Optimizing Mango Harvest Timing in the Nasik Region (Maharashtra, India) by CNNs (Residual Network 101)

Notice

This is an unedited manuscript accepted for publication and provided as an Article in Press for early access at the author’s request. The article will undergo copyediting, typesetting, and galley proof review before final publication. Please be aware that errors may be identified during production that could affect the content. All legal disclaimers of the journal apply.

Year : 2026 | Volume : 13 | 01 | Page :
    By

    Rajesh Kapur,

  • Rajesh Kulkarni,

  1. Professor, Department of MCA, Thakur Institute of Management Studies Career Development and Research, Mumbai, Maharashtra, India
  2. Associate Professor, Department of Computer Science and Engineering, Maturi Venkata Subba Rao (Mvsr) Engineering College, smania university, Telangana, India

Abstract

The determination of optimal harvest timing is one of the most critical decisions in mango production, directly affecting postharvest quality, market value, transportation resilience, and export readiness. In regions such as Nashik, Maharashtra—one of India’s major fruit- producing belts—the climatic variability, cultivar differences, monsoon patterns, and market- driven pressures make accurate harvest timing essential. Traditional maturity assessment relies on subjective visual inspection, specific gravity, or destructive testing, each of which is limited in precision and scalability. This has enhanced the motivation to applying image processing and deep learning (DL), especially convolutional neural networks (CNNs), to analyze mango images and infer maturity stages objectively. We will leverage an appropriate publicly available dataset, such as the Temporal Mango fruit Dataset or similar ripeness- focused collections from platforms like Roboflow/Kaggle, which contain labelled images across various maturity stages. The paper details the CNN architecture (ResNet), and the methodology to for the development of a maturity classification model. The findings will establish a robust, scalable framework for precision agriculture, providing Nasik’s mango growers with a data-driven tool for optimizing harvest windows, thereby enhancing fruit quality, reducing post-harvest losses, and improving market competitiveness.

Keywords: Mango Maturity Classification, Convolutional Neural Networks (CNN), ResNet-101, Precision Agriculture, Harvest Timing Optimization

How to cite this article:
Rajesh Kapur, Rajesh Kulkarni. Optimizing Mango Harvest Timing in the Nasik Region (Maharashtra, India) by CNNs (Residual Network 101). Journal of Image Processing & Pattern Recognition Progress. 2026; 13(01):-.
How to cite this URL:
Rajesh Kapur, Rajesh Kulkarni. Optimizing Mango Harvest Timing in the Nasik Region (Maharashtra, India) by CNNs (Residual Network 101). Journal of Image Processing & Pattern Recognition Progress. 2026; 13(01):-. Available from: https://journals.stmjournals.com/joipprp/article=2026/view=241156


References

  1. A. Sam and M. G. Manikandan, “Deep learning-based fruit maturity detection using convolutional neural networks,” International Journal of Soft Computing and Artificial Intelligence, vol. 6, no. 4, pp. 22–30, 2024.
  2. Ashok, P. Bharathi, and C. Shivakumara, “Machine learning-based non-destructive mango fruit disease classification with a medium-scale dataset,” International Journal of Image, Graphics and Signal Processing, vol. 15, no. 4, pp. 58–67, 2023.
  3. Yadav, S. K. Jain, D. Rajpurohit, K. K. Meena, and K. Jain, “Fruit maturity detection using deep learning: An overview,” International Journal of Biochemistry Research, vol. 8, no. 7S, pp. 1597–1605, 2024.
  4. Sreeram and J. Kathirvelan, “Non-invasive detection of artificially ripened mangoes using computer vision and deep learning,” Food Production, Processing and Nutrition, vol. 6, 2025.
  5. Gavali, S. Thigale, P. Togrikar, S. Karve, and Ubale, “Ripeness detection of mango using Faster R-CNN,” International Journal of Intelligent Systems and Applications in Engineering, 2023.
  6. V. Mante and M. V. Yeul, “Mango fruit disease detection and ripeness classification using VGG16 transfer learning,” International Journal for Research Trends and Innovations, vol. 10, no. 5, pp. 1–6, 2025.
  7. Neethi and N. Raviraj, “Mango crop yield estimation using convolutional neural networks,” International Journal of Soft Computing and Artificial Intelligence, vol. 9, no. 1, pp. 1–10, 2025.
  8. Nithya, S. Santhi, N. Manikandan, A. Rahimi, and A. H. Gandomi, “A computer vision system for mango surface defect detection using a deep convolutional neural network,” Scientific Reports, vol. 12, 2022.
  9. He, , Zhang, X., Ren, S., & Sun, J. (2016). Deep Residual Learning for Image Recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 770-778.
  1. Huang, G., Liu, Z., van der Maaten, L., & Weinberger, K. Q. (2017). Densely Connected Convolutional Networks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 4700-4708.
  2. Ronneberger, O., Fischer, P., & Brox, T. (2015). U-Net: Convolutional Networks for Biomedical Image Segmentation. International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI), 234-241.
  3. Lin, T. Y., Goyal, P., Girshick, R., He, K., & Dollár, P. (2017). Focal Loss for Dense Object Detection. Proceedings of the IEEE International Conference on Computer Vision (ICCV), 2980-2988.
  4. Thambi and R. Ramalingam, “Utilizing deep learning algorithms for fruit ripeness classification,” in AIP Conference Proceedings, vol. 3112, 2024.
  5. Yosinski, J., Clune, J., Lu, Y., & Lipson, H. (2014). “How transferable are features in deep neural networks?” Advances in Neural Information Processing Systems (NIPS), 3320–
  6. Reference: Ganin, Y., & Lempitsky, V. (2016). “Domain-Adversarial Training of Neural Networks (DANN).” Journal of Machine Learning Research, 17(59), 1–35.
  7. Shu, Y., Zhang, J., Wang, Y., & Wei, Y. (2025). Fruit Freshness Classification and Detection Based on the ResNet-101 Network and Non-Local Attention Mechanism. Foods, 14(11), https://doi.org/10.3390/foods14111987
Ahead of Print Subscription Original Research
Volume 13
01
Received 27/12/2025
Accepted 21/02/2026
Published 28/04/2026
Publication Time 122 Days
24

Login


My IP

PlumX Metrics