Weed and Crop Detection in Rice Field Using R-CNN and Its Hybrid Models

Mostly, Weeds are the responsible for agricultural losses in recent years. Removing weeds is a challenging task as there are much similarity between weed and crop in terms of texture, color and shape. To deal with this challenge, a farmer needs to spray herbicides uniformly throughout the field. In addition to requiring a lot of pesticides, this method has an adverse effect on the environment and people’s health. To overcome this, precision agriculture is used. Unmanned aerial vehicles (UAVs) have been shown great prospective for weed detection, as they can cover large areas of farmland quickly and efficiently. For this experiment, phantom p4 drone was used to take the images of rice field. Therefore, in this work, we propound a weed recognition system using UAVs and a combination of RCNN model and modified RCNN-LSTM and RCNN-GRU. The performance was compared using accuracy, precision, recall, and f1-score as evaluation criteria. Among all RCNN with GRU outperformed with 97.88%.