Optimizing herbicide reduction: A simulation approach using Artificial Intelligence and different nozzle configurations

Targeted application aims to minimize product usage by spraying only where needed. However, there is a lack of tools to evaluate the potential savings and the model's limitations. To address this, we developed a computer simulation using two YOLOv8x models (bounding boxes and segmentation masks) to assess spray volume reduction (SVR) and nozzle limitations across varying weed densities. We used Python, OpenCV, and Tkinter to analyze videos from row-middle trials at the University of Florida. The system tracks weeds crossing a horizontal detection line within a 70 cm row-middle width, dividing the frame into one to eight vertical zones representing nozzle distribution. When a weed is detected, the corresponding frame is saved. The system calculates the activation time in seconds by considering the total number of video frames. Spray volume calculations were based on manual measurements of a TeeJet 8001VS nozzle tip, which dispenses a known volume of liquid per second at 35 PSI. The activation time was multiplied by this rate to estimate the targeted spray volume. The broadcast application volume was calculated by multiplying the total video duration by the same tip output. The results showed that the models achieved up to 96 % accuracy (mAP) with no statistical difference. A polynomial model for low and medium weed densities demonstrated SVR of 74 % (six nozzles) and 57 % (seven nozzles). A linear model for high density achieved a 40 % reduction. The lowest reduction occurred with a single nozzle (4 % for medium, 2 % for high density). These findings demonstrate that nozzle density significantly impacted spray reduction at medium and high densities while low-density savings remained consistent. • Computer simulation to assess herbicide savings. • Herbicide savings depend on weed density. • Develop simulation models to evaluate targeted agrochemical application efficiency. • Using Yolo for optimal nozzle setup identification for maximum agrochemical reduction. • Bounding boxes and segmentation masks did not impact weed detection accuracy.