Abstract:
Maintaining a healthy and robust lifestyle necessitates the consumption of
nutritious food, with vegetables playing a pivotal role. Among the different types
of vegetables, leafy vegetables are one of them which provide a rich source of
important nutrients, including vitamins, minerals, and antioxidants. However, it
might be a difficult task to correctly identify and distinguish between various leafy
vegetables, especially when they have a similar appearance. The challenges can be
addressed by replacing the monitoring system with an automated system for leafy
vegetable Identification. The aim of this study is to propose a leafy vegetable
identifying system using a transfer learning model. A sophisticated machine
learning method called transfer learning is used to improve the performance of
recognition and classification utilizing image processing. The proposed model
focuses on using a pre-trained CNN model to extract leaf attributes and Analysis
different kinds of leafy vegetables and identify the actual name of leafy vegetables.
In this study, a custom dataset is used for implementing the transfer learning model
utilizing image processing which consists of six types of leafy vegetables, including
Colocasia, Red Spinach, Malabar Spinach, Jute Spinach, Organic Spinach, and
Stem Amarnath. Evaluating the models on the custom dataset, InceptionV3
achieved the highest accuracy which is 99.75% based on performance metrics such
as precision, recall, and f1-score. Thus, this study reduces the eye dependency of
humans and consumes the time of the general public.
