Integrating field camera imagery for monitoring maize phenology, biophysical traits, and agroclimatic factors in smallholder farms

• Field cameras detect key phenological stages across diverse agroecological zones. • Site-specific biophysical and agroclimatic variations captured effectively. • Crops in dryer ecosystems respond more rapidly to agroclimatic variations. • Inexpensive & high-frequency crop monitoring approach for smallholder farms. Accurate monitoring of crop phenology, biophysical attributes and agroclimatic variability is essential for optimizing agricultural practices, particularly in smallholder farming systems. In this study, we evaluated how field camera-derived Green Chromatic Coordinate (GCC) reflected variations in agroclimatic factors (rainfall and soil moisture) and biophysical attributes (leaf area index, crop height, chlorophyll content, and stomatal conductance) across agroecological zones (AEZs) in Kenya. Next, we utilized GCC time series to detect six key phenological stages of maize ( Zea mays L.) - emergence, stem elongation, tasseling, kernel development, ripening, and senescence - using an amplitude-based relative threshold method. This approach was cross-validated against field observed phenology. Our analysis revealed positive correlations between GCC and plant height, chlorophyll content, and leaf area index (LAI). Daily-scale Pearson lag correlation between GCC and agroclimatic factors revealed that crops in drier ecosystems exhibited shorter response times to agroclimatic fluctuations (32 days to rainfall and 14 days to soil moisture), highlighting site-specific differences in vegetation dynamics captured by field cameras. Furthermore, results indicate that GCC effectively captured phenological stages with high accuracy (R 2 = 0.9, RMSE = 7.1–7.7 days), though variability was observed across sites and growth stages. Comparisons between within-site and inter-site validation suggest that localized calibration can improve accuracy. Nevertheless, the method remains robust across varying conditions, which is supported by comparison against established curve-fitting methods. Our findings highlight the potential of field cameras as a cost-effective tool for crop monitoring at a high spatial and temporal scale, with applications in crop phenology detection, biophysical monitoring, and validation of remote sensing products. Integrating this approach into regenerative agriculture frameworks could enhance decision-making and management interventions in smallholder farms.