<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>

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Squat exercise is frequently used in physiotherapy rehabilitation for stroke patients. In the early stage of rehabilitation, patients are urged to avoid performing any deep squat as the strains on tendon and ligament are much higher compared to the half-squat exercise. Therefore, it is important for patients to be aware of their squat depth. One of the ways to measure squat depth is by using a wearable device which adds unnecessary weight to the patients and makes them feel uncomfortable. Thus, we propose a single camera system that captures video from the frontal view to measure the squat angle continuously according to the number of frames per second. The system will provide knee angle measurements for every frame taken based on a combined approach of deep learning tracking and deep belief networks regressor. The proposed system requires just a bounding box input of the whole test subject taken at an upright position, which will later be the input to a convolutional neural networks-based tracker. Both the head and upper body parts of the exerciser will be tracked independently. The resultant tracked points will be normalized with the test subject height to find the ratio of height to the corresponding points. The ratio features are then will be the input to multiple deep belief networks' regressors to predict the knee angle. The mean of ratio features will be used to segregate the input frame into its respective regressor. The experimental results show that the system produces the lowest mean error angle of 8.64 • based on the setup of five regressors with each of them consists of five hidden layers. Hence, it is suitable to be implemented in a squat angle monitoring system to notify the patients of their squat angle depth.INDEX TERMS Squat angle analysis, physiotherapy monitoring, visual object tracking, deep belief networks regressor.

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