Invading pests and diseases always degrade the quality and quantity of plants. Early and accurate identification of plant diseases is critical for plant health and growth. This work proposes a smartphone-based solution using a Vision Transformer (ViT) model for identifying healthy plants and unhealthy plants with diseases. The collected dataset of tomato leaves was used to collectively train Vision Transformer and Inception V3-based deep learning (DL) models to differentiate healthy and diseased plants. These models detected 10 different tomato disease classes from the dataset containing 10,010 images. The performance of the two DL models was compared. This work also presents a smartphone-based application (Android App) using a ViT-based model, which works on the basis of the self-attention mechanism and yielded a better performance (90.99% testing) than Inception V3 in our experimentation. The proposed ViT-SmartAgri is promising and can be implemented on a colossal scale for smart agriculture, thus inspiring future work in this area.

Medicinal plants are a potential source of income particularly for rural populations in India who rely on these medicinal plants to treat a variety of different diseases through both targeted temporary and daily use. Through this data paper we have given a reference to our collected specimen set where almost 117 (one hundred seventeen) medicinal plant species with their leaf samples are stored. We have used Mendeley platform to store the dataset and visited many medicinal plant gardens situated in Assam to collect them. The dataset consists of raw leaf samples, U-net segmented gray leaf samples and a plant name table. The table includes Botanical name, family of the species, Common name and Assamese name. For segmentation U-net model was used and the resultant U-net segmented gray image frames are uploaded to the database. These segmented samples can be directly used for deep learning model for training and classification. Researchers will be able to use those to build recognition tool for Android or PC based system.

This paper discusses a deep learning-based image processing method to recognize human emotion from their facial expression with special concentration on infant’s face between one to five years of age. The work has importance because most of the time it becomes necessary to understand need of a child from their facial expression and behavior. This work is still a challenge in the field of Human Facial Emotion Recognition due to confusing facial expression that sometimes found in some of the samples. We have tried to recognize any facial expression into one of the mostly understood human mood namely Angry, Disgust, Fear, Happy, Sad, Surprise and Neutral. For this purpose, we have trained an image classifier with Convolutional Neural Network with Kaggle's Fer2013 Dataset. After the completion of the project, we achieved good accuracy in most of the prominent emotions by testing with 20 random images for each emotion.

Accurate diagnosis and well-informed treatment choices are only possible with the help of sophisticated medical imaging technology. The introduction of digital imaging methods has led to a dramatic rise in the volume of data associated with medical images, which in turn has led to difficulties in their storage, transmission, and administration. It is crucial to utilize effective image compression techniques to address these challenges without compromising the diagnostic integrity of the pictures. The wavelet transform has matured into a potent method for striking a good compromise between picture quality and file size reduction while compressing. The safe and efficient transfer of medical image data is a major concern in today’s healthcare settings. In this academic investigation, we investigate how wavelet transform-based techniques may be used to enhance medical picture compression. The proposed techniques optimize compression ratios while maintaining diagnostic picture quality by making use of the wavelet transform’s multi-resolution and frequency localization features. To address the unique challenges given by medical image collections, various iterations of the wavelet transform and compression techniques are investigated. Through a series of detailed tests involving several medical picture modalities, the effectiveness of these technologies is thoroughly evaluated, demonstrating their effectiveness in achieving significant data reduction without sacrificing clinical information.

Sattriya is an Indian classical dance of Assam composed of complex hand gestures, body moments, facial expressions and background music. At a first glance for common people meaning of mudras incorporated in Sattriya dance become difficult to understand. Authors have generated a dataset of Sattriya double handed gestures performed by total fifty artists from different dance academies which can be used to explore ways to build an automated system using machine learning and deep learning algorithms. There are eight double handed gestures or mudras made available through the referred dataset. Mendeley platform is used to store the dataset and samples were collected by visiting some protruding Sattriya dance schools located in Guwahati, Assam. In this article, various tables are used to illustrate variations of the mudras in terms of appearance, title, and compared to other Indian classical dances. To segment and analyze the mudras watershed segmentation is used. In this article researchers have enumerated a few very specific characteristics of Sattriya mudra that may be helpful to academics working in various disciplines of study.