Non-destructive monitoring and modeling of greenhouse tomato growth based on multi-sources of RGB, multispectral, and thermal infrared images

Non-destructive real-time monitoring of stem-leaf fresh biomass during the vegetative and reproductive stages in greenhouse tomatoes is essential for optimizing fruit management. Traditional RGB-based methods frequently encounter signal saturation due to canopy closure and lack critical plant physiological information. To address these limitations, an integrated RGB, multispectral, and thermal infrared (RGB-MS-TIR) monitoring system was developed presenting a "Volume-Density-Vitality" analytical framework. First, the ZoeDepth and EfficientSAM algorithms were utilized to extract plant height and canopy cover with high precision, establishing a structural ‘volume’ skeleton ( R 2 = 0.90, RMSE = 12.43 cm). Second, multispectral indices were employed to quantify chlorophyll ‘density’, while thermal infrared features were used to characterize physiological ‘vitality’. The results demonstrated that when canopy cover exceeded 75 % (the saturation zone), the canopy-air temperature difference (Δ T c-a ) exhibited a significant negative correlation with biomass ( P < 0.001), effectively mitigating geometric signal saturation through the transpiration cooling effect. Ultimately, the Random Forest (RF) model integrating RGB, MS, and TIR features achieved the superior performance in estimating stem-leaf fresh biomass, with a coefficient of determination ( R 2 ) of 0.96 and an RMSE of 46.28 g plant⁻¹. This research highlights that multimodal fusion significantly enhances estimation accuracy under complex canopy conditions, providing a robust solution for intelligent greenhouse crop management.