Abstract:
Water scarcity and poor irrigation timing continue to constrain vegetable yields in smallholder
farming systems. This study developed a Python based irrigation scheduling tool for six
vegetables at the Busitema Habuleke Irrigation Scheme that integrates GIS, remote sensing
data, and machine learning (ML) soil moisture predictions. The study addressed the challenge
of impractical and costly field-based monitoring for irrigation scheduling and the lack of
packaged, data driven tools for smallholder farmers. The specific objectives were to (1) analyse
spatial and temporal factors influencing root zone volumetric water content (VWC), (2)
develop and compare ML models for VWC prediction, and (3) implement and validate an
irrigation scheduling tool that generates trigger-based irrigation recommendations. For
Objective 1, spatial and statistical analyses revealed that soil moisture distribution closely
followed rainfall, evapotranspiration, and soil texture patterns. Higher VWC values were
recorded in loam soils, while areas with sandy textures exhibited lower moisture levels. The
mean VWC was 19.34% (SD = 5.86%), and rainfall, field capacity, bulk density, and available
water content (AWC) were all recognised as significant contributors. Multiple regression
explained 88% of the variance in VWC (R² = 0.88), with rainfall (β ≈ 0.305) and AWC (β ≈
0.295) being the best predictors (p < 0.001). Sensitivity analysis showed that a 10% change in
rainfall and AWC caused a 3% and 2.9% change in VWC, respectively. Under Objective 2,
three ML models, Random Forest (RF), Long Short Training Memory (LSTM), and Extreme
Gradient Boosting (xGBoost), were developed and compared using soil, climatic, and
topographic data from 2010 to April 2025. XGBoost exhibited the best performance (NSE =
0.985, RMSE = 0.022, MAE = 0.017), followed by Random Forest (NSE = 0.954) and LSTM
(NSE = 0.873). The superior accuracy of XGBoost was attributed to its strong ability to capture
non linear relationships between soil moisture and environmental variables. For Objective 3,
the validated irrigation scheduling tool produced irrigation recommendations. Seasonal gross
requirements for tomatoes under drip ≈ 420 mm season total; green pepper sprinkler ≈ 540–
560 mm and event-level recommendations of carrots: start irrigation at VWC ≈ 0.22–0.25
m³/m³ with 1.5–4 mm pulses were obtained. The tool schedules exhibited lower variability and
greater alignment with reference evapotranspiration-based methods compared to farmer
practice, leading to measurable improvements in yield consistency. Overall, the integration of
in situ calibration, remote sensing, and ML models provides a practical, site specific tool for
smallholder irrigation management. Future work should focus on adding offline functionality,
expanding farmer trials, and performing uncertainty analysis to enhance operational reliability.
Key words: Vegetable irrigation, Irrigation scheduling, GIS, Remote sensing, Soil moisture
prediction, Machine learning, Smallholder farming.
