Corn Crop Disease Detection Using Convolutional Neural Network (CNN) to Support Smart Agricultural Farming
Corn Crop Disease Detection Using Convolutional Neural Network (CNN) to Support Smart Agricultural Farming |
||
 |
 |
|
| © 2024 by IJETT Journal | ||
| Volume-72 Issue-6 |
||
| Year of Publication : 2024 | ||
| Author : Jovelin M. Lapates |
||
| DOI : 10.14445/22315381/IJETT-V72I6P120 | ||
How to Cite?
Jovelin M. Lapates , "Corn Crop Disease Detection Using Convolutional Neural Network (CNN) to Support Smart Agricultural Farming," International Journal of Engineering Trends and Technology, vol. 72, no. 6, pp. 195-203, 2024. Crossref, https://doi.org/10.14445/22315381/IJETT-V72I6P120
Abstract
Corn crop disease detection is crucial in ensuring crop health and optimizing agricultural productivity. This study explores the implementation of the YOLOv8 algorithm for efficient and accurate disease detection in corn crops. The research focuses on detecting diseases such as blight, common rust, and gray leaf spot, significantly impacting crop yield and quality. The YOLOv8 model trains using a carefully annotated dataset of corn leaf images, encompassing both diseaseinfected samples and healthy leaves. The model performance is evaluated using a separate test set, and promising outcomes are observed, with high mean Average Precision (mAP) values achieved across different disease categories. Notably, the model demonstrates exceptional accuracy in recognizing healthy corn plants, with an mAP of 0.99 for the healthy class. These results indicate the potential of YOLOv8 as an effective tool for early disease detection and precise interventions in smart agricultural farming. The findings of this study contribute to the advancement of automated disease detection systems in agriculture, paving the way for improved crop management practices and optimized yields in corn farming.
Keywords
Corn, Crop disease, YOLOv8, Smart agricultural farming, CNN.
References
[1] David G. Lowe, “Distinctive Image Features from Scale-Invariant Keypoints,” International Journal of Computer Vision, vol. 60, pp. 91-110, 2004.
[CrossRef] [Google Scholar] [Publisher Link]
[2] Aanis Ahmad, Dharmendra Saraswat, and Aly El Gamal, “A Survey on Using Deep Learning Techniques for Plant Disease Diagnosis and Recommendations for Development of Appropriate Tools,” Smart Agricultural Technology, vol. 3, pp. 1-13, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[3] Sunita I. Naik, Vivekanandreddy, and S.S. Sannakki, “Plant Disease Diagnosis System for Improved Crop Yield,” International Journal of Innovations in Engineering and Technology, vol. 4, no. 1, pp. 198-204, 2014.
[Google Scholar] [Publisher Link]
[4] Yasamin Borhani, Javad Khoramdel, and Esmaeil Najafi, “A Deep Learning Based Approach for Automated Plant Disease Classification Using Vision Transformer,” Scientific Reports, vol. 12, no. 1, pp. 1-10, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[5] M. Shobana et al., "Plant Disease Detection Using Convolution Neural Network," 2022 International Conference on Computer Communication and Informatics, Coimbatore, India, pp. 1-5, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[6] Ajay Talele, Aseem Patil, and Bhushan Barse, “Detection of Real Time Objects Using TensorFlow and OpenCV,” Asian Journal for Convergence in Technology, vol. 5, no. 1, pp. 1-4, 2019.
[Google Scholar] [Publisher Link]
[7] Florin-Alexandru Vasluianu et al., “NTIRE 2023 Image Shadow Removal Challenge Report,” Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 1788-1807, 2023.
[Google Scholar] [Publisher Link]
[8] Florin-Alexandru Vasluianu et al., “NTIRE 2023 Image Shadow Removal Challenge Report,” 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, Vancouver, BC, Canada, pp. 1788-1807, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[9] Dionis A. Padilla, Ramon Alfredo I. Pajes, and Jerome T. De Guzman, “Detection of Corn Leaf Diseases Using Convolutional Neural Network With OpenMP Implementation,” 2020 IEEE 12th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management, Manila, Philippines, pp. 1-6, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[10] Laith Alzubaidi et al., “A Survey on Deep Learning Tools Dealing with Data Scarcity: Definitions, Challenges, Solutions, Tips, and Applications,” Journal of Big Data, vol. 10, no. 1, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[11] Davinder Singh et al., “PlantDoc: A Dataset for Visual Plant Disease Detection,” Proceedings of the 7th ACM IKDD CoDS and 25th COMAD, Hyderabad India, pp. 249-253, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[12] Pei-Wern Chin, Kok-Why Ng, and Naveen Palanichamy, “Plant Disease Detection and Classification Using Deep Learning Methods: A Comparison Study,” Journal of Informatics and Web Engineering, vol. 3, no. 1, pp. 155-168, 2024.
[CrossRef] [Google Scholar] [Publisher Link]
[13] Andreas Kamilaris, and Francesc X. Prenafeta-Boldú, “Deep Learning In Agriculture: A Survey,” Computers and Electronics in Agriculture, vol. 147, pp. 70-90, 2018.
[CrossRef] [Google Scholar] [Publisher Link]
[14] Sharada P. Mohanty, David P. Hughes, and Marcel Salathé, “Using Deep Learning for Image-Based Plant Disease Detection,” Frontiers in Plant Science, vol. 7, pp. 1-11, 2016.
[CrossRef] [Google Scholar] [Publisher Link]
[15] S.A. Sanchez, H.J. Romero, and A.D. Morales, “A Review: Comparison of Performance Metrics of Pretrained Models for Object Detection Using the Tensorflow Framework,” IOP Conference Series: Materials Science and Engineering, vol. 844, no. 1, pp. 1-15, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[16] Khalid Hamza, Saba un Nisa, and Gulshan Irshad, “A Review on Potato Disease Detection and Classification by Exploiting Deep Learning Techniques,” Journal of Agriculture and Veterinary Science, vol. 1, no. 2, pp. 81-88, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[17] Deepak Kumar, Sonam Gupta, and Pradeep Gupta, “Plant Disease Recognition Using Machine Learning and Deep Learning Classifiers,” 13th International Conference Advanced Computing, Kolhapur, India, pp. 3-14, 2024.
[CrossRef] [Google Scholar] [Publisher Link]
[18] Jiawei Li et al., “An Improved YOLOv5-Based Vegetable Disease Detection Method,” Computers and Electronics in Agriculture, vol. 202, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[19] Mohit Agarwal et al., “A Convolution Neural Network Based Approach to Detect the Disease in Corn Crop,” 2019 IEEE 9th International Conference on Advanced Computing, Tiruchirappalli, India, pp. 176-181, 2019.
[CrossRef] [Google Scholar] [Publisher Link]
[20] Wen Tao et al., “Review of the Internet of Things Communication Technologies in Smart Agriculture and Challenges,” Computers and Electronics in Agriculture, vol. 189, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[21] David Vallejo-Gómez, Marisol Osorio, and Carlos A. Hincapié, “Smart Irrigation Systems in Agriculture: A Systematic Review,” Agronomy, vol. 13, no. 2, pp. 1-25, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[22] Md. Ashraful Haque et al., “Deep Learning-Based Approach for Identification of Diseases of Maize Crop,” Scientific Reports, vol. 12, no. 1, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[23] Gopi Krishna Akella et al., “A Systematic Review of Blockchain Technology Adoption Barriers and Enablers for Smart and Sustainable Agriculture,” Big Data and Cognitive Computing, vol. 7, no. 2, pp. 1-22, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[24] Sjaak Wolfert et al., “Big Data in Smart Farming – A Review,” Agricultural Systems, vol. 153, pp. 69-80, 2017.
[CrossRef] [Google Scholar] [Publisher Link]
[25] Jaqueline Iaksch, Ederson Fernandes, and Milton Borsato, “Digitalization and Big Data in Smart Farming–A Review,” Journal of Management Analytics, vol. 8, no. 2, pp. 333-349, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[26] Achim Walter et al., “Smart Farming is Key to Developing Sustainable Agriculture,” Proceedings of the National Academy of Sciences, vol. 114, no. 24, pp. 6148-6150, 2017.
[CrossRef] [Google Scholar] [Publisher Link]
[27] Yemeserach Mekonnen et al., “IoT Sensor Network Approach for Smart Farming: An Application in Food, Energy and Water System,” 2018 IEEE Global Humanitarian Technology Conference, San Jose, CA, USA, pp. 1-5, 2018.
[CrossRef] [Google Scholar] [Publisher Link]
[28] Achyut Morbekar, Ashi Parihar, and Rashmi Jadhav, “Crop Disease Detection Using YOLO,” 2020 International Conference for Emerging Technology, Belgaum, India, pp. 1-5, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[29] Corn Disease, Roboflow. [Online]. Available: https://app.roboflow.com/buksu/corn-disease-odni2/visualize/2