An Optimum Tea Fermentation Detection Model Based on Deep Convolutional Neural Networks

Predicting the effect of chemicals on fruit using graph neural networks

The neural network method is a type of machine learning that has made significant advances over the past few years in a variety of fields, particularly text, speech, images, videos, etc. In areas where data is unstructured, traditional machine learning has not been able to surpass the 'glass ceiling'; therefore, researchers have turned to neural networks as auxiliary tools to achieve significant breakthroughs or develop new research methods. An array of computational chemistry challenges can be addressed using neural networks, including virtual screening, quantitative structure-activity relationships, protein structure prediction, materials design, quantum chemistry, and property prediction, among others. This paper proposes a strategy for predicting the chemical properties of fruits by using graph neural networks, and it aims to provide some guidance to researchers and streamline the identification process.

Tea Harvesting and Processing Techniques and Its Effect on Phytochemical Profile and Final Quality of Black Tea: A Review

Tea (Camellia sinensis) has grown for over 300 years and is recognized worldwide as among other well-renowned crops. The quality of black tea depends on plucking (method, standard, season, and intervals), withering and rolling (time and temperature), fermentation (time, temperature, and RH), drying (temperature and method), and storage conditions, which have a high influence on the final quality of black tea. At the rolling stage, the oxidation process is initiated and ends at the early drying stage until the enzymes that transform tea polyphenols into thearubigins (TRs) and theaflavins (TFs) are denatured by heat. By increasing fermentation time, TRs increased, and TF decreased. Each is liable for black tea's brightness, taste, and color. The amino acids and essential oils also grant a distinctive taste and aroma to black tea. Throughout withering, rolling, and fermentation, increases were found in essential oil content, but during drying, a decrease was observed. However, the Maillard reaction, which occurs when amino acids react with sugar during drying, reimburses for this decrease and enhances the flavor and color of black tea. As compared to normal conditions, accelerated storage showed a slight decrease in the total color, TF, and TRs. It is concluded that including plucking, each processing step (adopted technique) and storage system has a remarkable impact on black tea's final quality. To maintain the quality, an advanced mechanism is needed to optimize such factors to produce high-quality black tea, and an objective setting technique should be devised to attain the desirable quality characteristics.

Deep Learning and Geometry Flow Vector Using Estimating Vehicle Cuboid Technology in a Monovision Environment

This study introduces a novel model for accurately estimating the cuboid of a road vehicle using a monovision sensor and road geometry information. By leveraging object detection models and core vectors, the proposed model overcomes the limitations of multi-sensor setups and provides a cost-effective solution. The model demonstrates promising results in accurately estimating cuboids by utilizing the magnitudes of core vectors and considering the average ratio of distances. This research contributes to the field of intelligent transportation by offering a practical and efficient approach to 3D bounding box estimation using monovision sensors. We validated feasibility and applicability are through real-world road images captured by CCTV cameras.

A Data Descriptor for Black Tea Fermentation Dataset

Tea is currently the most popular beverage after water. Tea contributes to the livelihood of more than 10 million people globally. There are several categories of tea, but black tea is the most popular, accounting for about 78% of total tea consumption. Processing of black tea involves the following steps: plucking, withering, crushing, tearing and curling, fermentation, drying, sorting, and packaging. Fermentation is the most important step in determining the final quality of the processed tea. Fermentation is a time-bound process and it must take place under certain temperature and humidity conditions. During fermentation, tea color changes from green to coppery brown to signify the attainment of optimum fermentation levels. These parameters are currently manually monitored. At present, there is only one existing dataset on tea fermentation images. This study makes a tea fermentation dataset available, composed of tea fermentation conditions and tea fermentation images.