Journal of Advances in Biology & Biotechnology

Plant phenology, the examination of cyclical biological occurrences in plants, is essential for comprehending crop growth, development, and yield under diverse environmental settings. This review methodically analyses the revolutionary impact of sophisticated crop models and technology-based methodologies in contemporary phenological research. Prominent crop models include MLCan (Multi-layer Canopy Model), AquaCrop 7.0, Decision Support System for Agrotechnology Transfer (DSSAT), and OpenSimRoot, each providing distinct functionalities in simulating canopy processes, water productivity, root system dynamics, and yield forecasting. These models, supported by comprehensive meteorological, soil, crop, and management data, offer strong frameworks for comprehending the intricate relationships between crops and their environments. The review emphasises the incorporation of innovative technology, including UAV-mounted sensors, Normalised Difference Vegetation Index (NDVI), and sophisticated root imaging systems like MyROOT 2.0, which improve the accuracy, scalability, and temporal resolution of phenological observations. The integration of machine learning algorithms enhances predictive modelling by identifying non-linear interactions, refining agricultural management practices, and facilitating real-time decision-making. These inventions collectively offer robust solutions to the concerns of climate change, resource scarcity, and the necessity for sustainable agriculture methods. This analysis underscores the significance of utilising model-based and technology-driven methodologies to enhance crop yield, optimise resource efficiency, and bolster global food security amid changing environmental and socio-economic challenges. Subsequent research ought to concentrate on optimising these instruments, improving their accessibility, and incorporating them into holistic decision support systems to amplify their influence on agricultural sustainability and resilience.