Supervised Machine Learning for Plants Identification Based on Images of Their Leaves

Abstract

Botanists study in general the characteristics of leaves to give to each plant a scientific name; such as shape, margin...etc. This paper proposes a comparison of supervised plant identification using different approaches. The identification is done according to three different features extracted from images of leaves: a fine-scale margin feature histogram, a Centroid Contour Distance Curve shape signature and an interior texture feature histogram. First represent each leaf by one feature at a time in, then represent leaves by two features, and each leaf was represented by the three features. After that, the authors classified the obtained vectors using different supervised machine learning techniques; the used techniques are Decision tree, Naïve Bayes, K-nearest neighbour, and neural network. Finally, they evaluated the classification using cross validation. The main goal of this work is studying the influence of representation of leaves' images on the identification of plants, and also studying the use of supervised machine learning algorithm for plant leaves classification.

Maize seed appearance quality assessment based on improved Inception-ResNet

“…Machine learning (Rahmani et al, 2016;Prajapati et al, 2018;Liang et al, 2019;Sharaff et al, 2021) is an active field of artificial intelligence research that has advantages in terms of training small data samples and wide applications in agricultural product identification and defect detection. Farooqui et al (2019) used a gray-level co-occurrence matrix for disease feature extraction, a support vector machine classifier for plant disease identification, combined with advanced neural network to optimize the data to improve the detection accuracy and demonstrated the feasibility of this approach for plant disease diagnosis through experiments.…”

“…Machine learning ( Rahmani et al., 2016 ; Prajapati et al., 2018 ; Liang et al., 2019 ; Sharaff et al, 2021 ) is an active field of artificial intelligence research that has advantages in terms of training small data samples and wide applications in agricultural product identification and defect detection. Farooqui et al.…”