Analyzing the best machine learning algorithm for plant disease classification

Neelakantan, P.

doi:10.1016/j.matpr.2021.07.358

Cited by 24 publications

(12 citation statements)

References 11 publications

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“…In the following, we presented a comparative study of MLA. The MLA find their applications in several areas, namely: text classification [13]- [17], medical diagnosis [18], pollution prediction [19], spam email detection [20], plant disease identification [21], and stock daily trading [22]. For example, The paper [13] describes the use of the KNN algorithm with the TF-IDF method for text classification.…”

Section: Related Workmentioning

confidence: 99%

Modeling of an artificial intelligence based enterprise callbot with natural language processing and machine learning algorithms

Aattouri¹,

Mouncif

Rida³

2023

IJ-AI

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<p>The management of customer services by telephone encounters several problems: an uncontrollable flow of calls, complicated resource management, a very high cost of service, and more. Opportunities to improve the quality of service, save time and money triggered the widespread implementation of artificial intelligence (AI) based callbot. This article outlines the straightforward workflow developed to model the architecture of the callbot. Therefore, several algorithms were evaluated and compared based on real knowledge of a call center of an insurance society. The algorithms considered are: k-nearest neighbours (KNN), support vector machine (SVM), random forests (RF), logistic regression (LR), and Na¨ıve Bayes (NB). The comparison criteria are: correct responses, response time, accuracy, Cohen’s kappa and F1 score using n-gram (1.1) and (2.2). The results obtained show that the SVM (accuracy=70.29%) presents the best results on all the comparison criteria. The comparison between the results of the human agents and the callbot shows an improvement in several levels: the cost savings are greater than 80% on all the tests carried out, the holding time decrease to 0 seconds, and the processing time (almost a third or more). The results obtained sufficiently meet the objectives of this project.</p>

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Section: Related Workmentioning

confidence: 99%

Modeling of an artificial intelligence based enterprise callbot with natural language processing and machine learning algorithms

Aattouri¹,

Mouncif

Rida³

2023

IJ-AI

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“…It is realized by genetic algorithm, and it also covers the investigation of different disease classification technologies that can be used for plant leaf disease detection. Literature [ 8 ] established an algorithm that combines a supervised machine learning algorithm with image processing. The author tested methods such as random forests (RF), support vector machines (SVM), decision trees (DT), and so on.…”

Section: Related Workmentioning

confidence: 99%

Disease Control and Prevention in Rare Plants Based on the Dominant Population Selection Method in Opportunistic Social Networks

Gou

Tian

2022

Computational Intelligence and Neuroscience

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The spread of seeds of rare and dangerous plants affects the regeneration, pattern, genetic structure, invasion, and settlement of plant populations. However, seed transmission is a relatively weak research link. The spread of plant seeds is not controlled by the communicator. Rather, this event results from the interaction between the host and the external environment determined by the mother. The way plants transmit and accept seeds is similar to how user nodes accept data transmission requests in social networks. Plants select the characteristics including seed size, maturity time, and gene matching, which are consistent with the size, delay, and keywords of the data received by the user. In this study, we selected rare and endangered Pterospermum heterophyllum as the research object and applied them to a social network. All plants were considered nodes and all seeds as transmitted data. This method avoids the influence of errors in actual sampling and statistical laws. By using historical information to record the reception of seeds, the Infection and Immunity Algorithm (IAIA) in opportunistic social networks was established. This method selects healthy plants through plant social populations and reduces the number of diseased plants. The experimental results show that the IAIA algorithm has a good effect in distinguishing dominant seedlings from seedlings with disease genes and realizes the selection of dominant plants in social networks.

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“…The researchers in [6], proposed a method in which they segmented the data by using multi thresholding algorithm then used Gray-Level Co-occurrence Matrix (GLCM) for feature extraction and at last used the SVM classifier for classifying 4 different classes of Tomato Plant Leaf Disease. In [7], the researcher conducted a study to identify best machine learning algorithm that could classify 5 classes of tomato leaves (4-diseased and 1-healthy). The research methodology consists of 4 stages: Image acquisition in which data was collected manually, Image Preprocessing in which images were resized to 400x400 pixels, Image Segmentation in which features were identified by the gradient of images intensity and edge detection intensity values and at last Image Classification was carried out by using Random Forest (RF), SVM, KNN, Naïve Bayes (NB) and Decision Tree (DT) classifiers that provided the respective accuracies of 89%, 83%, 82%, 81% and 77%.…”

Section: Related Workmentioning

confidence: 99%

Double Channel CNN Based Tomato Plant Leaf Disease Detection

Kumar

2021

QUESTRJ

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Early blight, late blight, bacterial spot, leaf mold, spider mites, yellow leaf curl virus and Septoria leaf spot are the most common diseases of tomato plant which cause major loss to agriculture sector. Diagnosis and prognosis at early stage are prime challenges to save life of tomato crop. This research presents a state-of-the-art Double-Channel Convolution Neural Network (D-CNN) model for diagnoses of 8 different Classes of Tomato Plant Leaves. The proposed model integrates two separate Convolutional Neural Network (CNN) channels with similar number of Conv Layers and Parameters. One Channel processes the pre-processed Red-Green-Blue (RGB) data, whereas the second channel processes the region segmented data obtained by applying a Multi-Otsu Thresholding algorithm. We verified the proposed model on the Plant Village dataset which consists of more than 16,000 images of tomato plant leaves. The proposed model attains an overall accuracy of 94% after 200 iterations, comparatively 3.2% faster than Support Vector Machine (SVM), 2.2% faster than Probabilistic Neural Network (PNN), and 8% faster than Residual Neural Network (ResNet-50).

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Analyzing the best machine learning algorithm for plant disease classification

Cited by 24 publications

References 11 publications

Modeling of an artificial intelligence based enterprise callbot with natural language processing and machine learning algorithms

Modeling of an artificial intelligence based enterprise callbot with natural language processing and machine learning algorithms

Disease Control and Prevention in Rare Plants Based on the Dominant Population Selection Method in Opportunistic Social Networks

Double Channel CNN Based Tomato Plant Leaf Disease Detection

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