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Published Subscription Original Research
A Comparative Analysis of Machine Learning Techniques for Fruit Defect Detection SystemsBy Suraj Mohit Yadav, Tejaswini Sanjay Todkar, Shivam Bhikaji Thorat, Mohammad Jarjish Suleman Siddibapa, Ashvini Gaikwad, Rushikesh Nikam
Abstract: With evolving technologies in machine learning, significant advancements have been made in the livestock industry, helping to reduce waste, increase yield, achieve cost savings, and improve competitiveness in the marketplace. Fruit defect detection models support precision agriculture by providing valuable data for decision-making and enhancing overall efficiency through automated inspection processes. This study implements and comparatively evaluates machine learning models including MobileNetV2, a custom-designed convolutional neural network (CNN) model, ResNet50, …
Published in Journal of Image Processing & Pattern Recognition Progress · Vol. 11, Issue 3, 2024 · pp. 37–47 Read article →