Classification of Fruit Ripeness with Model Descriptor Using Vgg 16 Architecture

The quality of the fruit is largely determined by the level of ripeness contained by the fruit itself. Until now, determining the level of fruit maturity is still done manually, as a result there are differences in perceptions in determining the level of fruit maturity. Therefore we need a system that is able to classify fruit maturity automatically. This research was conducted on 4 objects, namely apples, oranges, mangoes, and tomatoes. The training was conducted with split data with a presentation 70:20:10 based on 4 test scenarios, the data was converted to RGB to L * a * b first and some were not converted and were immediately trained using CNN VGG16 with the transfer learning method where fine tuning would be done on the layer block 5 and modification of the classification layer using the Multi-SVM classifier. The highest accurasi reach 92% at scenario 4 with 90 data per class

A comprehensive review of fruit and vegetable classification techniques

Recent advancements in computer vision have enabled wide-ranging applications in every field of life. One such application area is fresh produce classification, but the classification of fruit and vegetable has proven to be a complex problem and needs to be further developed. Fruit and vegetable classification presents significant challenges due to interclass similarities and irregular intraclass characteristics. Selection of appropriate data acquisition sensors and feature representation approach is also crucial due to the huge diversity of the field. Fruit and vegetable classification methods have been developed for quality assessment and robotic harvesting but the current state-of-the-art has been developed for limited classes and small datasets. The problem is of a multi-dimensional nature and offers significantly hyperdimensional features, which is one of the major challenges with current machine learning approaches. Substantial research has been conducted for the design and analysis of classifiers for hyperdimensional features which require significant computational power to optimise with such features. In recent years numerous machine learning techniques for example, Support Vector Machine (SVM), K-Nearest Neighbour (KNN), Decision Trees, Artificial Neural Networks (ANN) and Convolutional Neural Networks (CNN) have been exploited with many different feature description methods for fruit and vegetable classification in many real-life applications. This paper presents a critical comparison of different state-of-the-art computer vision methods proposed by researchers for classifying fruit and vegetable

Multi-class Fruit Classification using RGB-D Data for Indoor Robots

Abstract — In this paper we present an effective and robust system to classify fruits under varying pose and lighting conditions tailored for an object recognition system on a mobile platform. Therefore, we present results on the effectiveness of our underlying segmentation method using RGB as well as depth cues for the specific technical setup of our robot. A combination of RGB low-level visual feature descriptors and 3D geometric properties is used to retrieve complementary object information for the classification task. The unified approach is validated using two multi-class RGB-D fruit categorization datasets. Experimental results compare different feature sets and classification methods and highlight the effectiveness of the proposed features using a Random Forest classifier