Comprehensive Analysis of Fruit Variety Classification: Techniques, Challenges, and Application

Fruit variety classification is a crucial aspect in agricultural processes and supply chain management, influencing market competitiveness, and consumer satisfaction. This paper provides a comprehensive review of various fruit variety classification techniques utilizing machine learning (ML) methodologies, highlighting the motivations driving research in this domain and the challenges that researchers and practitioner's encounter. The capabilities of ML algorithms and deep learning (DL) models have facilitated significant advancements in fruit classification accuracy. Motivated by the growing demand globally for fruits and their varieties and the need to optimize resources utilized in agriculture, researchers have focused on developing ML-driven classification systems capable of automating fruit sorting, grading, maturity estimation, and quality control processes. DL particularly has ability to learn complex representations from images, among which the primary architecture is the convolutional neural network (CNN) for applications related to image classification. Based on the extensive literature survey conducted, its observed that utilization of CNN for fruit variety classification has immensely increased generating outstanding results using "from-scratch"or "pretrained"model for transfer learning, however it often struggles with limited datasets, leading to poor generalization, and difficulty in handling variations in fruit appearance due to lighting, orientation, or ripeness. Besides this, the paper presents frameworks, model design, and one practical application on the use of CNN for fruit variety classification.