Convolutional Neural Network Based Ripeness Detection of Fruits

Year : 2024 | Volume : 13 | Issue : 02 | Page : 30 36
    By

    Shreyash Chandrashekhar Bawlekar,

Abstract

The accurate and efficient assessment of fruit ripeness plays a crucial role in ensuring the quality of fruits and optimizing supply chain management. This paper presents a novel approach for the automated detection of apple and banana ripeness using Convolutional Neural Networks (CNNs). The suggested method supports the capability of CNNs to learn hierarchical features from images, variations in color and shape associated with different ripeness stages. The online dataset used comprises a wide range of apple and banana images at various ripeness levels. To make network training and evaluation easier, the dataset is separated into training, validation, and testing sets. A custom CNN architecture is designed and trained on the dataset to provide better results for products on a large scale. By enabling accurate ripeness detection, the model contributes to reducing food waste, enhancing sustainability, and supporting eco-friendly practices in the agricultural and food industries. This would help to contribute in the field of agricultural technology by offering an automated solution for ripeness assessment, reducing the stress on manual inspection and enabling efficient sorting and grading of fruits. The CNN based approach can be further extended to other fruit varieties and has the potential to change the fruit industry by improving post-harvest processes and minimizing food waste.

Keywords: Fruit Ripeness, Convolutional Neural Network, Machine Learning. enabling efficient, demonstrated remarkable

[This article belongs to Research & Reviews : Journal of Agricultural Science and Technology ]

How to cite this article:
Shreyash Chandrashekhar Bawlekar. Convolutional Neural Network Based Ripeness Detection of Fruits. Research & Reviews : Journal of Agricultural Science and Technology. 2024; 13(02):30-36.
How to cite this URL:
Shreyash Chandrashekhar Bawlekar. Convolutional Neural Network Based Ripeness Detection of Fruits. Research & Reviews : Journal of Agricultural Science and Technology. 2024; 13(02):30-36. Available from: https://journals.stmjournals.com/rrjoast/article=2024/view=183422


References

  1. Galal, H., Elsayed, S., Allam, A., Farouk, M. “Indirect Quantitative Analysis of Biochemical Parameters in Banana”, 2022.
  2. Mr. Akshay Dhandrave, Dr. V.T. Gaikwad “Implementation of Fruit Detection System and Checking Fruit Quality using Computer Vision,” IRJET, vol. 8, no. 6, pp. 4116-4120, June 2021.
  3. Brinzel Rodrigues, Revant Kansara, Shruti Singh, Dhruwa Save, Shreya Parihar “Ripe-Unripe: Machine Learning based Ripeness Classification” presented at the the 5th International Conference on Intelligent Computing and Control Systems (ICICCS 2021), Madurai, India.
  4. Raymond Erz Saragih, Andi W. R. Emanuel “Banana Ripeness Classification Based on Deep Learning using Convolutional Neural Network,” presented at the 3rd East Indonesia Conference on Computer and Information Technology(EIConCIT), IEEE, Surabaya, Indonesia, 2021, doi:10.1109/EIConCIT50028.2021.9431928
  5. Zubaidah Al-Mashhadani; Balasubramaniyan Chandrasekaran, “Autonomous Ripeness Detection Using Image Processing for an Agricultural Robotic System,” IEEE, pp. 0743 – 0748, 2020, doi:10.1109/UEMCON51285.2020.9298168
  6. Anuprita Mande, Gayatri Gurav, Kanchan Ajgaonkar, Pooja Ombase, Vaishali Bagul “Detection of Fruit Ripeness Using Image Processing” in Advances in Computing and Data Sciences,Ed(s). Mayank Singh, P. K. Gupta, Prof. Dr. Vipin Tyagi, Jan Flusser, Dr. Tuncer Ören, Springer, Singapore, 2018, pp. 545–555, doi: https://doi.org/10.1007/978-981-13-1813-9_54
  7. Gao, Z., Shao, Y., Xuan, G., Wang, Y., Liu, Y., Han, X., . “Real-time hyperspectral imaging for the in-field estimation of strawberry ripeness with deep learning”. Artif. Intell. Agric. 4, 31–38 Publisher: Elsevier BV,2020.
  8. Aghilinategh, N., Dalvand, M.J., Anvar, A., “Detection of ripeness grades of berries using an electronic nose”. Food Sci. Nutri. 8, 4919–4928 URL: https://doi.org/ 10.1002/fsn3.1788.,2021
  9. R. Dandavate and V. Patodkar. (2020) “CNN and Data Augmentation Based Fruit Classification Model.” Fourth International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC), 2020, pp. 784-787.
  10. Altaheri, H., Alsulaiman, M., Muhammad, G., “Date fruit classification for robotic harvesting in a natural environment using deep learning”. IEEE Access 7, 117115–117133.. https://doi.org/10.1109/ACCESS.2019.2936536. conference Name: IEEE Access.,2019
  11. Klasson, M., Zhang, C., Kjellström, H., “A hierarchical grocery store image dataset with visual and semantic labels”. 2019 IEEE Winter Conference on Applications of Computer Vision (WACV), pp. 491–500 https://doi.org/10.1109/WACV.2019.00058, 2019.
Regular Issue Subscription Article
Volume 13
Issue 02
Received 17/07/2024
Accepted 31/08/2024
Published 31/08/2024
348

Login


My IP

PlumX Metrics