Crop monitoring with unmanned aerial vehicles (UAVs) has the potential to reduce field monitoring costs while increasing monitoring frequency and improving efficiency. However, the utilization of RGB-based UAV imagery for crop-specific monitoring, especially for sugarcane, remains limited. This work proposes a UAV platform with an RGB camera as a low-cost solution to monitor sugarcane fields, complementing the commonly used multi-spectral methods. This new approach optimizes the RGB vegetation indices for accurate prediction of sugarcane growth, providing many improvements in scalable crop-management methods. The images were captured by a DJI Mavic Pro drone. Four RGB vegetation indices (VIs) (GLI, VARI, GRVI, and MGRVI) and the crop surface model plant height (CSM_PH) were derived from the images. The fractional vegetation cover (FVC) values were compared by image classification. Sugarcane plant height predictions were generated using two machine learning (ML) algorithms—multiple linear regression (MLR) and random forest (RF)—which were compared across five predictor combinations (CSM_PH and four VIs). At the early stage, all VIs showed significantly lower values than later stages (p < 0.05), indicating an initial slow progression of crop growth. MGRVI achieved a classification accuracy of over 94% across all growth phases, outperforming traditional indices. Based on the feature rankings, VARI was the least sensitive parameter, showing the lowest correlation (r < 0.5) and mutual information (MI < 0.4). The results showed that the RF and MLR models provided better predictions for plant height. The best estimation results were observed withthe combination of CSM_PH and GLI utilizing RF model (R² = 0.90, RMSE = 0.37 m, MAE = 0.27 m, and AIC = 21.93). This study revealed that VIs and the CSM_PH derived from RGB images captured by UAVs could be useful in monitoring sugarcane growth to boost crop productivity.

Superabsorbent polymers (SAPs) are effective soil amendments that can control soil erosion by improving soil quality. However, many commercial SAPs face challenges including limited biodegradability, high costs, and adverse effects on soil hydrological properties, which can lead to increased water and soil loss. This study examined the potential of lower dosages of biodegradable SAPs to improve the hydrological properties of “Shimajiri-maji” (clay) soil. Three concentrations of biodegradable Fasal Amrit polymer (EFP) (P1: 0, P2: 3 g m⁻², and P3: 6 g m⁻²) were evaluated under three simulated rainfall intensities (I1: 35; I2: 70 and I3: 110 mm h⁻¹) and two gradients (7.5%, and 15%) during consecutive storms. The time to generate runoff, infiltration, runoff, soil loss, and water storage (WS) were quantified over one hour. The results show that runoff generation was delayed in EFP-treated soils compared to the control. Both polymer treatments enhanced infiltration (P2 > P3 > P1) and reduced runoff and soil loss (P2 < P3 < P1). Higher EFP rates improved water storage at surface depths (P3 > P2 > P1). EFP-treated soils exhibited lower interrill erodibility, suggesting greater resistance to soil erosion compared to the control. EFP treatments also significantly improved the soil’s physical properties (bulk density, porosity, organic matter, aggregate stability). EFPs can diminish runoff and soil loss as the EFP-treated plots exhibited greater aggregate stability than the control. It was concluded that low EFP concentrations can improve soil hydrological properties and mitigate soil erosion. Further investigations are needed to optimize the EFP concentrations for different soil types.