STM Journals Peer-reviewed research from Consortium e-Learning Network

Research & Reviews : Journal of Agricultural Science and Technology Article

Convolutional Neural Network Based Ripeness Detection of Fruits

  1. 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

References (11)

  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. Saragih RE, Emanuel AWR. Banana Ripeness Classification Based on Deep Learning using Convolutional Neural Network. 2021 3rd East Indonesia Conference on Computer and Information Technology (EIConCIT). 2021:85-89. doi:10.1109/eiconcit50028.2021.9431928
  5. Al-Mashhadani Z, Chandrasekaran B. Autonomous Ripeness Detection Using Image Processing for an Agricultural Robotic System. 2020 11th IEEE Annual Ubiquitous Computing, Electronics & Mobile Communication Conference (UEMCON). 2020:0743-0748. doi:10.1109/uemcon51285.2020.9298168
  6. Mande A, Gurav G, Ajgaonkar K, Ombase P, Bagul V. Detection of Fruit Ripeness Using Image Processing. Communications in Computer and Information Science. 2018:545-555. doi: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 MJ, Anvar A. Detection of ripeness grades of berries using an electronic nose. Food Science & Nutrition. 2020;8(9):4919-4928. doi:10.1002/fsn3.1788
  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. 2019;7:117115-117133. doi:10.1109/access.2019.2936536
  11. Klasson M, Zhang C, Kjellstrom H. A Hierarchical Grocery Store Image Dataset With Visual and Semantic Labels. 2019 IEEE Winter Conference on Applications of Computer Vision (WACV). 2019:491-500. doi:10.1109/wacv.2019.00058
Support