Classification of jujube fruit based on several pricing factors using machine learning methods

• URL: http://arxiv.org/abs/2111.00112v1
• Date: Fri, 29 Oct 2021 23:10:21 GMT
• Title: Classification of jujube fruit based on several pricing factors using machine learning methods
• Authors: Abdollah Zakeri, Ruhollah Hedayati, Mohammad Khedmati, Mehran Taghipour-Gorjikolaie
• Abstract summary: There are several factors in jujube pricing such as weight, wrinkles and defections. Some jujube farmers sell their product all at once, without any proper sorting or classification, for an average price. Our studies and experiences show that their profit can increase significantly if their product is sold after the sorting process.
• Score: 1.7205106391379026
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Jujube is a fruit mainly cultivated in India, China and Iran and has many health benefits. It is sold both fresh and dried. There are several factors in jujube pricing such as weight, wrinkles and defections. Some jujube farmers sell their product all at once, without any proper sorting or classification, for an average price. Our studies and experiences show that their profit can increase significantly if their product is sold after the sorting process. There are some traditional sorting methods for dried jujube fruit but they are costly, time consuming and can be inaccurate due to human error. Nowadays, computer vision combined with machine learning methods, is used increasingly in food industry for sorting and classification purposes and solve many of the traditional sorting methods' problems. In this paper we are proposing a computer vision-based method for grading jujube fruits using machine learning techniques which will take most of the important pricing factors into account and can be used to increase the profit of farmers. In this method we first acquire several images from different samples and then extract their visual features such as color features, shape and size features, texture features, defection and wrinkle features and then we select the most useful features using feature selection algorithms like PCA and CFS. A feature vector is obtained for each sample and we use these vectors to train our classifiers to be able to specify the corresponding pre-defined group for each of the samples. We used different classifiers and training methods in order to obtain the best result and by using decision tree we could reach 98.8% accuracy of the classification.

Related papers

• A novel method for identifying rice seed purity based on hybrid machine learning algorithms [0.0]
  In the grain industry, the identification of seed purity is a crucial task as it is an important factor in evaluating the quality of seeds. This study proposes a novel method for automatically identifying the rice seed purity of a certain rice variety based on hybrid machine learning algorithms.
  arXiv  Detail & Related papers  (2024-06-09T17:13:25Z)
• Few-Shot Fruit Segmentation via Transfer Learning [4.616529139444651]
  We develop a few-shot semantic segmentation framework for infield fruits using transfer learning. Motivated by similar success in urban scene parsing, we propose specialized pre-training. We show that models with pre-training learn to distinguish between fruit still on the trees and fruit that have fallen on the ground.
  arXiv  Detail & Related papers  (2024-05-04T04:05:59Z)
• Food Image Classification and Segmentation with Attention-based Multiple Instance Learning [51.279800092581844]
  The paper presents a weakly supervised methodology for training food image classification and semantic segmentation models. The proposed methodology is based on a multiple instance learning approach in combination with an attention-based mechanism. We conduct experiments on two meta-classes within the FoodSeg103 data set to verify the feasibility of the proposed approach.
  arXiv  Detail & Related papers  (2023-08-22T13:59:47Z)
• An Application of Deep Learning for Sweet Cherry Phenotyping using YOLO Object Detection [8.943378640301734]
  Tree fruit breeding is a long-term activity involving repeated measurements of various fruit quality traits on a large number of samples. These traits are traditionally measured by manually counting the fruits, weighing to indirectly measure the fruit size, and fruit colour is classified subjectively into different color categories using visual comparison to colour charts. Recent advancements in deep learning can help automate this process. A method was developed to automatically count the number of sweet cherry fruits in a camera's field of view in real time using YOLOv3.
  arXiv  Detail & Related papers  (2023-02-13T21:24:09Z)
• Fruit Ripeness Classification: a Survey [59.11160990637616]
  Many automatic methods have been proposed that employ a variety of feature descriptors for the food item to be graded. Machine learning and deep learning techniques dominate the top-performing methods. Deep learning can operate on raw data and thus relieve the users from having to compute complex engineered features.
  arXiv  Detail & Related papers  (2022-12-29T19:32:20Z)
• Facilitated machine learning for image-based fruit quality assessment in developing countries [68.8204255655161]
  Automated image classification is a common task for supervised machine learning in food science. We propose an alternative method based on pre-trained vision transformers (ViTs) It can be easily implemented with limited resources on a standard device.
  arXiv  Detail & Related papers  (2022-07-10T19:52:20Z)
• Learning to Select Cuts for Efficient Mixed-Integer Programming [46.60355046375608]
  We propose a data-driven and generalizable cut selection approach, named Cut Ranking, in the settings of multiple instance learning. Cut Ranking has been deployed in an industrial solver for large-scale MIPs. It has achieved the average speedup ratio of 12.42% over the production solver without any accuracy loss of solution.
  arXiv  Detail & Related papers  (2021-05-28T07:48:34Z)
• Few-shot Image Classification with Multi-Facet Prototypes [48.583388368897126]
  We organize visual features into facets, which intuitively group features of the same kind. It is possible to predict facet importance from a pre-trained embedding of the category names. In particular, we propose an adaptive similarity measure, relying on predicted facet importance weights for a given set of categories.
  arXiv  Detail & Related papers  (2021-02-01T12:43:03Z)
• Fine-Grained Visual Classification via Progressive Multi-Granularity Training of Jigsaw Patches [67.51747235117]
  Fine-grained visual classification (FGVC) is much more challenging than traditional classification tasks. Recent works mainly tackle this problem by focusing on how to locate the most discriminative parts. We propose a novel framework for fine-grained visual classification to tackle these problems.
  arXiv  Detail & Related papers  (2020-03-08T19:27:30Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.