Smart Agriculture Using Advancing Tea Leaf Quality Assessment With Deep Learning

Abstract

The primary objective of this chapter is to conduct a comprehensive study tailored to classify, detect, and accurately evaluate the quality of tea leaves based on their age. The research aims to serve as a foundational resource, enabling the effective deployment of advanced machine learning algorithms for automating the quality assessment process. By replacing or supplementing manual inspection, these algorithms can provide more precise, reliable, and scalable solutions for quality evaluation. Such an approach is particularly significant for large-scale tea production, where consistent quality control is vital for maintaining market competitiveness. Moreover, this study aspires to deepen the understanding of the relationship between tea leaf age and quality, offering valuable insights into how leaf maturity impacts characteristics such as flavor, texture, and nutritional content.These advancements have the potential to catalyze broader applications of technology in agriculture, fostering innovation and sustainability across the sector.