Zeae-maydis, also known as maize gray leaf spot, and porcinia sorghi, known as maize common rust, are the two most prevalent and dangerous diseases that harm maize crops in Nigeria. Plant diseases are difficult for Nigerian farmers to recognize correctly, and it is impossible to assess their severity with the unaided eye. However, hiring a pathologist is more costly and time-consuming for large farms. Moreover, many support vector machine (SVM) classification models for maize leaf disease classification have been developed by different researchers. However, these existing models are impacted by imbalanced datasets, irrelevant feature selection, and difficulty in fine-tuning the hyperparameters of the SVM. Consequently, to resolve these problems, two optimized multiclass support vector machine classification models (BPSO-SVM and RSA-SVM) were trained to categorize maize leaves disease into Zeae-maydis and porcinia sorghi using 1,648 photos of maize leaves across all maize datasets, which included 574 photos of gray leaf spot disease, 574 photos of common rust disease, and 500 photos of healthy leaves obtained from the Kaggle village datasets. The images were scaled down, converted t
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