Real-time precision spraying application for tobacco plants

Abstract

This paper introduces a precision agriculture application aimed at mitigating the excessive utilization of agricultural chemicals, including pesticides and fungicides during crop spraying. The prevailing spraying techniques face two principle challenges: first, the indiscriminate dispensation of chemicals irrespective of plant size and requirements and second, the farmer's exposure to health hazards. To tackle these issues, a detection and segmentation model employing both YOLOv5 and YOLOv6 architectures is proposed and a comparative assessment of their accuracies within the same model category is conducted. The training dataset originates from a subset of the TobSet dataset, while the evaluation of the trained models is executed using publicly accessible aerial videos/images from available repository. The best detection accuracy achieved for the tobacco plant model size is observed with YOLOv6s and the YOLOv5-segmentation model, yielding accuracies of 95% and 94.8%, respectively. Additional performance metrics such as precision, recall, area under the PR-curve, inference time, and NMS per image are also compared between the two models. The YOLOv5-segmentation model excels by outperforming the YOLOv6s model in precision, recall score, and area under the PR-curve whereas slightly extended inference time and NMS per image duration are noted for the YOLOv5-segmentation model and the speed performance is comparable for the two models. Subsequently, the evaluation of these two models is conducted on the drone videos, which were recorded during drone traversal at a speed of 2 km/hr. The results demonstrate superiority of YOLOv5-segmentation model over the YOLOv6s model, with detection accuracies of 98.1% and 97.3%, respectively. These findings indicate the potential of integrating YOLOv5 segmentation models in precision spraying applications and contribute in improving the overall agricultural practices.