Leaf Diseases Detection of Medicinal Plants Based on Support Vector Machine Classification Algorithm

Bose, Payal; Dutta, Shawni; Goyal, Vishal; Bandyopadhyay, Samir Kumar

doi:10.9734/jpri/2021/v33i42a32391

Cited by 6 publications

(3 citation statements)

References 12 publications

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“…Overall, the algorithms showing the best results for the three metrics were LR and SVM using reflectance, i.e., using all the information from the spectral range provided by the sensor. The authors of [47] found high accuracy values in the classification of diseased and healthy leaves by using the SVM algorithm, similarly to [44], who used LR to find a disease detection model. The SVM has been proven to be an effective algorithm in several classification tasks, such as classifying soybean genotypes regarding the primary macronutrient contents [48] and classifying soybean genotypes according to their content of industrial grain parameters [10].…”

Section: Discussionmentioning

confidence: 91%

Hyperspectral Response of the Soybean Crop as a Function of Target Spot (Corynespora cassiicola) Using Machine Learning to Classify Severity Levels

de Queiroz Otone,

Theodoro,

Santana

et al. 2024

AgriEngineering

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Plants respond to biotic and abiotic pressures by changing their biophysical and biochemical aspects, such as reducing their biomass and developing chlorosis, which can be readily identified using remote-sensing techniques applied to the VIS/NIR/SWIR spectrum range. In the current scenario of agriculture, production efficiency is fundamental for farmers, but diseases such as target spot continue to harm soybean yield. Remote sensing, especially hyperspectral sensing, can detect these diseases, but has disadvantages such as cost and complexity, thus favoring the use of UAVs in these activities, as they are more economical. The objectives of this study were: (i) to identify the most appropriate input variable (bands, vegetation indices and all reflectance ranges) for the metrics assessed in machine learning models; (ii) to verify whether there is a statistical difference in the response of NDVI (normalized difference vegetation index), grain weight and yield when subjected to different levels of severity; and (iii) to identify whether there is a relationship between the spectral bands and vegetation indices with the levels of target spot severity, grain weight and yield. The field experiment was carried out in the 2022/23 crop season and involved different fungicide treatments to obtain different levels of disease severity. A spectroradiometer and UAV (unmanned aerial vehicle) imagery were used to collect spectral data from the leaves. Data were subjected to machine learning analysis using different algorithms. LR (logistic regression) and SVM (support vector machine) algorithms performed better in classifying target spot severity levels when spectral data were used. Multivariate canonical analysis showed that healthy leaves stood out at specific wavelengths, while diseased leaves showed different spectral patterns. Disease detection using hyperspectral sensors enabled detailed information acquisition. Our findings reveal that remote sensing, especially using hyperspectral sensors and machine learning techniques, can be effective in the early detection and monitoring of target spot in the soybean crop, enabling fast decision-making for the control and prevention of yield losses.

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Section: Discussionmentioning

confidence: 91%

Hyperspectral Response of the Soybean Crop as a Function of Target Spot (Corynespora cassiicola) Using Machine Learning to Classify Severity Levels

de Queiroz Otone,

Theodoro,

Santana

et al. 2024

AgriEngineering

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“…Akurasi LBP+ SVM adalah 40,6% dan kombinasi fitur HOG dan LBP dengan SVM sebagai klasifikasi mencapai akurasi sebesar 91,25%. Dalam penelitian [12] tentang deteksi penyakit daun pada tanaman obat. Daun diekstraksi menggunakan algoritma HOG dan LBP kemudian diklasifikasi dengan algoritma SVM dan menghasilkan akurasi sekitar 99%.…”

Section: Pendahuluanunclassified

Klasifikasi Daun Herbal Berdasarkan Fitur Bentuk dan Tekstur Menggunakan KNN

Meiriyama¹,

Devella²,

Adelfi³

2022

JATISI

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Indonesia memiliki kekayaan dan keanekaragaman hayati yang sangat melimpah. Dari total sekitar 40.000 jenis tanaman herbal yang dikenal di dunia, terdapat kurang lebih 30.000 tanaman herbal disinyalir berada di Indonesia. Tanaman herbal merupakan tanaman yang umum digunakan masyarakat, khususnya di Indonesia yang memiliki keanekaragaman hayati sebagai bahan pembuatan obat herbal. Tanaman herbal tentu tidak mudah untuk dikenali meskipun sering tumbuh di sekitar lingkungan. Karena masih kurangnya pengetahuan masyarakat tentang tanaman herbal, maka tidak memungkinkan untuk memanfaatkan tanaman herbal tersebut. Penelitian ini bertujuan untuk mengklasifikasikan daun tanaman herbal menggunakan metode K-Nearest Neighbor (KNN) dengan nilai K = 3 dan ekstraksi ciri Histogram of Oriented Gradient (HOG) dan Local Binary Patterns (LBP). Penelitian dilakukan pada 15 jenis tanaman herbal. Akurasi metode HOG dengan KNN adalah 92,67%, Akurasi LBP dengan KNN adalah 88,67% dan akurasi kombinasi fitur HOG dan LBP dengan metode KNN adalah 92,67%. Berdasarkan ketiga skenario percobaan yang telah dilakukan menunjukkan bahwa kombinasi fitur HOG dan LBP tidak mempengaruhi akurasi klasifikasi daun tanaman herbal.

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“…The filter masks derived from Laws' masks of length 9 were found to have the highest classification accuracy (90.27 percent). Various techniques had been studied and suggested in this paper are those of image enhancement, feature extraction, and classification [17]. All extracted the features are compared.…”

Section: Review Of Previous Workmentioning

confidence: 99%

Identification and classification of medicinal plants using leaf with deep convolutional neural networks

Bhelkar¹,

Sharma²

2022

ijhs

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The Indian medical practise of Ayurveda has gained international renown. Herbal preparations are the basis of Ayurveda medicine. The pharmaceutical industry is beginning to pay more attention to medicinal plants because they have fewer adverse effects and reactions than modern medicine and are also less expensive. In recent years, numerous Deep learning, machine learning algorithms that are both effective and reliable have been utilised for plant classifications by using images of leaf. In this work, 45 distinct medicinal plant leaves were used, and a deep learning model was applied in order to achieve a high degree of accuracy in the classification and recognition procedures that were carried out with the help of computer vision techniques. After categorising the leaves of numerous medicinal plants, the Xception model has a 97.65% accuracy rate.

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Leaf Diseases Detection of Medicinal Plants Based on Support Vector Machine Classification Algorithm

Cited by 6 publications

References 12 publications

Hyperspectral Response of the Soybean Crop as a Function of Target Spot (Corynespora cassiicola) Using Machine Learning to Classify Severity Levels

Hyperspectral Response of the Soybean Crop as a Function of Target Spot (Corynespora cassiicola) Using Machine Learning to Classify Severity Levels

Klasifikasi Daun Herbal Berdasarkan Fitur Bentuk dan Tekstur Menggunakan KNN

Identification and classification of medicinal plants using leaf with deep convolutional neural networks

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