ENHANCING MAIZE LEAF DISEASE CLASSIFICATION PERFORMANCE USING IMAGE PROCESSING TECHNIQUES
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Abstract
The outbreak of maize (corn) diseases poses a major challenge, significantly reducing yield and affecting market prices. Timely and accurate diagnosis is essential for effective management, including prevention and treatment. However, self-diagnosis is often unreliable due to fatigue and symptom similarity. Artificial Intelligence (AI) offers a promising solution to reduce farmer burden and improve accuracy. Nevertheless, traditional algorithms may struggle with effective classification. This research proposes an enhanced maize disease classification approach using leaf images, employing HSV color space processing (to better distinguish disease-related color shades) and Data Augmentation (to increase data diversity and balance) prior to Deep Learning model training. Experimental results demonstrate an improved average classification accuracy of 93.17%, compared to 91.69% with conventional methods.
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This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
The published articles are copyright of the Engineering Journal of Research and Development, The Engineering Institute of Thailand Under H.M. The King's Patronage (EIT).
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