Development and evaluation of a novel parallel-plate capacitive sensor for spray deposition on crop leaves

• Three prototypes were designed and fabricated to evaluate spray deposition on crop leaves. • Mathematically analyze the linear relationship between capacitance change and droplet deposition. • Investigate the effects of leaf thickness and leaf water loss on sensor performance. • The feasibility of rapidly generating linear regression models using a specifically designed algorithm with controlled-volume droplets was assessed. Accurate quantification of spray deposition on crop leaves is crucial for effectively controlling insects and diseases, while also reducing pesticide use and mitigating environmental risks. This study developed a novel parallel-plate capacitive sensor for on-leaf measurement of spray deposition and fabricated three prototypes (Sensors 1–3) using feeler gauges of different thicknesses ( H = 0.4, 0.5, 0.7 mm). The sensor, consisting of two parallel copper plates, detected droplet deposition through capacitance changes (∆C), with a dedicated electronic interface and a specially designed linear regression algorithm. Linear relationship analysis between droplet deposition and capacitance change was performed. Sensors 2 and 3 evaluated the effect of leaf thickness (Leaves 5–9), while Sensor 1 assessed leaf moisture loss (Leaves 1, 2, 6) and the feasibility of rapidly constructing linear regression equations using a specifically designed algorithm with controlled-volume droplets (Leaves 3, 4). Experimental results showed that smaller distance ( d ) increased R² , Pure water wipes prevented moisture loss, and 3 µL droplets yielded the lowest errors (MAPE 15.00%), confirming high measurement accuracy. The sensor shows promise as an effective and convenient tool for monitoring droplet deposition, and the controlled-volume droplet approach, combined with a specifically designed linear regression algorithm, enables rapid construction of regression equations from leaves attached to the sensor in the field. This study holds great significance for advancing precision spraying operations and greatly improves the efficiency and reliability of droplet deposition experiments, thereby contributing to more effective evaluation and optimization of spray technologies.