Weed detection in agricultural fields via automatic graph cut segmentation with Mobile Net classification model

Agriculture is heavily affected by weeds due to their random appearances in fields, competition for water, nutrients, and sunlight, and, if not controlled effectively, negative impact on crop yields. In general, there are many prevention strategies, but they are expensive and time consuming; moreover, labor costs have increased substantially. To overcome these challenges, a novel AGS-MNFELM model has been proposed for weed detection in agricultural fields. Initially, the gathered images are pre-processed using bilateral filter for noise removal and CLAHE for enhancing the image quality. The pre-processed images are taken as an input for automatic graph cut segmentation (AGS) model for segmenting regions with bounding box using the RCNN rather than manual initialization, hence eliminating the need for manual interpretation. The Mobile Net model is used to acquired rich feature representations for a variety of images, and the retrieved features FELM (Fuzzy Extreme Learning Machine Model) classifier is used to classify four weed types of maize and soyabean: cocklebur, redroot pigweed, foxtail, and giant ragweed. The proposed AGS-MNFELM model has been evaluated in terms of its sensitivity, accuracy, specificity, and F1 score. The experimental result reveals that the proposed AGS-MNFELM model attains the overall accuracy of 98.63%. The proposed deep learning-based MobileNet improves the overall accuracy range of 7.91%, 4.15%, 3.44% and 5.88% better than traditional LeNet, AlexNet, DenseNet and ResNet, respectively.