Challenges like variable lighting and data collection are addressed, emphasizing AI's role in sustainable agriculture for non-technical users like botanists and farmers.