Janhavi Borkar,,
Shrishti Kamanboina,
Satyam Bhalerao,,
Mugdha A. Kango,
- Student, Department of Electronic and Telecommunication, PES Modern College of Engineering, Pune Maharashtra, India
- Student, Department of Electronic and Telecommunication, PES Modern College of Engineering, Pune Maharashtra, India
- Student, Department of Electronic and Telecommunication, PES Modern College of Engineering Pune Maharashtra, India
- Student, Department of Electronic and Telecommunication, PES Modern College of Engineering Pune Maharashtra, India
Abstract
This research presents an overview of different image processing techniques for the identification of leaf disease. Many algorithms can be used to identify and categorize leaf diseases in plants, and digital image processing provides a quick, dependable, and accurate method of disease detection. This paper presents various techniques used on multiple crops and the achieved accuracy for each model. Leaf disease detection is a critical task in agriculture to ensure the health and productivity of crops. Traditional methods of disease identification are often labor-intensive and subjective, highlighting the need for automated solutions. In recent years, deep learning techniques have emerged as powerful tools for automatic disease detection, offering the potential to revolutionize agricultural practices. Our work primarily focuses on analyzing several methods for detecting leaf disease and presents a summary of various image-processing methods. When given the resources and information required for early disease identification, farmers are more equipped to make informed decisions and act swiftly to prevent it. As a result, their total crop yield and income can rise. From the review we conclude that convolutional neural network models show the most accurate results with highest accuracy scores and are best suited for image processing because they employ the technique known as parameter sharing that makes them more structured while working with image data.
Keywords: Image processing, leaf disease, agriculture, pesticides, crop
[This article belongs to Current Trends in Signal Processing(ctsp)]
Browse Figures
References
- Andrew J, Eunice J, Popescu DE, Chowdary MK, Hemanth Deep learning-based leaf disease detection in crops using images for agricultural applications. Agronomy. 2022; 12: 2395.
- Kibriya H, Rafique R, Ahmad W, Adnan Tomato leaf disease detection using convolution neural network. In: Proceedings of 2021 18th International Bhurban Conference on Applied Sciences and Technologies, Islamabad, Pakistan, January 12–16, 2021. pp. 346–351.
- Phadikar S, Sil Rice disease identification using pattern recognition techniques. In: Proceedings of 11th International Conference on Computer and Information Technology (ICCIT 2008), Khulna, Bangladesh, December 25–27, 2008. pp. 420–423.
- Rothe PR, Kshirsagar RV. Cotton leaf disease identification using pattern recognition techniques. In: International Conference on Pervasive Computing (ICPC), Pube, India, January 8–10, 2015. pp. 1–6.
- Jaisakthi SM, Mirunalini P, Thenmozhi D, Vatsala. Grape leaf disease identification using machine learning techniques. In: Second International Conference on Computational Intelligence in Data Science (ICCIDS-2019), Chennai, India, February 21–23, 2019. pp. 1–6.
- Lv M, Zhou G, He M, Chen A, Zhang W, Hu Maize leaf disease identification based on feature enhancement and DMS-robust Alexnet. IEEE Access. 2020; 8: 57952–57966.
- Zhong Y, Zhao Research on deep learning in apple leaf disease recognition. Computers Electron Agric. 2020; 168: 105146.
- Raut S, Ingole Review on leaf disease detection using image processing techniques. Int Res J Eng Technol. 2017; 4 (4): 2044–2047.
- Gavhale KR, Gawande An overview of the research on plant leaves disease detection using image processing techniques. IOSR J Computer Eng. 2014; 16 (1): 10–16.
- Prodeep AR, Hoque AM, Kabir MM, Rahman MS, Mridha M Plant disease identification from leaf images using deep CNN’s EfficientNet. In: Proceedings of the 2022 International Conference on Decision Aid Sciences and Applications (DASA), Chiangrai, Thailand, March 23–25, 2022. pp. 523–527.
Current Trends in Signal Processing
| Volume | 14 |
| Issue | 01 |
| Received | April 23, 2024 |
| Accepted | May 23, 2024 |
| Published | August 16, 2024 |