<span lang="EN-US">Tomato is a red-colored edible fruit originated from the American continent. There are a lot of plant diseases associated with tomatoes such as leaf mold, late blight, and mosaic virus. Tomato is an important vegetable crop that contributes to the world economically. Despite tremendous efforts in plant management, viral diseases are notoriously difficult to control and eradicate completely. Thus, accurate and faster detection of plant diseases is needed to mitigate the problem at the early stage. A computer vision approach is proposed to identify the disease by capturing the leaf images and detect the possibility of the diseases. A deep learning classifier is utilized to make a robust decision that covers a wide variety of leaf appearances. Compact deep learning architecture, which is MobileNet V2 has been fine-tuned to detect three types of tomato diseases. The algorithm is tested on 4,671 images from PlantVillage dataset. The results show that MobileNet V2 is able to detect the disease up to more than 90% accuracy.</span>
Facial expression is one of the obvious cues that humans used to express their emotions. It is a necessary aspect of social communication between humans in their daily lives. However, humans do hide their real emotions in certain circumstances. Therefore, facial micro-expression has been observed and analyzed to reveal the true human emotions. However, micro-expression is a complicated type of signal that manifests only briefly. Hence, machine learning techniques have been used to perform micro-expression recognition. This paper introduces a compact deep learning architecture to classify and recognize human emotions of three categories, which are positive, negative, and surprise. This study utilizes the deep learning approach so that optimal features of interest can be extracted even with a limited number of training samples. To further improve the recognition performance, a multi-scale module through the spatial pyramid pooling network is embedded into the compact network to capture facial expressions of various sizes. The base model is derived from the VGG-M model, which is then validated by using combined datasets of CASMEII, SMIC, and SAMM. Moreover, various configurations of the spatial pyramid pooling layer were analyzed to find out the most optimal network setting for the micro-expression recognition task. The experimental results show that the addition of a multiscale module has managed to increase the recognition performance. The best network configuration from the experiment is composed of five parallel network branches that are placed after the second layer of the base model with pooling kernel sizes of two, three, four, five, and six.
Understanding a person’s attitude or sentiment from their facial expressions has long been a straightforward task for humans. Numerous methods and techniques have been used to classify and interpret human emotions that are commonly communicated through facial expressions, with either macro- or micro-expressions. However, performing this task using computer-based techniques or algorithms has been proven to be extremely difficult, whereby it is a time-consuming task to annotate it manually. Compared to macro-expressions, micro-expressions manifest the real emotional cues of a human, which they try to suppress and hide. Different methods and algorithms for recognizing emotions using micro-expressions are examined in this research, and the results are presented in a comparative approach. The proposed technique is based on a multi-scale deep learning approach that aims to extract facial cues of various subjects under various conditions. Then, two popular multi-scale approaches are explored, Spatial Pyramid Pooling (SPP) and Atrous Spatial Pyramid Pooling (ASPP), which are then optimized to suit the purpose of emotion recognition using micro-expression cues. There are four new architectures introduced in this paper based on multi-layer multi-scale convolutional networks using both direct and waterfall network flows. The experimental results show that the ASPP module with waterfall network flow, which we coined as WASPP-Net, outperforms the state-of-the-art benchmark techniques with an accuracy of 80.5%. For future work, a high-resolution approach to multi-scale approaches can be explored to further improve the recognition performance.
Automatic ship detection on remote sensing images is one of the important modules in the maritime surveillance system. Its main task is to detect possible pirate threats as early as possible. Thus, the detection system must be accurate enough as it plays a vital role in national security. Therefore, this paper proposes a deep learning approach to detect the presence of a ship in the harbour areas. DenseNet architecture has been selected as the core convolutional neural network-based classifier, where various finetuning has been done to find the optimal setup. The three hyperparameters that have been fine-tuned are optimizer selection, batch size, and learning rate. The experimental results show a success rate of over 99.75% when Adam optimizer is selected with a learning rate of 0.0001. The test was done on the Kaggle Ships dataset with 4,200 images. This algorithm can be further fine-tuned by considering other types of convolutional neural network architecture to increase detection accuracy.
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