Identification of the Quality of Tea Leaves by Using Artificial Intelligence Techniques: A Review

Abstract

This paper summarizes the outcome of the survey carried out for quality identification of a tea leaf and eventually price prediction. Quality identification can allow to categorizing leaf in different grades, which helps the buyer and seller to acquire suitable quality to their need. Price prediction is an important feature, which can bring certainty at price and farmers can be benefitted more for their reasonable good quality. Additionally, if the leaf disease is identified at the initial stage that would also allow farmers to timely resolve the concerned issues and save their corps. In the field of agriculture, this has been always a research area to identify and predict the quality of tea leaves. Various artificial intelligence techniques are hot topics in the field of recognition and their effective combination cannot only solve the problem but also enhance recognition accuracy. Therefore, there is an imminent need for a detailed survey on compiling techniques used for the identification of different varieties of tea plants. In this research, we aim to propose a review of the various techniques which can be utilized for determining the quality and price prediction. The survey is hybrid in nature with a combination of different artificial techniques, which is a suitable approach to target effective tea leaf identification. Further for the classification of tea leaf images, various algorithms can be combined as well to obtain better results and different algorithms can be used for feature extraction on the basis of texture extraction, color extraction, and shape extraction.

Classification of Tea Leaves Based on Fluorescence Imaging and Convolutional Neural Networks

“…This technology can be easily transferred to platforms e.g., the smartphone, which owns a strong calculation ability and high-resolution complementary metal oxide semiconductor (CMOS) image sensor, and has already been applied to the fields of food authentication [ 18 ], tea leaf diseases detection [ 19 ], etc. Compared with conventional digital image processing methods, the computer vision shows characteristics of higher stability and precision [ 20 ], and has made preliminary progress on tea leaves [ 21 , 22 , 23 , 24 ]. Bakhshipour et al extracted and evaluated 18 color features, 13 gray image texture features, and 52 wavelet texture features for black tea.…”