Assessing the Influence of Tillage on Maize Performance Using Unmanned Aerial Vehicle Imagery

Abstract

Maize is a staple food in Sub-Saharan Africa, and tillage is widely used to boost its yield, though it affects soil and the environment both positively and negatively. To support farmers and policymakers, a data-driven approach using UAV technology was introduced. This study was conducted for two seasons in a randomized complete block design with four treatments (Harrowing only, Ploughing only, Ploughing and Harrowing, and No-tillage). The results showed that No-tillage had the lowest growth parameters, while Ploughing and Harrowing recorded the highest in terms of LAI (1.50–1.75), stem diameter (20–22.5 mm), plant height (165–175 cm), and yield (7.20–10.93 t/ha biomass, 4.619–5.67 t/ha grain yield). Despite its lower yields, No-tillage showed the highest yield improvement (+1.11 t/ha). UAVs imagery with Yolov8-small achieved high germination rate detection (mAP50: 0.89–0.95) and accurate plant height estimation (RMSE < 7 cm, R²: 0.98–0.99). For LAI estimation, UAV technology coupled with Huber regression model achieved R² scores of 0.80– 0.94 and RMSE as low as 0.14, and coupled with Gradient Boosting Machines reached R² of 0.87 and RMSE of 0.281 t/ha at the vegetative stage for Yield prediction. Ploughing and Harrowing is recommended for short-term tillage, while No-tillage is better for the long term. UAV imagery with machine learning reliably monitors maize and predicts yield. Future research should explore the long-term effects of No-tillage, UAV-based stem girth estimation, and the cost-benefit of UAV adoption in small-scale farming. Keywords: Tillage, UAV technology, maize, yield prediction, maize.