Published November 30, 2025 | Version CC-BY-NC-ND 4.0
Journal article Open

Fruit Plant Recognition and Classification from Plant Leaves using Deep Learning, CNN Models

Authors/Creators

  • 1. Department of Computer Science and Engineering, Oriental University, Indore (M.P.), India.

Contributors

  • 1. Department of Computer Science and Engineering, Oriental University, Indore (M.P.), India.

Description

Abstract: Plants are an integral part of human life, and the ability to identify a fruit plant from its leaf image is both fascinating and challenging. Advances in image processing and pattern recognition have enabled plant identification using digital images. Machine learning (ML) and convolutional neural network (CNN) models have demonstrated strong capabilities in handling texture-related features in image processing tasks, including segmentation. In this Paper, we present an approach that utilises ML and CNN models, including AlexNet, Inception, ResNet, LeNet, VGG Net, MobileNet, DenseNet, and GoogLeNet. These models are used to classify fruit plants from leaf images, achieving promising performance on leaf image datasets. Among the evaluated CNN models, MobileNet achieved the highest performance with 94.81% training, 99.57% validation, and 99.44% test accuracy, outperforming all others. LeNet, AlexNet, and ResNet also showed strong results above 93%, while DenseNet, GoogLeNet, and VGGNet achieved moderate accuracy. Inception performed the worst, confirming that MobileNet is the most efficient and reliable model for fruit plant leaf classification.

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Additional details

Identifiers

DOI
10.35940/ijrte.D8303.14041125
EISSN
2277-3878

Dates

Accepted
2025-11-15
Manuscript received on 26 September 2025 | First Revised Manuscript received on 10 October 2025 | Second Revised Manuscript received on 21 October 2025 | Manuscript Accepted on 15 November 2025 | Manuscript published on 30 November 2025.

References

  • Zhang, Y., Cui, J., Wang, Z., Kang, J., & Min, Y. (2020). Leaf Image Recognition Based on Bag of Features. Applied Sciences, 10(15), 5177, DOI: https://doi.org/10.3390/app10155177
  • Kristin, J.D.; Gerard, M.; Sven, D. Computational Texture and Patterns: From Textons to Deep Learning. Synth. Lect. Comput. is. 2018, 8, 1– 113, DOI: https://doi.org/10.2200/S00819ED1V01Y201712COV014
  • Sahu P., Singh A. P., Chug A., and Singh D., "A Systematic Literature Review of Machine Learning Techniques Deployed in Agriculture: A Case Study of Banana Crop" IEEE Access, vol. 10, pp. 87333–87356, Aug. 2022, DOI: https://doi.org/10.1109/ACCESS.2022.3199926
  • Hasim, Abdurrasyid & Herdiyeni, Yeni & Douady, Stéphane. (2016). Leaf Shape Recognition using Centroid Contour Distance. IOP Conference Series: Earth and Environmental Science. 31. 012002, DOI: https://doi.org/10.1088/1755-1315/31/1/012002
  • H. Genitha C, E. Dhinesh, A. Jagan, et al., "Detection of Leaf Disease Using Principal Component Analysis and Linear Support Vector Machine," Advances in Computing, ICoAC, Dec. 2019, DOI: https://doi.org/10.1109/ICoAC48765.2019.246866.
  • M. Heydarian, T. E. Doyle and R. Samavi, "MLCM: Multi-label Confusion Matrix," in IEEE Access, vol. 10, pp. 19083-19095, 2022, DOI: https://doi.org/10.1109/ACCESS.2022.3151048
  • Mustofa, S., Ahad, M. T., Emon, Y. R., & Sarker, A. (2024), "BDPapayaLeaf: A dataset of papaya leaf for disease detection, classification, and analysis," Data in Brief, Volume 57, 2024, 110910, ISSN 2352-3409, DOI: https://doi.org/10.1016/j.dib.2024.110910, Turhal, Ümit. Plant Identification Via Leaf Classification Using Colour and Biometric Features. ISPEC Journal of Agricultural Sciences. 2021, 393-400, DOI: https://doi.org/10.46291/ISPECJASvol5iss2pp393-400
  • A. I. Pathan, K. Patil, D. Patil, H. Patil, J. Patil, and T. Patil, "Application to Detect Fake Reviews Using CNN and Advanced Machine Learning Techniques," International Research Journal of Modernization in Engineering, Technology and Science, vol. 7, no. 6, pp. 2328–2336, Jun 2025, DOI: https://www.doi.org/10.56726/IRJMETS79546
  • Keivani, M., Mazloum, J., Sedaghatfar, E., Tavakoli, M.B. (2020). Automated analysis of leaf shape, texture, and colour features for plant classification. Traitement du Signal, Vol. 37, No. 1, pp. 17-28, DOI: https://doi.org/10.18280/ts.370103.
  • Shivadekar, S., Kataria, B., Hundekari, S., Kirti Wanjale, Balpande, V. P., & Suryawanshi, R. (2023). Deep Learning Based Image Classification of Lungs Radiography for Detecting COVID-19 using a Deep CNN and ResNet 50. International Journal of Intelligent Systems and Applications in Engineering, 11(1s), 241–250, Availble: https://ijisae.org/index.php/IJISAE/article/view/2499
  • Amirtha T., Gokulalakshmi T., and Umamaheshari P., "Machine Learning Based Nutrient Deficiency Detection in Crops", International Journal of Recent Technology and Engineering (IJRTE), ISSN: 2277-3878, vol. 8, Issue 6, pp. 3530-5333, March 2020, Available: https://www.ijrte.org/wp-content/uploads/papers/v8i6/F9322038620.pdf
  • Yasin, Elham & Koklu, Murat. (2023). Utilizing Random Forests for the Classification of Pudina Leaves through Feature Extraction with InceptionV3 and VGG19. Proceedings of the International Conference on New Trends in Applied Sciences, DOI: https://doi.org/10.58190/icontas.2023.48
  • Islam, M.A., Yousuf, M.S.I., Billah, M.M. (2019). Automatic plant detection using HOG and LBP features with SVM. International Journal of Computer (IJC), 33(1): 26-38, DOI: https://doi.org/10.53896/ijc.v33i1.1384
  • Kheirkhah, F.M., Asghari, H. (2018). Plant leaf classification using GIST texture features. IET Computer Vision, 13(4): 369-375. DOI: https://doi.org/10.1049/iet-cvi.2018.5028.
  • Mukti, Zinnia & Rahman, Mohammed, & Nahar, Lutfun. (2022). Leaf Classification Using Machine Learning Algorithms. Journal of Applied Computer Science & Mathematics. 16. 18-23, DOI: https://doi.org/10.4316/JACSM.202201003
  • A. K. Hrithik and V. Kumar, "Classification of Fruit Plants Leaf and Comparative Analysis of Machine Learning and Deep Learning Algorithms," 2022 International Conference on Computing, Communication, and Intelligent Systems (ICCCIS), Greater Noida, India, 2022, pp. 673-680, DOI: https://doi.org/10.1109/ICCCIS56430.2022.10037609
  • Naqvi, S. A., Khan, M. A., Hamza, A., Alsenan, S., Alharbi, M., Teng, S., & Nam, Y. (2024). Fruit and vegetable leaf disease recognition based on a novel custom convolutional neural network and shallow classifier. Frontiers in Plant Science, 15, 1469685. DOI: https://doi.org/10.3389/fpls.2024.1469685.
  • D. Sutaji and H. Rosyid, "Convolutional Neural Network (CNN) Models for Crop Diseases Classification," KINETIK, vol. 7, no. 2, pp. 187–196, May 2022. DOI: https://doi.org/10.22219/kinetik.v7i2.1443
  • Ajahar Ismailkha Pathan, and Swati Pandey. (2025). Fruits Plant Leaf [Data set]. Kaggle. https://doi.org/10.34740/KAGGLE/DSV/12928903.
  • Epsita Medhi, Nabamita Deb, PSFD-Musa: a dataset of banana plant, stem, fruit, leaf, and disease, Data in Brief (2022), DOI: https://doi.org/10.17632/4wyymrcpyz.1
  • Ajahar Pathan, Swati Pandey, "Papaya Leaves", IEEE Dataport, September 4, 2025, DOI: https://doi.org/10.21227/r5gx-yw47
  • R. Pathak and H. Makwana, "Classification of fruits using convolutional neural network and transfer learning models," Journal of Management Information and Decision Sciences, vol. 24, pp. 1–12, 2021, Available: https://www.abacademies.org/articles/classification-of-fruits-using-con volutional-neural-network-and-transfer-learning-models.pdf
  • M. E. Irhebhude, A. O. Kolawole, and C. Chinyio, "Classification of plants by their fruits and leaves using convolutional neural networks," Science in Information Technology Letters, vol. 5, no. 1, pp. 1–15, May 2024, DOI: https://doi.org/10.31763/sitech.v5i1.1364
  • G. S. Hukkeri, B. C. Soundarya, H. L. Gururaj, and V. Ravi, "Classification of various plant leaf disease using pretrained convolutional neural network on ImageNet," The Open Agriculture Journal, vol. 18, pp. e18743315305194, 2024, DOI: https://doi.org/10.2174/0118743315305194240408034912
  • Salim, Farsana & Saeed, Faisal & Basurra, Shadi & Qasem, Sultan & Al-Hadhrami, Tawfik. (2023). DenseNet-201 and Xception Pre-Trained Deep Learning Models for Fruit Recognition. Electronics. 12. 3132. DOI: https://doi.org/10.3390/electronics12143132
  • Q. Xiang, et al., Fruit image classification based on Mobilenetv2 with transfer learning technique, Proceedings of the 3rd International Conference on Computer Science and Application Engineering (2019), DOI: https://doi.org/10.3390/su15031906
  • D. Hughes and M. Salathé, "An open access repository of images on plant health to enable the development of mobile disease diagnostics," arXiv preprint arXiv:1511.08060, 2015, DOI: https://doi.org/10.48550/arXiv.1511.08060
  • Kaur, S., Joshi, G., & Vig, R. (2019). Plant Disease Classification using the Deep Learning Google Net Model. International Journal of Innovative Technology and Exploring Engineering, Available: https://www.ijitee.org/wp-content/uploads/papers/v8i9S/I10510789S19 .pdf