Improving Accuracy of Corn Leaf Disease Recognition Through Image Enhancement Techniques

Guifan Weng; Wenyan Liu; Lingfeng Guo

doi:10.70393/6a6374616d.333136

Authors

Guifan Weng University of Southern California
Wenyan Liu Carnegie Mellon University
Lingfeng Guo Trine University

DOI:

https://doi.org/10.70393/6a6374616d.333136

ARK:

https://n2t.net/ark:/40704/JCTAM.v2n5a01

Disciplines:

Computer Vision

Subjects:

Image Recognition

References:

45

Keywords:

Corn Leaf Disease, Image Enhancement, Agricultural Automation, Disease Recognition

Abstract

Corn leaf disease recognition represents a critical challenge in modern agricultural systems, where early detection can significantly impact crop yield and food security. This research investigates the application of advanced image enhancement techniques to improve the accuracy of automated corn leaf disease identification. The study implements a comprehensive preprocessing pipeline incorporating histogram equalization, contrast enhancement, and noise reduction algorithms to optimize image quality before feature extraction and classification. Experimental validation using a dataset of 3,000 corn leaf images demonstrates substantial accuracy improvements across multiple disease categories. The proposed enhancement framework achieves 94.7% recognition accuracy, representing a 12.3% improvement over baseline methods without preprocessing. Statistical analysis confirms the significance of image enhancement in reducing misclassification rates, particularly for early-stage disease symptoms. The methodology provides a practical solution for agricultural automation systems, enabling more reliable disease detection in field conditions with varying lighting and environmental factors.

Author Biographies

Guifan Weng, University of Southern California

Computer Science, University of Southern California, CA, USA.

Wenyan Liu, Carnegie Mellon University

Electrical & Computer Engineering, Carnegie Mellon University, PA, USA.

Lingfeng Guo, Trine University

Business Analytics, Trine University, AZ, USA.

References

[1] Gavhale, K. R., & Gawande, U. (2014). An overview of the research on plant leaves disease detection using image processing techniques. Iosr journal of computer engineering (iosr-jce), 16(1), 10-16.

[2] Rao, G., Trinh, T. K., Chen, Y., Shu, M., & Zheng, S. (2024). Jump prediction in systemically important financial institutions' CDS prices. Spectrum of Research, 4(2).

[3] Jin, H., Li, Y., Qi, J., Feng, J., Tian, D., & Mu, W. (2022). GrapeGAN: Unsupervised image enhancement for improved grape leaf disease recognition. Computers and Electronics in Agriculture, 198, 107055.

[4] Rao, G., Lu, T., Yan, L., & Liu, Y. (2024). A Hybrid LSTM-KNN Framework for Detecting Market Microstructure Anomalies:: Evidence from High-Frequency Jump Behaviors in Credit Default Swap Markets. Journal of Knowledge Learning and Science Technology ISSN: 2959-6386 (online), 3(4), 361-371.

[5] Thangadurai, K., & Padmavathi, K. (2014, February). Computer visionimage enhancement for plant leaves disease detection. In 2014 world congress on computing and communication technologies (pp. 173-175). IEEE.

[6] Rao, G., Wang, Z., & Liang, J. (2025). Reinforcement learning for pattern recognition in cross-border financial transaction anomalies: A behavioral economics approach to AML. Applied and Computational Engineering, 142, 116-127.

[7] Khirade, S. D., & Patil, A. B. (2015, February). Plant disease detection using image processing. In 2015 International conference on computing communication control and automation (pp. 768-771). IEEE.

[8] Rao, G., Ju, C., & Feng, Z. (2024). AI-driven identification of critical dependencies in US-China technology supply chains: Implications for economic security policy. Journal of Advanced Computing Systems, 4(12), 43-57.

[9] Rathod, A. N., Tanawal, B., & Shah, V. (2013). Image processing techniques for detection of leaf disease. International Journal of Advanced Research in Computer Science and Software Engineering, 3(11).

[10] Rao, G., Zheng, S., & Guo, L. (2025). Dynamic Reinforcement Learning for Suspicious Fund Flow Detection: A Multi-layer Transaction Network Approach with Adaptive Strategy Optimization.

[11] Dhingra, G., Kumar, V., & Joshi, H. D. (2018). Study of digital image processing techniques for leaf disease detection and classification. Multimedia Tools and Applications, 77(15), 19951-20000.

[12] Ju, C., & Rao, G. (2025). Analyzing foreign investment patterns in the US semiconductor value chain using AI-enabled analytics: A framework for economic security. Pinnacle Academic Press Proceedings Series, 2, 60-74.

[13] Ojo, M. O., & Zahid, A. (2023). Improving deep learning classifiers performance via preprocessing and class imbalance approaches in a plant disease detection pipeline. Agronomy, 13(3), 887.

[14] Liu, W., Rao, G., & Lian, H. (2023). Anomaly Pattern Recognition and Risk Control in High-Frequency Trading Using Reinforcement Learning. Journal of Computing Innovations and Applications, 1(2), 47-58.

[15] Hungilo, G. G., Emmanuel, G., & Emanuel, A. W. (2019, April). Image processing techniques for detecting and classification of plant disease: a review. In Proceedings of the 2019 international conference on intelligent medicine and image processing (pp. 48-52).

[16] Ge, L., & Rao, G. (2025). MultiStream-FinBERT: A Hybrid Deep Learning Framework for Corporate Financial Distress Prediction Integrating Accounting Metrics, Market Signals, and Textual Disclosures. Pinnacle Academic Press Proceedings Series, 3, 107-122.

[17] Wang, Z., Trinh, T. K., Liu, W., & Zhu, C. (2025). Temporal evolution of sentiment in earnings calls and its relationship with financial performance. Applied and Computational Engineering, 141, 195-206.

[18] Li, M., Liu, W., & Chen, C. (2024). Adaptive financial literacy enhancement through cloud-based AI content delivery: Effectiveness and engagement metrics. Annals of Applied Sciences, 5(1).

[19] Jiang, X., Liu, W., & Dong, B. (2024). FedRisk A Federated Learning Framework for Multi-institutional Financial Risk Assessment on Cloud Platforms. Journal of Advanced Computing Systems, 4(11), 56-72.

[20] Fan, J., Lian, H., & Liu, W. (2024). Privacy-preserving AI analytics in cloud computing: A federated learning approach for cross-organizational data collaboration. Spectrum of Research, 4(2).

[21] Liu, W., Qian, K., & Zhou, S. (2024). Algorithmic Bias Identification and Mitigation Strategies in Machine Learning-Based Credit Risk Assessment for Small and Medium Enterprises. Annals of Applied Sciences, 5(1).

[22] Liu, W., & Meng, S. (2024). Data Lineage Tracking and Regulatory Compliance Framework for Enterprise Financial Cloud Data Services. Academia Nexus Journal, 3(3).

[23] Wu, Z., Wang, S., Ni, C., & Wu, J. (2024). Adaptive traffic signal timing optimization using deep reinforcement learning in urban networks. Artificial Intelligence and Machine Learning Review, 5(4), 55-68.

[24] Wu, Z., Feng, E., & Zhang, Z. (2024). Temporal-Contextual Behavioral Analytics for Proactive Cloud Security Threat Detection. Academia Nexus Journal, 3(2).

[25] Xiong, K., Wu, Z., & Jia, X. (2025). Deepcontainer: a deep learning-based framework for real-time anomaly detection in cloud-native container environments. Journal of Advanced Computing Systems, 5(1), 1-17.

[26] Zhang, Z., & Wu, Z. (2023). Context-aware feature selection for user behavior analytics in zero-trust environments. Journal of Advanced Computing Systems, 3(5), 21-33.

[27] Wu, Z., Feng, Z., & Dong, B. (2024). Optimal feature selection for market risk assessment: A dimensional reduction approach in quantitative finance. Journal of Computing Innovations and Applications, 2(1), 20-31.

[28] Lei, Y., & Wu, Z. (2025). A Real-Time Detection Framework for High-Risk Content on Short Video Platforms Based on Heterogeneous Feature Fusion. Pinnacle Academic Press Proceedings Series, 3, 93-106.

[29] Wu, Z., Cheng, C., & Zhang, C. (2025). Cloud-Enabled AI Analytics for Urban Green Space Optimization: Enhancing Microclimate Benefits in High-Density Urban Areas. Pinnacle Academic Press Proceedings Series, 3, 123-133.

[30] Zhu, L., Yang, H., & Yan, Z. (2017, July). Extracting temporal information from online health communities. In Proceedings of the 2nd International Conference on Crowd Science and Engineering (pp. 50-55).

[31] Zhu, L., Yang, H., & Yan, Z. (2017). Mining medical related temporal information from patients' self-description. International Journal of Crowd Science, 1(2), 110-120.

[32] Zhang, Z., & Zhu, L. (2024). Intelligent detection and defense against adversarial content evasion: A multi-dimensional feature fusion approach for security compliance. Spectrum of Research, 4(1).

[33] Cheng, C., Zhu, L., & Wang, X. (2024). Knowledge-Enhanced Attentive Recommendation: A Graph Neural Network Approach for Context-Aware User Preference Modeling. Annals of Applied Sciences, 5(1).

[34] Wang, X., Chu, Z., & Zhu, L. (2024). Research on Data Augmentation Algorithms for Few-shot Image Classification Based on Generative Adversarial Networks. Academia Nexus Journal, 3(3).

[35] Wang, M., & Zhu, L. (2024). Linguistic Analysis of Verb Tense Usage Patterns in Computer Science Paper Abstracts. Academia Nexus Journal, 3(3).

[36] Guan, H., & Zhu, L. (2023). Dynamic Risk Assessment and Intelligent Decision Support System for Cross-border Payments Based on Deep Reinforcement Learning. Journal of Advanced Computing Systems, 3(9), 80-92.

[37] Zhu, L., & Zhang, C. (2023). User Behavior Feature Extraction and Optimization Methods for Mobile Advertisement Recommendation. Artificial Intelligence and Machine Learning Review, 4(3), 16-29.

[38] Kuang, H., Zhu, L., Yin, H., Zhang, Z., Jing, B., & Kuang, J. The Impact of Individual Factors on Careless Responding Across Different Mental Disorder Screenings: A Cross-Sectional Study.

[39] Wu, Z., Zhang, Z., Zhao, Q., & Yan, L. (2025). Privacy-preserving financial transaction pattern recognition: A differential privacy approach.

[40] Li, P., Jiang, Z., & Zheng, Q. (2024). Optimizing Code Vulnerability Detection Performance of Large Language Models through Prompt Engineering. Academia Nexus Journal, 3(3).

[41] Zheng, Q., & Liu, W. (2024). Domain Adaptation Analysis of Large Language Models in Academic Literature Abstract Generation: A Cross-Disciplinary Evaluation Study. Journal of Advanced Computing Systems, 4(8), 57-71.

[42] Zhang, H., & Liu, W. (2024). A Comparative Study on Large Language Models' Accuracy in Cross-lingual Professional Terminology Processing: An Evaluation Across Multiple Domains. Journal of Advanced Computing Systems, 4(10), 55-68.

[43] Wang, X., Chu, Z., & Weng, G. (2025). Improved No-Reference Image Quality Assessment Algorithm Based on Visual Perception Characteristics. Annals of Applied Sciences, 6(1).

[44] Wang, Y., & Zhang, C. (2023). Research on Customer Purchase Intention Prediction Methods for E-commerce Platforms Based on User Behavior Data. Journal of Advanced Computing Systems, 3(10), 23-38.

[45] Xie, H., & Qian, K. (2025). Research on Low-Light Image Enhancement Algorithm Based on Attention Mechanism. Journal of Advanced Computing Systems, 5(5), 1-14.