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.

The tank cascade system (TCS) has been used for over 2000 years for water management in Sri Lanka. Since surface water is limited in the dry zone of Sri Lanka, agricultural production, especially of upland crops, relies on groundwater for irrigation. We sampled 29 wells in the Ulagalla cascade, a prominent TCS near Anuradhapura city in the dry zone of Sri Lanka, in Yala (dry) and Maha (wet) seasons, the two main cropping seasons in Sri Lanka. We evaluated the suitability of groundwater for irrigation using the analytic hierarchy process and geographical information system. Water quality did not vary notably between seasons. However, it deteriorated with the onset of high intensity heavy rain, especially during the Maha season. A water quality zoning map indicated that groundwater in 4% and 96% of the study area is suitable and moderately suitable for irrigation, respectively. Irrigation water quality in tank cascade landscapes and similar environments can be assessed using this methodology and our results.

This research presents a low-cost, easy-to-assemble nondispersive infrared (NDIR) device for monitoring N₂O gas concentration in agricultural soils during field and laboratory experiments. The study aimed to develop a cost-effective instrument with a simple optic structure suitable for detecting a wide range of soil N₂O gas concentrations with a submerged silicone diffusion cell. A commercially available, 59 cm path-length gas cell, microelectromechanical systems (MEMS)-based infrared emitter, pyroelectric detector, two anti-reflective (AR) coated optical windows, and one convex lens were assembled into a simple instrument with secure preciseness and responsivity. Control of the IR emitter and data recording processes was achieved through a microcontroller unit (MCU). Tests on humidity tolerance and the saturation rate of the diffusion cell were carried out to test the instrument function with the soil atmosphere. The developed calibration model was validated by repeatability tests and accuracy tests. The soil N₂O gas concentration was monitored at the laboratory level by a specific experimental setup. The coefficient of determination (R²) of the repeatability tests was more than 0.9995 with a 1–2000 ppm measurability range and no impact of air humidity on the device output. The new device achieved continuous measuring of soil N₂O gas through a submerged diffusion cell.

Soil erosion by the wind is an important phenomenon in drastic soil degradation. In Japan, andosol agricultural field is eroded by the wind and agricultural productivity is significantly affected. The aim of this study was to evaluate the effect of wind velocity and soil moisture condition on the soil erosion in andosol agricultural fields. Also, we determined the timing and amount of irrigation water needed to prevent soil erosion by the wind with respect to the wind and soil moisture conditions. A numerical model to simulate airflow in bare andosol field was developed using a continuity equation and Navier Stokes equations. Wind tunnel experiments which described a bare andosol field were performed to measure the degree of soil erosion for four levels of soil moisture condition and five wind velocities. Using the measured amount of soil transferred by wind, the erodibility parameter in Bagnold’s method that quantifies soil erosion was estimated inversely for four soil moisture values. The amounts of soil erosion calculated using this parameter were in good agreement with the measured amounts. These results indicate that the soil moisture and wind conditions under which soil erosion occurs can be determined and the amount of soil erosion can be predicted. Using these conditions and the erodibility parameter, the amount of irrigation needed for the prevention of soil erosion was quantified and the effect of irrigation on soil erosion was evaluated.

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.