Weeds Detection Using Mask R-CNN and Yolov5

Agriculture have seen a decrease in yields crops due to weeds, so they were encouraged to use pesticides and chemical treatments to eliminate them and obtain the results despite their environmental damage, another hand the new IA technology can be give best solution help farmers to detect and elimination weeds. The detection of objects is crucial in many computer vision applications, and our focus is on detecting weeds, which pose a significant challenge in agriculture due to their negative impact on harvest yield and quality. In this study, we evaluate two state-of-the-art convolutional neural network-based object detectors for weed detection under real-world conditions without staging. Our evaluation considers both speed and accuracy metrics using a dataset of images captured using a smartphone camera, the performance of weed detection is evaluated according to a predefined geographic location in Tebessa, an Algerian eastern region. Additionally, we compare the performance of these models with and without additional training using examples from different databases.