Plant Species Recognition Using Morphological Features and Adaptive Boosting Methodology

Abstract

Plant species detection aims at the automatic identification of plants. Although a lot of aspects like leaf, flowers, fruits, seeds could contribute to the decision, but leaf features are the most significant. As a plant leaf is always more accessible as compared to other parts of the plants, it is obvious to study it for plant identification. The present paper introduced a novel plant species classifier based on the extraction of morphological features using a Multilayer Perceptron with Adaboosting. The proposed framework comprises pre-processing, feature extraction, feature selection, and classification. Initially, some pre-processing techniques are used to set up a leaf image for the feature extraction process. Various morphological features, i.e., centroid, major axis length, minor axis length, solidity, perimeter, and orientation are extracted from the digital images of various categories of leaves. Different classifiers, i.e., k-NN, Decision Tree and Multilayer perceptron are employed to test the accuracy of the algorithm. AdaBoost methodology is explored for improving the precision rate of the proposed system. Experimental results are obtained on a public dataset (FLAVIA) downloaded from http://flavia.sourceforge.net/. A precision rate of 95.42% has been achieved using the proposed machine learning classifier, which outperformed the state-ofthe-art algorithms.

Morphometric Parameters of Plants of Crambe Spp. During Vegetation

“…The leaf is one of the most significant features for the identification of concrete species in plant morphology among other plant organs. Wherein have used different classifications of leaf features (Kumar et al, 2019). The morphometric parameters of leaves can be useful in ecophysiological study to estimate the tolerance of a plant to the environment, for example, in the case of C. maritima as a salt-tolerant crop (Vos et al, 2010).…”

Estimation of Cold Stress, Plant Age, and Number of Leaves in Watermelon Plants Using Image Analysis

“…In studies involving morphological feature analysis of plants, some key features measured include plant leaf length, width, angle, diameter, perimeter area, and volume (Harish et al, 2013). Leaf morphological features are useful for plant recognition, identification, classification, and disease identification and classification (Aptoula and Yanikoglu, 2013;Ramcharan et al, 2017Ramcharan et al, , 2019Kumar et al, 2019;Tan et al, 2020).…”

“…In plant studies, imaging techniques and analysis have the advantage of being non-destructive and able to extract intricate information that can be used to analyze biological patterns of plant growth (Nabwire et al, 2021). The application of image analysis in morphological studies has been done to automate plant recognition tasks (Aptoula and Yanikoglu, 2013;Kumar et al, 2019), classification of plant leaves using leaf shape feature extraction techniques (Manik et al, 2016), automation of plant classification systems (Harish et al, 2013), and development of leaf disease detection and diagnosis systems (Jagtap and Hambarde, 2014). Specifically, image analysis has been applied in cold stress response classification in maize plants (Enders et al, 2019), drought and heat stress tolerance screening in wheat (Schmidt et al, 2020), weed growth stage estimation (Teimouri et al, 2018), and leaf counting in Arabidopsis using deep learning (Aich and Stavness, 2017).…”

Adaptive boosting of random forest algorithm for automatic petrophysical interpretation of well logs

“…It is commonly used in classification and regression problems. It has been found to improve the performance of Random Forest algorithm and has also been used with other Machine Learning algorithms to improve their performance and has been demonstrated in banking (Sanjaya et al 2020 ), structural engineering (Feng et al 2020 ), cybersecurity (Sornsuwit and Jaiyen 2019 ), plant species identification (Kumar et al 2019 ) and many other avenues. In this study the application of Random Forest algorithm is demonstrated to predict reservoir properties of 4 wells using 1 well as a training dataset, and then using Adaptive Boosting to increase the performance of the Random Forest algorithm.…”