A Review on Tomato Quality Classification Using Transfer Learning and Machine Learning Classifiers

Author(s)	Mr. SALLAUDDIN MAHAMMAD, Dr. HARISH KUMAR K
Country	India
Abstract	Tomato quality classification plays an important role in modern agriculture, influencing both market value and supply chain efficiency. Manual sorting methods are often unreliable and impractical for large-scale processing. Recent advances in computer vision and artificial intelligence have led to the development of automated classification systems. This review investigates hybrid approaches that combine deep feature extraction using transfer learning models with traditional classifiers such as SVM, decision trees, and instance-based learners. The paper summarizes recent techniques used for tomato ripeness and quality grading, analyzing the datasets, model performance, architectural improvements, and deployment challenges. It further highlights potential directions to enhance scalability, efficiency, and practical implementation in agricultural environments.
Keywords	Tomato quality classification, Transfer learning, Convolutional neural networks, Machine learning classifiers, InceptionV3, Agricultural automation, Feature extraction, Hybrid models
Field	Computer > Artificial Intelligence / Simulation / Virtual Reality
Published In	Volume 16, Issue 3, July-September 2025
Published On	2025-08-10

About IJSAT Fees & Payment Current Issue Publication Archive	Submit Research Paper Track Submission Status Publication Guidelines Publication Ethics Peer Review & Plagiarism	Join as a Reviewer Editors & Reviewers Reviewer Referral Program Get Reviewer Membership Certi.	Website/Journal Policies Usage Policy Content Policies Privacy Policy

Contact Us	Message on WhatsApp	+91-9687-182-185	editor@ijsat.org

International Journal on Science and Technology