An Enhanced Identification and Classification Algorithm for Plant Leaf Diseases Based on Deep Learning

Umamageswari, A.; Duela, J. Shiny; Sara, Dioline; Kothandaraman, Raja

doi:10.18280/ts.390328

Cited by 10 publications

(7 citation statements)

References 22 publications

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“…To improve the result of the segmentation, researchers will Accuracy 91%. [56] FCM is used to find the spots on the leaf that are affected.…”

Section: Segmentation Process On Plant Leavesmentioning

confidence: 99%

“…The Hybrid dataset was 99.5% accurate, while the Fruit dataset was 94% accurate, and the Leaf dataset was 97.7% accurate. This study shows that 97% of the time, sick leaf spots are found using fuzzy c-means grouping [56]. Grey-level co-occurrence matrix (COLCM) is used to find the features, and progressive neural architecture search is used to put them into groups.…”

Section: Machine Learning Based Classifier Techniquesmentioning

confidence: 99%

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Symptoms Based Image Predictive Analysis for Citrus Orchards Using Machine Learning Techniques: A Review

Dinesh,

Lakshmanan

2023

IJRITCC

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In Agriculture, orchards are the deciding factor in the country’s economy. There are many orchards, and citrus and sugarcane will cover 60 percent of them. These citrus orchards satisfy the necessity of citrus fruits and citrus products, and these citrus fruits contain more vitamin C. The citrus orchards have had some problems generating good yields and quality products. Pathogenic diseases, pests, and water shortages are the three main problems that plants face. Farmers can find these problems early on with the support of machine learning and deep learning, which may also change how they feel about technology. By doing this in agriculture, the farmers can cut off the major issues of yield and quality losses. This review gives enormous methods for identifying and classifying plant pathogens, pests, and water stresses using image-based work. In this review, the researchers present detailed information about citrus pathogens, pests, and water deficits. Methods and techniques that are currently available will be used to validate the problem. These will include pre-processing for intensification, segmentation, feature extraction, and selection processes, machine learning-based classifiers, and deep learning models. In this work, researchers thoroughly examine and outline the various research opportunities in the field. This review provides a comprehensive analysis of citrus plants and orchards; Researchers used a systematic review to ensure comprehensive coverage of this topic.

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“…To improve the result of the segmentation, researchers will Accuracy 91%. [56] FCM is used to find the spots on the leaf that are affected.…”

Section: Segmentation Process On Plant Leavesmentioning

confidence: 99%

Section: Machine Learning Based Classifier Techniquesmentioning

confidence: 99%

Symptoms Based Image Predictive Analysis for Citrus Orchards Using Machine Learning Techniques: A Review

Dinesh,

Lakshmanan

2023

IJRITCC

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“…All the nonlinear curve fits' correlation coefficients (R2) are more than 0.98. A novel tool for farmers to automatically detect plant leaf diseases is discussed in the study of [31]. The diseased leaf spots are first identified using fuzzy c-means clustering.…”

Section: Literature Reviewmentioning

confidence: 99%

Thermal Image Diseases Identification Using Hybrid Genetic Algorithm with Relevance Vector Machine Classification

Anitha¹,

Srinivasan²

2022

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For thermal-based applications, images are obtained by an Infrared camera through Plank’s law. Thermal sensitivity is the smallest temperature difference detected by the camera. Thermal-images were captured through the heat emitted from plant leaves. Initially, the Gaussian noise in the medical Infrared (IR) images is pre-processed by the median filter. Then the features from the preprocessed images are obtained through the principal component analysis algorithm. From the extracted features, the optimal features are selected using the Scale Invariant Differential Evolution-based Feature (SIDEF) algorithm. Through the exploitation of the selected features, the hybrid genetic algorithm with Relevance Vector Machine (HGRVMA) classifier classifies the features into diseases and non-diseases. To validate the performance of the proposed algorithm, it is compared with the existing algorithms in terms of metrics such as sensitivity, accuracy, precision, and recall. The validation results prove that the proposed HGRVM algorithm is optimal than the existing algorithms for all the metrics.

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“…Sabrol and Satish [7] have used a classification tree for the purpose of tomato plant disease classification based on five types of tomato disease. A review on different ML classifiers such as SVM, KNN, RF, Naive Bayes (NB), Fuzzy classifier and artificial neural network is done in the plant disease research [8][9][10][11][12][13][14]. Bauer et al [15] have used high-resolution multi-spectral images for the classification of diseases in sugar beet leaves based on conditional random fields.…”

Section: Related Workmentioning

confidence: 99%

Evaluation of Machine Learning Models for Plant Disease Classification Using Modified GLCM and Wavelet Based Statistical Features

Tabbakh¹,

Barpanda²

2022

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In this paper, different types of plant diseases in the PlantVillage dataset are getting focused for classification. In the realm of machine vision, plant disease identification is one of the most crucial tasks in the agricultural sector. It is a technique that employs equipment to capture images to detect and classify different types of diseases in plants. However, nakedeye monitoring of plants is impractical due to long processing times and a lack of specialists on farms in remote locations. Hence, combining image processing techniques with machine learning provides a solution to the problem of agricultural production while also ensuring food security. The plant features are extracted using a modified gray-level co-occurrence matrix (GLCM) technique and based on various statistical features. Both of these approaches were applied on original images with background and segmented images without background. Wavelet transform is also used with segmented images to decompose the image into sub-bands. All the features obtained are combined and SMOTE technique is used to balance the dataset prior to classification. For the purpose of classification, six machine learning models were compared, namely Light Gradient Boosting Machine (LGBM), Random Forest (RF), Decision Trees (DT), Logistic Regression (LG), AdaBoost, and Support Vector Machine (SVM). Further, different combinations of features were experimented and the experimental results prove that employing LGBM and SVM models resulted in attaining higher accuracy values i.e. 94.39% and 93.15%, respectively.

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An Enhanced Identification and Classification Algorithm for Plant Leaf Diseases Based on Deep Learning

Cited by 10 publications

References 22 publications

Symptoms Based Image Predictive Analysis for Citrus Orchards Using Machine Learning Techniques: A Review

Symptoms Based Image Predictive Analysis for Citrus Orchards Using Machine Learning Techniques: A Review

Thermal Image Diseases Identification Using Hybrid Genetic Algorithm with Relevance Vector Machine Classification

Evaluation of Machine Learning Models for Plant Disease Classification Using Modified GLCM and Wavelet Based Statistical Features

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