Advancing Maize Leaf Disease Detection: A Machine Learning Approach

Volume: 11 | Issue: 01 | Year 2025 | Subscription
International Journal of Image Processing and Pattern Recognition
Received Date: 10/09/2024
Acceptance Date: 02/14/2025
Published On: 2025-03-28
First Page: 17
Last Page: 25

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By: Nyamatulla Patel, Mohammed Ziaullah, and Aarif Makandar

Abstract

Every day, countless grains and fruits are cultivated around the world. To keep up with the growing population’s needs, farmers are constantly adopting new and advanced techniques. Furthermore, the development and application of new technologies enable the quick and simple detection of plant diseases. Identifying plant diseases at an early stage is crucial to minimizing their effects on crop productivity and quality. Traditional diagnostic methods often rely on expert knowledge and can be slow. However, with AI, this process can be automated, allowing farmers to quickly identify problems and take prompt action to address them. Using computer vision algorithms, we examined high-resolution images of peaches and corn leaves to spot signs of diseases like peach scab, bacterial spot, and fungal infections in corn, such as gray leaf spot and northern corn leaf blight. In this study, we used artificial intelligence and computer science to identify the ailments and offer treatments using pictures of a fruit called a peach and a diseased leaf of a grain called corn.

Keywords: Maize leaf, SVM, KNN, ML, plant disease detection

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Citation:

How to cite this article: Nyamatulla Patel, Mohammed Ziaullah, and Aarif Makandar, Advancing Maize Leaf Disease Detection: A Machine Learning Approach. International Journal of Image Processing and Pattern Recognition. 2025; 11(01): 17-25p.

How to cite this URL: Nyamatulla Patel, Mohammed Ziaullah, and Aarif Makandar, Advancing Maize Leaf Disease Detection: A Machine Learning Approach. International Journal of Image Processing and Pattern Recognition. 2025; 11(01): 17-25p. Available from:https://journalspub.com/publication/ijippr/article=18151

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