Abstract
This study proposes the machine learning based classification of medical plant leaves. The total six varieties of medicinal plant leaves-based dataset are collected from the Department of Agriculture, The Islamia University of Bahawalpur, Pakistan. These plants are commonly named in English as (herbal) Tulsi, Peppermint, Bael, Lemon balm, Catnip, and Stevia and scientifically named in Latin as Ocimum sanctum, Mentha balsamea, Aegle marmelos, Melissa officinalis, Nepeta cataria, and Stevia rebaudiana, respectively. The multispectral and digital image dataset are collected via a computer vision laboratory setup. For the preprocessing step, we crop the region of the leaf and transform it into a gray level format. Secondly, we perform a seed intensity-based edge/line detection utilizing Sobel filter and draw five regions of observations. A total of 65 fused features dataset is extracted, being a combination of texture, run-length matrix, and multi-spectral features. For the feature optimization process, we employ a chi-square feature selection approach and select 14 optimized features. Finally, five machine learning classifiers named as a multi-layer perceptron, logit-boost, bagging, random forest, and simple logistic are deployed on an optimized medicinal plant leaves dataset, and it is observed that the multi-layer perceptron classifier shows a relatively promising accuracy of 99.01% as compared to the competition. The distinct classification accuracy by the multi-layer perceptron classifier on six medicinal plant leaves are 99.10% for Tulsi, 99.80% for Peppermint, 98.40% for Bael, 99.90% for Lemon balm, 98.40% for Catnip, and 99.20% for Stevia.
Highlights
Living things on earth depend on the oxygen produced by plants
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Summary
Living things on earth depend on the oxygen produced by plants. There are many different types of plants, all of them playing an important role in maintaining the earth’s biodiversity by providing air and water to living humans [1]. Many researchers work on plant leaf disease classification, segmentation, and quality assessment, for instance, Reference [13] proposed a medicinal plant classification framework using the shape and color feature of the leaf They deployed a Support Vector Machine (SVM) classifier on the optimized features dataset and obtained 96.66% accuracy. Reference [15] proposed a system for the classification of sugarcane leaf disease based on fungi They used the triangle threshold approach for the segmentation of sugarcane leaves and obtained 98.60% accuracy. Reference [19] proposed the Local Binary Patterns (LBP) approach for plant classification using leaves images They dealt with texture feature extraction, salt and pepper noise removal using ML techniques.
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