## 3 Results and Discussion  
### Manual Residue Coverage Area Determination  
The feasibility of the proposed software was evaluated with a manual residue coverage area determination analysis. This proof-of-concept experiment resulted in the independent evaluation of citrus leaves treated with OTC and Kocide 3000. The schematic in Figure 4A represents the process of pesticide residue deposition on citrus leaves in a grove after foliar spray. Due to elevated temperatures in field conditions, the pesticide droplets tend to evaporate, leading to a visible build-up of residue on the surface over time. The area of pesticide residue coverage was assessed manually using RGB color separation with the open source software ImageJ to quantify the pixels within blue (Kocide 3000) and green (OTC) images. Figure 4B,C shows the linear response of pesticide coverage on the leaf surface in relation to the concentration of the applied pesticide. The pesticide residue at a higher concentration exhibits a visible pattern of deposition with a higher coverage area. Both pesticides, OTC and Kocide, showcased good Pearson correlation values (R\({}^{2}\)) of 0.974 and 0.870, respectively. Similar to previously reported work, the OTC residue can only be observed under UV light, and therefore the images for OTC were acquired under this type of light (Figure 4D) [41]. Meanwhile, the Kocide 3000 residue was visible under white light as a light blue solid (Figure 4E). This difference in the light used for residue analysis allows higher sensitivity and visualization of the greater extent of the OTC residue, with a 65.7% coverage at the highest concentrations, in contrast to Kocide 3000, which showed a lower percentage of coverage of up to 4.3% at the highest concentration of copper (Cu).  
Figure 4: (**A**) Schematic representation of the pesticide deposition on the tree leaves after foliar spray. (**B**) Calibration curve of OTC at different concentrations based on green-colored coverage area in the digital images from (**D**) in relation to the leaf surface. (**C**) Calibration curve of Kocide 3000 at different copper concentrations based on blue-colored coverage area in the digital images from (**E**) in relation to the leaf surface. (**D**) Digital images of citrus leaves under UV light exposure showing the OTC residue coverage area. (**E**) Digital images of citrus leaves under white light exposure showing the Kocide 3000 residue coverage area.  
The concentration range used in this proof-of-concept experiment was based on the relevant application rates used in citrus groves, based on the suggested pesticide label. Altogether, these results served as a baseline study to indicate the feasibility of this work towards developing an ML-powered imaging analysis software to determine the pesticide residue coverage on the leaf surface. The highlighted good visual response between the applied pesticide (OTC and Kocide 3000) concentration and the leaf surface coverage area corroborates the suitability of both agrochemicals to be used as model pesticides for image residue analysis. This image analysis tool will foster the development of an affordable and reliable software for growers to assess residue in-field to guide their crop management strategies, reconsidering pesticide use rate and frequency.