Search

Presently, precision agriculture processes like plant disease, crop yield prediction, species recognition, weed detection, and irrigation can be accomplished by the use of computer vision (CV) approaches. Weed plays a vital role in influencing crop productivity. The wastage and pollution of farmland's natural atmosphere instigated by full coverage chemical herbicide spraying are increased. Since the proper identification of weeds from crops helps to reduce the usage of herbicide and improve productivity, this study presents a novel computer vision and deep learning based weed detection and classification (CVDL-WDC) model for precision agriculture. The proposed CVDL-WDC technique intends to properly discriminate the plants as well as weeds. The proposed CVDL-WDC technique involves two processes namely multiscale Faster RCNN based object detection and optimal extreme learning machine (ELM) based weed classification. The parameters of the ELM model are optimally adjusted by the use of farmland fertility optimization (FFO) algorithm. A comprehensive simulation analysis of the CVDL-WDC technique against benchmark dataset reported the enhanced outcomes over its recent approaches interms of several measures. [ABSTRACT FROM AUTHOR]

Recent advancements in wireless technologies enable pervasive and device free gesture recognition that enable assisted living utilizing off the shelf commercial Wi-Fi devices. This paper proposes a Device-Free Wi-Fi-based Sign Language Recognition (DF-WiSLR) for recognizing 30 static and 19 dynamic sign gestures. The raw Channel State Information (CSI) acquired from the Wi-Fi device for 49 sign gestures, with a volunteer performing the sign gestures in home and office environments. The proposed system adopts machine learning classifiers such as SVM, KNN, RF, NB, and a deep learning classifier CNN, for measuring the gesture recognition accuracy. To address the practical limitation of building a voluminous dataset, DF-WiSLR augments the originally acquired CSI values with Additive White Gaussian Noise (AWGN). Higher-order cumulant features of orders 2, 3, and 4 are extracted from the original and augmented data, as the machine learning classifiers demand manual feature extraction. To reduce the computational complexity of machine learning classifiers, an informative and reduced optimal feature subset is selected using MIFS. Whilst the pre-processed original and augmented CSI values directly fed as input to an 8-layer deep CNN, it performs auto feature extraction and selection. DF-WiSLR reported better recognition accuracies with SVM for static and dynamic gestures in both home and office environments. SVM achieved 93.4% 98.8% and 98.9% accuracies in home and office environments respectively, for static gestures. For dynamic gestures, 92.3% recognition accuracy achieved in home environment. On augmented data, the corresponding gesture recognition accuracy values reported are 97.1%, 99.9%, 99.9%, and 98.5%. • Pioneers the study of Indian Sign Language gestures comprising single word and sentences utilizing the CSI of Wi-Fi. • Expands the training data using augmentation scheme. • Higher order feature extraction and selection for improved recognition accuracy. • Adopted several machine learning classifiers and a deep learning classifier for evaluation. • Study the impact of environmental factors and gesture orientation on recognition performance. [ABSTRACT FROM AUTHOR]

• The current study extracts higher-order features from fire images. • A new set of data is correctly labelled for classifying fire and non-fire. • Information-theoretic feature selection is adopted to minimize computational cost. • The SVM performs classification with an RBF kernel. • The model draws an overall accuracy of 96,21%, and a specificity of 97,99%. • The model draws an f-measure and g-mean values of 96,13% and 96,19% respectively. In recent years, wildfires and forest fires have ravaged millions of hectares of the forest all over the world. Recent technological breakthroughs have increased interest in computer vision-based fire classification that classifies fire and non-fire pixels from image or video datasets. Fire pixels from an image or video can be classified using either a traditional machine learning approach or a deep learning approach. Presently, the deep learning approach is the mainstream in forest fire detection studies. Although deep learning algorithms can handle vast amounts of data, they ignore the variation in complexity among training samples and as a result, their training model performance is limited. Furthermore, deep learning approaches with little data and features perform poorly in real-world challenging fire scenarios. As a result, the current study adopts a machine learning technique to extract higher-order features from the processed images from the publicly available datasets: Corsican dataset and FLAME, and a private dataset: Firefront_Gestosa, for classifying fire and non-fire pixels. It should be emphasized that in machine learning applications, handling multidimensional data to train a model is challenging. Feature selection is used to overcome this problem by removing redundant or irrelevant data that has an impact on the model's performance. In this paper, information-theoretic feature selection approaches are used to choose the most important features for classification while minimizing the computational cost. The traditional machine classifier, Support Vector Machine (SVM) is adopted in the present work, that works on the discriminative features input selected from the feature selection technique. The SVM performs the classification of fire and non-fire pixels with a Radial Basis Function (RBF) kernel, and the model's performance is measured using assessment measures such as overall accuracy, sensitivity, specificity, precision, recall, F-measure, and G-mean. The model draws an overall accuracy of 96,21%, a sensitivity of 94,42%, a specificity of 97,99%, a precision of 97,91%, a recall of 94,42%, an f-measure and g-mean values of 96,13% and 96,19% respectively. [ABSTRACT FROM AUTHOR]