International Journal of Emerging Research in Science, Engineering, and Management
Vol. 2, Issue 1, pp. 72-78, January 2026.
This work is licensed under a Creative Commons Attribution 4.0 International License.
V. Gopi
M.K. Arthi
Kuruva Lakshmanna
Pillari Chandu
Vattikundala Charan Sai
Yalamjeri Madhu
Department of CSE, Siddartha Institute of Science and Technology, Puttur, India.
Abstract: Tomatoes are consumed all over the world, and their quality has a direct impact on market value, post-harvest processing efficiency, and customer happiness. For large-scale sorting processes, traditional manual inspection techniques for determining ripeness and flaws are inefficient, subjective, and time-consuming. This research suggests an automated tomato quality classification system based on Convolutional Neural Networks (CNNs) and Deep Learning to get over these restrictions. Images of tomatoes are divided into quality classifications by the system, including fresh, medium, and low-quality. To improve resilience against changes in lighting conditions, backdrops, and tomato types, image preparation methods such as scaling, normalization, and data augmentation are used. To guarantee precise categorization, the CNN model automatically learns and extracts important visual characteristics including color, texture, form, and surface flaws. Furthermore, methods like as dropout and hyperparameter tweaking are included.
Keywords: Tomato Quality Assessment, Deep Learning, Convolutional Neural Networks (CNN), Image Classification, Computer Vision.
References:
- Das, F. Pathan, J. R. Jim, M. M. Kabir, and M. F. Mridha, “Deep learning-based classification, detection, and segmentation of tomato leaf diseases: A state-of-the-art review,” Artificial Intelligence in Agriculture, vol. 15, no. 2, pp. 192–220, Feb. 2025, doi: 10.1016/j.aiia.2025.02.006.
- L. Xu, Y. Dong, Y. Zhang, P. Xu, Z. Cai, and J. Zhao, “MLSA: A multi-head latent and self-attention deep learning network for tomato leaf disease identification,” Smart Agricultural Technology, vol. 12, p. 101604, Nov. 2025, doi: 10.1016/j.atech.2025.101604.
- A. Surekha, P. Aswini, M. S. Kumar, V. Manisha, V. N. Reddy, and B. N. K. Reddy, “AI-based food recognition and nutrient prediction,” International Journal of Emerging Research in Science Engineering and Management, vol. 1, no. 6, pp. 44–54, Dec. 2025, doi: 10.58482/ijersem.v1i6.6.
- M. Waseem, M. M. Sajjad, L. H. Naqvi, Y. Majeed, T. U. Rehman, and T. Nadeem, “Deep learning model for precise and rapid prediction of tomato maturity based on image recognition,” Food Physics, vol. 2, p. 100060, Jul. 2025, doi: 10.1016/j.foodp.2025.100060.
- T. Dhar et al., “Molecular validation integrated reliable deep learning approach for early blight severity detection in tomato crops under protected cultivation environments,” Results in Engineering, vol. 28, p. 107739, Oct. 2025, doi: 10.1016/j.rineng.2025.107739.
- Md. S. Alam, Md. R. Ali, and A. Rahman, “Detection and localization of ripe tomato in greenhouse environment using Keras-based deep learning models,” Journal of Agriculture and Food Research, vol. 23, p. 102182, Jul. 2025, doi: 10.1016/j.jafr.2025.102182.
- J. Venkatagiri and Z. Chaitanya, “Effect of microgravity simulation on plant germination,” International Journal of Emerging Research in Science Engineering and Management, vol. 1, no. 1, pp. 10–14, Jul. 2025, doi: 10.58482/ijersem.v1i1.2.
- X. Wang, Y. Hu, C. K. Ang, M. I. Solihin, J.-J. Tiang, and W. H. Lim, “Hyperspectral imaging-based deep learning benchmarks in non-destructive testing of cherry tomatoes,” Applied Food Research, vol. 5, no. 2, p. 101387, Sep. 2025, doi: 10.1016/j.afres.2025.101387.
- Y. -J. Jeon et al., “Volumetric Deep Learning-Based precision phenotyping of Gene-Edited tomato for vertical farming,” Plant Phenomics, vol. 7, no. 3, p. 100095, Aug. 2025, doi: 10.1016/j.plaphe.2025.100095.
- J. Chen et al., “Hyperspectral imaging-driven deep learning approach: Asymptomatic stage detection and severity grading of tomato yellow leaf curl disease,” Smart Agricultural Technology, vol. 12, p. 101281, Aug. 2025, doi: 10.1016/j.atech.2025.101281.
- R. Priyadarshini and C. L. Sudheer, “Comparative study of antioxidant properties in Indigenous medicinal plants of South India,” International Journal of Emerging Research in Science Engineering and Management, vol. 1, no. 2, pp. 14–19, Aug. 2025, doi: 10.58482/ijersem.v1i2.3.
- Y. Wang et al., “Accurate organ segmentation and phenotype extraction of tomato plants based on deep learning and clustering algorithm,” Smart Agricultural Technology, vol. 12, p. 101334, Aug. 2025, doi: 10.1016/j.atech.2025.101334.