Deep Learning-Based Approach for Weed Detection in Potato Crops

Abstract

The digital revolution is transforming agriculture by applying artificial intelligence (AI) techniques. Potato (Solanum tuberosum L.) is one of the most important food crops which is susceptible to different varieties of weeds which not only lower its yield but also affect crop quality. Artificial Intelligence and Computer Vision (CV) techniques have been proven to be state-of-the-art in terms of addressing various agricultural problems. In this study, a dataset of five different potato weeds was collected in different environments and under different climatic conditions such as sunny, cloudy, partly cloudy, and at different times of the day on a weekly basis. For weeds-detection purposes, the Tiny-YOLOv4 model was trained on the collected potato weeds dataset. The proposed model obtained 49.4% mAP value by calculating the IoU. The model trained with high prediction accuracy will later be used as part of a site-specific spraying system to apply agrochemicals for weed management in potato crops.

Keywords

• deep learning
• object detection
• YOLO
• weed detection
• digital agriculture