Image-Based Corn Leaf Disease Detection Framework using Yolov8 Model

Authors

  • Nerissa L. Javier, Thelma D. Palaoag, Carl Angelo S. Pamplona

Keywords:

Corn Leaf Diseases, Disease Detection, YOLOv8, Performance Metrics, Precision Agriculture.

Abstract

Crop diseases pose significant challenges to agricultural production, leading to substantial crop losses. With the increasing demand for food production to meet the needs of a growing population, ensuring the health and productivity of crops like corn is of paramount importance.  The proposed framework integrates cutting-edge technologies including computer vision, machine learning, and mobile application development to create a user-friendly and efficient tool to accurately identify and classify diseases affecting corn crops.    The framework aims to automate the disease detection process through the analysis of images of corn leaves affected by diseases like northern leaf blight (NLB), gray leaf spot (GLS), and northern leaf spot (NLS) obtained from Kaggle datasets. By utilizing Yolov8 for feature extraction and classification, the system achieves high accuracy in disease detection. The framework is designed to be scalable, adaptable, and efficient, making it suitable for real-time applications in agriculture. Experimental results demonstrate the effectiveness of the proposed approach in accurately diagnosing corn diseases, thereby aiding farmers in timely intervention and crop management. By integrating these advanced technologies into a comprehensive framework, the corn disease detection mobile application aims to provide farmers with a reliable tool for early diagnosis, effective intervention, and improved crop management practices, ultimately enhancing crop yield and ensuring food security.

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References

S. Rahayu, Waridin, Purbayu, and I. Mafruhah, “Stakeholder Role in Improving Agribusiness Efficiency and Food Security in Developing Countries,” International Journal of Economics and Business Administration, 2019, doi: 10.35808/ijeba/358.

K. Kostrakiewicz-Gierałt, “A Summary of the Use of Maize in Nutritional Products for Sportsmen,” Central European Journal of Sport Sciences and Medicine, 2020, doi: 10.18276/cej.2020.3-03.

L. O. Lopez-Zuniga et al., “Using Maize Chromosome Segment Substitution Line Populations for the Identification of Loci Associated With Multiple Disease Resistance,” G3 Genes|genome|genetics, 2019, doi: 10.1534/g3.118.200866.

C. Xiong et al., “Physiological and Molecular Characteristics of Southern Leaf Blight Resistance in Sweet Corn Inbred Lines,” International Journal of Molecular Sciences, 2022, doi: 10.3390/ijms231810236.

. Vanlalhruaia, S. Mahapatra, S. Chakraborty, and S. Das, “Prevalence of Southern Leaf Blight of Maize in Two Major Maize Producing States of India,” Journal of Cereal Research, 2022, doi: 10.25174/2582-2675/2022/123845.

X. Qian, C. Zhang, L. Chen, and K. Li, “Deep Learning-Based Identification of Maize Leaf Diseases Is Improved by an Attention Mechanism: Self-Attention,” Frontiers in Plant Science, 2022, doi: 10.3389/fpls.2022.864486.

D. A. Noola and D. R. Basavaraju, “Corn Leaf Image Classification Based on Machine Learning Techniques for Accurate Leaf Disease Detection,” International Journal of Electrical and Computer Engineering (Ijece), 2022, doi: 10.11591/ijece.v12i3.pp2509-2516.

A. Hidayat, U. Darusalam, and I. Irmawati, “Detection of Disease on Corn Plants Using Convolutional Neural Network Methods,” Jurnal Ilmu Komputer Dan Informasi, 2019, doi: 10.21609/jiki.v12i1.695.

H. Phan, A. Ahmad, and D. Saraswat, “Identification of Foliar Disease Regions on Corn Leaves Using SLIC Segmentation and Deep Learning Under Uniform Background and Field Conditions,” Ieee Access, 2022, doi: 10.1109/access.2022.3215497.

S. V. Meena, V. S. Dhaka, D. Sinwar, Kavita, M. F. Ijaz, and M. Woźniak, “A Survey of Deep Convolutional Neural Networks Applied for Prediction of Plant Leaf Diseases,” Sensors, 2021, doi: 10.3390/s21144749.

S. M. Hassan, A. K. Maji, M. Jasinski, Z. Leonowicz, and E. Jasińska, “Identification of Plant-Leaf Diseases Using CNN and Transfer-Learning Approach,” Electronics, 2021, doi: 10.3390/electronics10121388.

A. Waheed, M. Goyal, D. Gupta, A. Khanna, A. E. Hassanien, and H. M. Pandey, “An Optimized Dense Convolutional Neural Network Model for Disease Recognition and Classification in Corn Leaf,” Computers and Electronics in Agriculture, 2020, doi: 10.1016/j.compag.2020.105456.

M. S. Anari, “A Hybrid Model for Leaf Diseases Classification Based on the Modified Deep Transfer Learning and Ensemble Approach for Agricultural AIoT-Based Monitoring,” Computational Intelligence and Neuroscience, 2022, doi: 10.1155/2022/6504616.

L. Zhou, C. Zhang, F. Liu, Z. Qiu, and Y. He, “Application of Deep Learning in Food: A Review,” Comprehensive Reviews in Food Science and Food Safety, 2019, doi: 10.1111/1541-4337.12492.

M. Fraiwan, E. Faouri, and N. Khasawneh, “Classification of Corn Diseases From Leaf Images Using Deep Transfer Learning,” Plants, 2022, doi: 10.3390/plants11202668.

F. D. Adhinata, G. F. Fitriana, A. Wijayanto, and M. P. K. Putra, “Corn Disease Classification Using Transfer Learning and Convolutional Neural Network,” Juita Jurnal Informatika, 2021, doi: 10.30595/juita.v9i2.11686.

A. Semma, S. Lazrak, Y. Hannad, M. Boukhani, and Y. El Kettani, “WRITER IDENTIFICATION: THE EFFECT OF IMAGE RESIZING ON CNN PERFORMANCE,” The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol. XLVI-4/W5-2021, pp. 501–507, 2021, doi: 10.5194/isprs-archives-XLVI-4-W5-2021-501-2021.

B. Wang et al., “Smartphone-Based Platforms Implementing Microfluidic Detection With Image-Based Artificial Intelligence,” Nature Communications, 2023, doi: 10.1038/s41467-023-36017-x.

L. Ye and H. Yang, “From Digital Divide to Social Inclusion: A Tale of Mobile Platform Empowerment in Rural Areas,” Sustainability, 2020, doi: 10.3390/su12062424.

A. N. L. Hermans et al., “Mobile Health Solutions for Atrial Fibrillation Detection and Management: A Systematic Review,” Clinical Research in Cardiology, 2021, doi: 10.1007/s00392-021-01941-9.

S. P. Mohanty, D. P. Hughes, and M. Salathé, “Using Deep Learning for Image-Based Plant Disease Detection,” Frontiers in Plant Science, vol. 7, 2016, doi: 10.3389/fpls.2016.01419.

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Published

26.03.2024

How to Cite

Nerissa L. Javier. (2024). Image-Based Corn Leaf Disease Detection Framework using Yolov8 Model. International Journal of Intelligent Systems and Applications in Engineering, 12(21s), 3368 –. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/6032

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Section

Research Article