Proceedings of the International Conference on Sustainable Innovation with Artificial Intelligence and Machine Learning 2025 (ICSIAIML 2025)

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Dual-Task Convolutional Neural Network for Fruit Classification and Ripeness Prediction

Authors
Vidula V. Meshram1, Kailas Patil1, *, Vishal A. Meshram2, Ajay S. Chhajed2, Rajni Jadhav1, 2, Rushikesh Tanksale1, 2
1Vishwakarma University Kondhwa Main Rd Betal Nagar, Survey No 2 3, 4 Laxmi Nagar, Kondhwa, Pune, India, 411048
2Vishwakarma Institute of Technology, Upper Indira Nagar, Bibwewadi, Pune, India, 411037
*Corresponding author. Email: kasilas.patil@vupune.ac.in
Corresponding Author
Kailas Patil
Available Online 6 January 2026.
DOI
10.2991/978-94-6463-948-3_42How to use a DOI?
Keywords
Fruit Detection; Maturity Prediction; Convolutional Neural Network; Smart Agriculture
Abstract

This work proposed a novel Convolutional Neural Network (CNN) that can classify fruits and predict when they will be ripe is presented with higher accuracy. The architecture consists of task-specific nodes for classification and regression and a centralized node for shared feature extraction. Preprocessing methods like resizing and normalization are applied on input images. The division of the dataset is done into separate sets for training, validation and testing to ensure a robust evaluation. The categorical cross-entropy technique is used to categorize objects and mean squared error to train the network to assess maturity. Performance is frequently assessed considering both the accuracy and mean absolute error (MAE). The testing results show the efficacy of the model and its potential for use in different contexts, with an extraordinary classification accuracy of approximately 94.2% and a low maturity prediction error of approximately 0.21 to 0.34.

Copyright
© 2025 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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Volume Title
Proceedings of the International Conference on Sustainable Innovation with Artificial Intelligence and Machine Learning 2025 (ICSIAIML 2025)
Series
Advances in Intelligent Systems Research
Publication Date
6 January 2026
ISBN
978-94-6463-948-3
ISSN
1951-6851
DOI
10.2991/978-94-6463-948-3_42How to use a DOI?
Copyright
© 2025 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Cite this article

risenwbib
TY  - CONF
AU  - Vidula V. Meshram
AU  - Kailas Patil
AU  - Vishal A. Meshram
AU  - Ajay S. Chhajed
AU  - Rajni Jadhav
AU  - Rushikesh Tanksale
PY  - 2026
DA  - 2026/01/06
TI  - Dual-Task Convolutional Neural Network for Fruit Classification and Ripeness Prediction
BT  - Proceedings of the International Conference on Sustainable Innovation with Artificial Intelligence and Machine Learning 2025 (ICSIAIML 2025)
PB  - Atlantis Press
SP  - 593
EP  - 615
SN  - 1951-6851
UR  - https://doi.org/10.2991/978-94-6463-948-3_42
DO  - 10.2991/978-94-6463-948-3_42
ID  - Meshram2026
ER  -