MintLeafNet: A CNN Model for Accurate and Efficient Detection of Multiple Mint Leaf Diseases

Abstract

This study uses a CNN model, or convolutional neural network, to propose a deep learning strategy for the accurate forecasting of six frequent mint leaf diseases. The proposed model identifies and categorizes different illnesses based on the visual characteristics found in the input photos.The author gathered a dataset of photos of mint leaves from six categories, to assess the effectiveness of the suggested model. The dataset was divided into testing, training, and validation sets after being preprocessed to ensure consistency in image size and color. On the validation and training collections, the suggested framework was trained and validated, and the testing set was used for the evaluation. All six classes in the evaluation scored highly on accuracy and precision, via a total precision of 90.91% and an adjusted F1-score of 90.90%. Additionally reported were the accuracy, recall, as well as F1-score for each class, all of which demonstrated strong performance. The outcomes show that the suggested CNN model is capable of correctly recognizing and categorizing the six prevalent mint leaf illnesses. This model can be a useful tool for disease detection and avoidance in mint crops, allowing farmers to take appropriate action before the illnesses can cause serious harm. As a result, this study shows the promise of deep learning methods for the precise and effective diagnosis of plant diseases. The suggested CNN system can be further developed to recognize and categorize other plant diseases, advancing precision farming and environmentally friendly crop management techniques.

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