AI-Driven Plant Phenotyping for Crop Monitoring and Agricultural Insights

Plant phenotyping involves the detection and analysis of observable plant traits influenced by genetic and environmental interactions. It is essential for enhancing crop productivity, ensuring food security, and adapting agricultural practices to climate challenges. Conventional phenotyping approaches, which rely on visual inspections and manual measurements, are time-consuming, labor-intensive, and prone to inconsistencies. These constraints limit their effectiveness in large-scale agricultural applications, particularly when rapid and accurate trait evaluation is required. This study explores the recent advancements in plant phenotyping enabled by artificial intelligence (AI) techniques, including machine learning (ML), deep learning (DL), and TL. These AI-driven approaches enhance the efficiency, scalability, and accuracy of phenotypic analysis by automating data extraction from imaging and sensor systems. By analyzing the current methodologies, this study provides valuable insights into AI-based plant phenotyping, highlighting its role in data-driven decision-making and precision agriculture. The integration of intelligent systems in phenotyping continues to redefine crop research and monitoring, rendering significant enhancements to the progress of sustainable and high-performance agricultural approaches.