Fresh Fruit Detection Using Deep Learning

Fresh Fruit Detection Using Deep Learning

Dr. Aniruddha Kailuke*1, Prof. Aparna Bondade*2, Nayan Naik*3, Rahul Meshram*4, Rahul Vishwakarma*5, Rupesh Rithe*6

*1,2 Professors, Department of Artificial Intelligence & Data Science, Priyadarshini College of Engineering, Nagpur, Maharashtra

*3,4,5,6 UG Students, Department of Artificial Intelligence & Data Science, Priyadarshini College of Engineering, Nagpur, Maharashtra

Abstract—The Fresh Fruit Identification System constitutes a notable advancement in the meals industry. Employing sophisticated technologies along with machine-getting-to-know and computer imagination and prescient, it complements the great evaluation and optimization of perishable items, mainly focusing on culmination and greens. This contemporary system employs advanced gadgets gaining knowledge of models, especially convolutional neural networks (CNNs), to meticulously analyze high-decision pics of processed meals. By rapidly and appropriately identifying objects based totally on attributes that include shade, texture, and shape, the machine guarantees no longer the simplest precision in categorization but also an in-intensity best evaluation. This entails defect detection and the assessment of ripeness, making sure that the most effective rate-satisfactory merchandise reaches customers. The implementation of sturdy and scalable systems has adeptly addressed demanding situations associated with facts, model education, and real-time processing. Consequently, the Fresh Food Identification System emerges as a pivotal contributor to raising the standards of the perishable meals supply chain, embodying a dedication to excellence and performance.

Keywords— Fresh Fruit Detection, Machine Learning, Computer Vision, Fruit Quality Assessment, Food Safety, Image Processing,Classification Algorithms, Object Detection, Feature Extraction, Deep Learning,Convolutional Neural Networks (CNN).