The Application of Remote Sensing-Based Technology in The Field of Tea Identification and Distribution

Abstract

As a significant economic crop cultivated and consumed globally, the yield and quality of tea are directly correlated with the stability and growth of the international tea market. The application of remote sensing technology enables the precise monitoring of tea plant growth, the real-time assessment of soil moisture and nutrient distribution, and the identification of pests and diseases. This technology facilitates the implementation of scientific management practices, thereby enhancing the yield and quality of tea. This paper begins by providing an overview of the remote sensing data sources that can be used for tea monitoring. It then selects two newer remote sensing methods and discusses their potential applications to tea plantations in West Lake, Hangzhou and Bangladesh. There are numerous categories of remote sensing data sources, including satellite remote sensing data and unmanned aerial vehicle (UAV) low-altitude imagery. In the initial case study, the HRNetV2 base deep learning model was employed to detect Longjing tea in West Lake, Hangzhou. The technique integrated satellite remote sensing data with a machine learning model, resulting in a relatively low error rate. The second case study delves into assessing Bangladesh's suitability for sustainable tea land production, leveraging an expert system with satellite remote sensing data and Geographic Information Systems (GIS). This integrated research methodology presents a holistic, precise, and trustworthy framework indispensable for propelling the progression of the tea industry within the country's context.