Tomato Leaf Disease Detection Using Deep Learning

• Afifa Rubani

  Author

Tomato leaf diseases pose a significant threat to crop yield and quality, demanding timely and accurate diagnosis for effective management. In recent years, deep learning has emerged as a transformative tool in plant disease detection, offering automated, precise, and rapid diagnostic capabilities. This paper presents a comprehensive review of deep learning approaches applied to tomato leaf disease detection from 2020 to 2024. We focus on key architectures including Convolutional Neural Networks (CNNs), transformer-based models, and hybrid frameworks. Special attention is given to Dense Net, Efficient Net, Vision Transformers, and lightweight models, evaluated across metrics such as classification accuracy, computational efficiency, and realworld applicability. Furthermore, we discuss critical challenges in deploying these models in agricultural settings, including data scarcity, model interpretability, and scalability. Finally, we outline future research directions aimed at integrating deep learning technologies into precision agriculture systems for sustainable crop management.

[1] Anderson, R., Wilson, P., and Garcia, M.

(2024). Integrated UAV and IoT

systems for real-time crop disease

monitoring. Computers and Electronics

in Agriculture, 218, 108672.

[2] Balakrishnan, S., Ramesh, T., and

Kumar, P. (2023). Efficient Net-based

deep learning model for tomato leaf

disease classification. Computers and

Electronics in Agriculture, 204, 107551.

[3] Chen, Y., Wang, D., and Liu, Z. (2022).

GAN-based data augmentation for

improving tomato disease

classification. IEEE Access, 10, 45672-

45681.

[4] Chen, X., Li, H., and Zhang, R. (2024).

Transfer learning strategies for plant

disease recognition with limited data.Expert Systems with Applications, 238,

121845.

[5] FAO. 2022. World Food and Agriculture

– Statistical Yearbook 2022. Food and

Agriculture Organization of the United

Nations.

[6] Gupta, A., Sharma, S., and Patel, K.

(2023). Lightweight deep learning

models for mobile plant disease

diagnosis. Journal of Ambient

Intelligence and Humanized

Computing, 14(5), 6123-6135.

[7] Joshi, A., Kumar, V., and Singh, R.

(2024). Vision transformers for plant

disease classification: A comprehensive

evaluation. Computers and Electronics

in Agriculture, 216, 108493.

[8] Kaur, R., Singh, P., and Verma, S. (2024).

Multimodal learning for crop disease

detection: A review. Artificial

Intelligence in Agriculture, 9, 112-125.

[9] Kumar, S., Thompson, B., and Roberts,

M. (2023). DenseNet architectures for

high-accuracy plant disease

classification. IEEE Transactions on

AgriFood Electronics, 1(2), 45-58.

[10] Li, X., Zhang, Y., and Zhou, H. (2023).

Deep learning in precision agriculture:

A survey of recent developments.

Computers and Electronics in

Agriculture, 194, 106773. Miller, J., and

Thompson, L. (2024).

[11] Explainable AI for agricultural decision

support systems. Artificial Intelligence

in Agriculture, 10, 89-104. Patel, N., and

Singh, A. (2023). Residual networks for

plant disease detection: Performance

analysis and optimization. [12] Journal of Plant Diseases and

Protection, 130(3), 567-579. Roberts,

M., and Chen, K. (2023). Addressing

class similarity challenges in plant

disease classification. Plant Methods,

19(1), 34. Sharma, P., and Kumar, D.

(2023).

[13] Convolutional neural networks for

agricultural vision tasks: Architectures

and applications. Computers and

Electronics in Agriculture, 197, 106909.

Singh, R., Wang, H., and Gupta, S.

(2023). Domain adaptation for plant

disease detection in field conditions.

IEEE Geoscience and Remote Sensing

Letters, 20, 1-5. Wang, L., and Liu, H.

(2024).

[14] Hybrid CNN-transformer models for

agricultural image analysis. Computers

and Electronics in Agriculture, 217,

108576. White, S., Brown, T., and Davis,

K. (2023). Federated learning for

privacy-preserving agricultural

analytics. IEEE Internet of Things

Journal, 10(8), 13467-13479. [15] Yang, J., Zhang, Q., and Li, W. (2024).

Model compression techniques for edge

deployment in precision agriculture.

ACM Transactions on Embedded

Computing Systems, 23(2), 1-24.

Zhang, L., and Li, H. (2024).

[16] Mobile-optimized deep learning for infield plant disease diagnosis. Journal of

Field Robotics, 41(3), 567-582. Zhang,

Y., Wang, X., and Liu, Z. (2023).

[17] Recent advances in deep learningbased plant phenotyping: A

comprehensive review. Plant

Phenomics, 5, 0056. Zhou, H., Martinez,

J., and Wilson, K. (2024). Hyperspectral

imaging for early detection of plant

diseases: Challenges and opportunities.

Remote Sensing of Environment, 302,

113965.

• PDF

• 2026-05-26 (3)
• 2026-05-26 (2)
• 2026-05-26 (1)