Automatic Classification of Medicinal Plant Species Based on Color and Texture Features

Pacífico, Luciano D. S.; Britto, Larissa; Oliveira, Emília G.; Ludermir, Teresa B.

doi:10.1109/bracis.2019.00133

Cited by 13 publications

(3 citation statements)

References 19 publications

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“…However, multi-spectral imaging is costly, and it is not easy to carry the imaging device to the site of plants; it is not suitable for practical applications. Luciano et al also utilized color and texture features of leaves to classify plants using MLP network [10]. They collected 1148 leaf images of 15 plants from Brazil and achieved 97% accuracy.…”

Section: Related Workmentioning

confidence: 99%

Medicinal Plant Classification Using Particle Swarm Optimized Cascaded Network

Islam,

Rahman,

Hossain

et al. 2024

IEEE Access

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Medicinal plants are essential to healthcare since ancient times and are integral to developing drugs and other medical treatments. More than 25% of medicines in developed countries are produced from medicinal plants, while in developing countries, approximately 80% of individuals receive primary healthcare from these plants. Traditionally, these plants are identified manually by experts, which is tedious, time-consuming, subjective and dependent on the availability of experts. Furthermore, a wrong detection can result in serious health issues or death. This signifies the need for a more reliable approach to identifying medicinal plants, which is accurate and practical. Several automated methods were proposed previously, utilizing deep learning and traditional machine learning (TML) techniques, but they require singular leaf images and failed to achieve sufficient accuracy when demonstrated in a different setting. Capturing singular leaf images for each plant is also time-consuming and laborious. This paper presents a robust, accurate and practical system to identify medicinal plants from smartphone-captured plant images in the site of plants.The proposed system utilized a cascaded architecture to extract features using a pre-trained ResNet50 model, which were optimized using Particle Swarm Optimization (PSO) to classify the plants using a Support Vector Machine (SVM). The proposed ResNet50-PSO-SVM network classified seven medicinal plants with 99.60% accuracy, outperforming the state-of-the-art (99%). The system was demonstrated for three different smartphones, classifying an image in 0.15 seconds with 97.79% accuracy on average. The system's high accuracy, rapid identification time and robustness ensured its practical use.

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Section: Related Workmentioning

confidence: 99%

Medicinal Plant Classification Using Particle Swarm Optimized Cascaded Network

Islam,

Rahman,

Hossain

et al. 2024

IEEE Access

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“…In the process of the discussed paper, initially, vein segmentation is performed over original color images of legume leaves and then the central patch was extracted and finally, CNN with five layers was utilized to assort the leaves into three classes. In another research, 88 automatic recognition of 15 medicinal plant species relying on texture and color features using five ML algorithms was demonstrated. Also, a new dataset was introduced and the classification accuracy for all five algorithms shows that RF and MLP with backpropagation achieve higher accuracy.…”

Section: Related Workmentioning

confidence: 99%

Contemporary machine learning applications in agriculture: Quo Vadis?

Mahmood

Tiwari

Singh

et al. 2022

Concurrency and Computation

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Agricultural automation is an emerging subject today to accomplish the food demands of individuals across the globe. Machine learning is one such agricultural automation tool that has been adopted briskly in the recent decade due to its ability to process countless input data and handle non-linear tasks. Availability and continuous development of agricultural data led the machine learning pervasive in multiple aspects of agriculture. This paper systematically analyses and summarizes the 81 quality research efforts published in the past decade dedicated to the various contemporary machine learning applications in agriculture and food production systems. We examined and categorized each agricultural problem under study into four categories and each category into its subcategories. The finding demonstrates the contemporary applications of machine learning in broad agricultural subcategories and determines where it is heading shortly; based upon contributions of researchers, utilization of machine learning models/algorithms, and the availability of agricultural datasets.Through the analysis, it is discovered that the current innovation can help the improvement of agricultural automation to accomplish the advantages of minimal cost, high efficiency, and better precision. This paper can serve as an investigatory guide for researchers, academicians, engineers, and manufacturers to understand and apply modern and upgraded cognitive technologies to each subcategory of the agricultural sector.

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“…Kuang et al [16] proposed a method to construct a defect detection method of the bamboo strip using an SVM classifier by integrating LBP and GIST features. Pacifico et al [17] proposed an automatic plant classification system based on color and texture features using a multi-layer perception with backpropagation (ML-BP) classifier. Sujith and Neethu [18] proposed a feature combination method to classify plants using ANN classifier by combining shape and texture features.…”

Section: Preliminary Studymentioning

confidence: 99%

Plant Leaf Classification and Comparative Analysis of Combined Feature Set Using Machine Learning Techniques

Ariyapadath¹

2021

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The main purpose of this research work is to apply machine learning and image processing techniques for plant classification efficiently. In the plant classification system, the conventional method is time-consuming and needs to apply expensive analytical instruments. The automated plant classification system helps to predict plant classes easily. The most challenging part of the automated plant classification research is to extract unique features of leaves. This paper proposes a plant classification model using an optimal feature set with combined features. The proposed model is used to extract features from leaf images and applied to image classification algorithms. After the evaluation process, it is found that GIST, Local Binary Pattern and Pyramid Histogram Oriented Gradient have better results than others in this particular application. Combined these three features extraction techniques and selected the optimal feature set through Neighbourhood Component Analysis. The optimal feature set helps classify plants with maximum accuracy in minimal time. Here performed an extensive experimental comparison of the proposed optimal feature set and other feature extraction methods using different classifiers and tested on different data sets (Swedish Leaves, Flavia, D-Leaf). The results confirm that this optimal feature set with NCA using ANN classifier leads to better classification achieved 98.99% accuracy in 353.39 seconds.

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Automatic Classification of Medicinal Plant Species Based on Color and Texture Features

Cited by 13 publications

References 19 publications

Medicinal Plant Classification Using Particle Swarm Optimized Cascaded Network

Medicinal Plant Classification Using Particle Swarm Optimized Cascaded Network

Contemporary machine learning applications in agriculture: Quo Vadis?

Plant Leaf Classification and Comparative Analysis of Combined Feature Set Using Machine Learning Techniques

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