Towards Automated Weed Detection Through Two-Stage Semantic Segmentation of Tobacco and Weed Pixels in Aerial Imagery

In precision farming, weed detection is required for precise weedicide application, and the detection of tobacco crops is necessary for pesticide application on tobacco leaves. Automated accurate detection of tobacco and weeds through aerial visual cues holds promise. Precise weed detection in crop field imagery can be treated as a semantic segmentation problem. Many image processing, classical machine learning, and deep learning-based approaches have been devised in the past, out of which deep learning-based techniques promise better accuracies for semantic segmentation, i.e., pixel-level classification. We present a new method that improves the precision of pixel-level inter-class classification of the crop and the weed pixels. The technique applies semantic segmentation in two stages. In stage I, a binary pixel-level classifier is developed to segment background and vegetation. In stage II, a three-class pixel-level classifier is designed to classify background, weeds, and tobacco. The output of the first stage is the input of the second stage. To test our designed classifier, a new tobacco crop aerial dataset was captured and manually labeled pixel-wise. The two-stage semantic segmentation architecture has shown better tobacco and weeds pixel-level classification precision. The intersection over union (IOU) for the tobacco crop was improved from 0.67 to 0.85, and IOU for weeds enhanced from 0.76 to 0.91 with the new approach compared to the traditional one-stage semantic segmentation application. We observe that in stage I shallower, a smaller semantic segmentation model is enough compared to stage II, where a segmentation network with more neurons serves the purpose of good detection

Application of Digital Image Segmentation of Plantation Fruit Classification in Samarinda Agricultural Polytechnic

Applications of Digital Image Segmentation of Plantation Fruit Classification in Samarinda State Agricultural Polytechnic Based on Form The development of computer technology at this time has brought significant progress in various aspects of human life. Such development is supported by the availability of increasingly high hardware and software, one of the technologies experiencing rapid development is image processing. Image processing is a system where the process is carried out by entering an image and the result is also an image. Currently the use of digital images is widely used in various fields one of which is in the plantation sector. Therefore, the purpose of this study is to create a digital image segmentation application for the classification of plantation fruit based on shape. The method used for image segmentation is the Thresholding method, while the image classification uses the Artificial Neural Network (ANN) method. The accuracy generated by the system both in the training process and testing shows that the method used can classify fruit images wel