Hybrid CNN model for classification of rumex obtusifolius in Grassland

Abstract

Rumex obtusifolius Linnaeus (R. obtu. L.) is one of the vital broad-leaved weeds in grassland that needs removal. It affects dairy products and reduces their quality. Hand-removal methods are costly and time-consuming. Chemical treatment using herbicides has a negative impact on crops and causes environmental pollution. In smart farming, weeding is performed by using computer vision to recognize the weeds efficiently and effectively. Conventional machine learning (ML)-based algorithms face challenges, especially in identifying the weeds in real-world data due to a lack of features. Deep learning (DL) approaches use self-learning to extract all potential features that assist in classifying malignant weed species accurately. Recently, single deep learning methods achieved high performance in identifying well-separated and illumination but suffered from misclassification in more sophisticated cases such as overlapping and partial occlusion leaves. This paper presents a hybrid Convolutional Neural Network (CNN) model of three state-of-the-art CNNs to classify Rumex obtusifolius. The proposed model utilizes convolutional neural networks to extract features and classify images. The framework of the proposed method comprises three paramount stages to accomplish the classification key idea, including the data preparation phase, pre-processing phase, and classification phase. A hybrid model of three CNN extractor networks is used as the backbone in the classification stage. Our tested data is real-world data that includes multi-circumstances (overlap, occlusion, various illuminations, etc.) acquired from nature. The first extractor is the Visual Graphics Group-16 (VGG-16) for well-separated leaves and non-complicated issues. The second extractor is Residential Energy Services Network-50 (ResNet-50), to overcome complex real-world issues. The third extractor is Inception-v3 to solve the illumination problem. Therefore, combining three networks into one model improves the discriminatory ability to extract additional useful features. The proposed model has been tested using two benchmark datasets for Rumex weed plants. Both of these datasets were captured in real-world environments. The first dataset consists of 900 samples, while the second dataset consists of 677 samples. Each dataset is individually tested in the proposed model to evaluate the classification accuracy using a set of standard evaluation metrics including accuracy, precession, recall, True-Positive Rate (TPR), False-Positive Rate (FPR), and F1-score. The total averages of the proposed model on both datasets are 97.51%, 97.4%, 94.45%, and 95.9% on the accuracy, recall, precision, and F1-score, respectively.