Detection of Mulberry Ripeness Stages Using Deep Learning Models

نویسندگان

چکیده

Ripeness classification is one of the most challenging tasks in postharvest management mulberry fruit. The risks microbial contamination and human error manual sorting are significant; it may result quality degradation wasting processed products. Due to advanced developments computer vision machine learning, automated became possible. This study presents results developing testing a vision-based application using convolutional neural networks (CNNs) for fruit ripening stages. To reduce training cost improve accuracy classification, transfer learning was used fine-tune CNN models. models test include DenseNet, Inception-v3, ResNet-18, ResNet-50, AlexNet. Transfer classification. AlexNet ResNet-18 exhibited best performance with 98.32% 98.65% overall classifying ripeness white black mulberries, respectively. Moreover, did not change when data sets both genotypes were mixed. able classify genotype from 600 images 2.36 min an 98.03%, which superior other architectures. It indicates that model could be precise stages mulberries horticultural products, as part system.

برای دانلود باید عضویت طلایی داشته باشید

برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

منابع مشابه

Concept drift detection in business process logs using deep learning

Process mining provides a bridge between process modeling and analysis on the one hand and data mining on the other hand. Process mining aims at discovering, monitoring, and improving real processes by extracting knowledge from event logs. However, as most business processes change over time (e.g. the effects of new legislation, seasonal effects and etc.), traditional process mining techniques ...

متن کامل

Melanoma detection with a deep learning model

Background: Skin cancer is one of the most common forms of cancer in the world and melanoma is the deadliest type of skin cancer. Both melanoma and melanocytic nevi begin in melanocytes (cells that produce melanin). However, melanocytic nevi are benign whereas melanoma is malignant. This work proposes a deep learning model for classification of these two lesions.    Methods: In this analytic s...

متن کامل

Chord Detection Using Deep Learning

In this paper, we utilize deep learning to learn high-level features for audio chord detection. The learned features, obtained by a deep network in bottleneck architecture, give promising results and outperform state-of-the-art systems. We present and evaluate the results for various methods and configurations, including input pre-processing, a bottleneck architecture, and SVMs vs. HMMs for cho...

متن کامل

Early detection of MS in fMRI images using deep learning techniques

Introduction & Objective:MS is a disease of the central nervous system in which the body makes a defensive attack on its tissues. The disease can affect the brain and spinal cord, causing a wide range of potential symptoms, including balance, movement and vision problems. MRI and fMRI images are a very important tool in the diagnosis and treatment of MS. The aim of this study was to provide...

متن کامل

A Hybrid Optimization Algorithm for Learning Deep Models

Deep learning is one of the subsets of machine learning that is widely used in Artificial Intelligence (AI) field such as natural language processing and machine vision. The learning algorithms require optimization in multiple aspects. Generally, model-based inferences need to solve an optimized problem. In deep learning, the most important problem that can be solved by optimization is neural n...

متن کامل

ذخیره در منابع من


  با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید

ژورنال

عنوان ژورنال: IEEE Access

سال: 2021

ISSN: ['2169-3536']

DOI: https://doi.org/10.1109/access.2021.3096550