Leaf Disease Detection and Classification by Decision Tree

Rajesh, Bulla; Vardhan, Madhurima; Sujihelen, L.

doi:10.1109/icoei48184.2020.9142988

Cited by 45 publications

(14 citation statements)

References 6 publications

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“…With its various developments, the implementation of CNN provides satisfactory performance in classifying corn plant diseases [17][18][19] and other food crops [21][22][23]38]. Other food crop disease classifications using HSV [20,24,26] and Lab [25] also perform satisfactorily.…”

Section: Related Workmentioning

confidence: 99%

“…The early diagnosis of corn diseases and pests aims to reduce the likelihood of crop failure and preserve the quality and quantity of crop yields. The use of digital images as a dataset for identifying corn plant diseases and pests is increasing rapidly [9,[14][15][16][17][18][19], as well as in other food crops [20][21][22][23][24][25][26]. This increase is because the cost is cheaper than other technologies, such as infrared light [21].…”

Section: Introductionmentioning

confidence: 99%

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Fuzzy Discretization on the Multinomial Naïve Bayes Method for Modeling Multiclass Classification of Corn Plant Diseases and Pests

et al. 2023

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As an agricultural commodity, corn functions as food, animal feed, and industrial raw material. Therefore, diseases and pests pose a major challenge to the production of corn plants. Modeling the classification of corn plant diseases and pests based on digital images is essential for developing an information technology-based early detection system. This plant’s early detection technology is beneficial for lowering farmers’ losses. The detection system based on digital images is also cost-effective. This paper aims to model the classification of corn plant diseases and pests based on digital images by implementing fuzzy discretization. Discretization is an essential technique to improve the knowledge extraction process of continuous-type data. It is also essential in some methods where continuous data must be processed or handled. Fuzzy discretization allows classes to have overlapping intervals so that they can handle information that is vague or unclear. We developed hypotheses and proved that different combinations of membership functions in fuzzy discretization affect classification performance. Empirical assessment using Monte Carlo resampling was carried out to obtain the generalizability of the performance of the best classification model of all proposed models. The best model is determined based on the number of metrics with the highest value and the highest metric on the Fscore and Kappa, a multiclass measure. The combination of digital image data preprocessing and classification methods also affects the performance of the classification model. We hope this work can provide an overview for experts in building early detection systems of corn plant diseases and pests using classification models based on fuzzy discretization.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Fuzzy Discretization on the Multinomial Naïve Bayes Method for Modeling Multiclass Classification of Corn Plant Diseases and Pests

et al. 2023

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“…With the advancement of machine learning (ML), researchers have deployed various techniques in the field of agricultural science for plant disease detection. Notably, approaches such as K-nearest neighbors (KNN) [35], support vector machines(SVM) [39], Random forest [23], and decision tree (DT) [26] have been investigated to inspect and diagnose plant diseases. Despite their simplicity and minimal data requirements for training, ML-based approaches are time-consuming and heavily rely on trained human resources.…”

Section: Introductionmentioning

confidence: 99%

An Improved YOLOv5 for Accurate Detection and Localization of Tomato and Pepper Leaf Diseases

Tej,

Bouaafia,

Hajjaji

et al. 2024

Preprint

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Agriculture serves as a vital sector in Tunisia, supporting the nation's economy and ensuring food production. However, the detrimental impact of plant diseases on crop yield and quality presents a significant challenge for farmers. In this context, computer vision techniques have emerged as promising tools for automating disease detection processes. This paper focuses on the application of the YOLOv5 algorithm for the simultaneous detection and localization of multiple plant diseases on leaves. By using a self-generated dataset and employing techniques such as augmentation, anchor clustering, and segmentation, the study aims to enhance detection accuracy. An ablation study comparing YOLOv5s and YOLOv5x models demonstrates the superior performance of YOLOv5x, achieving a mean average precision (mAP) of 96.5%.

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“…Early detection of diseases and pests of corn aims to reduce the possibility of crop failure and maintain the quality and quantity of crop yields. Detection of food plant diseases using digital images by applying classi cation tasks to statistical machine learning algorithms has become popular in recent years (Resti et al, 2022;Xian and Ngadiran, 2021;Ngugi et al, 2021;Syarief and Setiawan, 2020;Rajesh et al, 2020;Kasinathan et al, 2021;Kusumo et al, 2018). This trend occurs because detection uses low-cost digital images (Ngugi et al, 2021) .…”

Section: Introductionmentioning

confidence: 99%

“…For continuous predictor variables, algorithm C4.5 of this model makes decisions by splitting the predictor variables locally (Quinlan, 1996) , and the performance of this model increases signi cantly if the continuous predictor variables are discretized rst (Dougherty et al, 1995) . In many cases, the implementation of this model has performed well (Kresnawati et al, 2021;Resti et al, 2021;Hussein et al, 2020), including classifying plant diseases using digital images (Rajesh et al, 2020;Kranth et al, 2018). However, in some other cases, the implementation of this model does not provide satisfactory performance (Xian and Ngadiran, 2021;Sahith et al, 2019;García et al, 2015).…”

Section: Introductionmentioning

confidence: 99%

Performance Improvement of Decision Tree Model using Fuzzy Membership Function for Classification of Corn Plant Diseases and Pests

Resti

Irsan

Amini

et al. 2022

Sci. Technol. Indones

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Corn is an essential agricultural commodity since it is used in animal feed, biofuel, industrial processing, and the manufacture of non-food industrial commodities such as starch, acid, and alcohol. Early detection of diseases and pests of corn aims to reduce the possibility of crop failure and maintain the quality and quantity of crop yields. A decision tree is a nonparametric classification model in statistical machine learning that predicts target variables using tree-structured decisions. The performance of this model can increase significantly if the continuous predictor variables are discretized into valid categories. However, in some cases, the result does not provide satisfactory performance. The possible cause is the ambiguity in discretizing predictor variables. The incorporation of fuzzy membership functions into the model to resolve discretization ambiguity issues. This work aims to classify diseases and pests of corn plants using the decision tree model and improve the model’s performance by implementing fuzzy membership functions. The main contribution of this work is that we have shown a significant improvement in the decision tree model performance by implementing fuzzy membership functions; S-growth, triangle, and S-shrinkage curves. The proposed fuzzy model is better than the decision tree model, with an average performance increase from the largest to the smallest; kappa (12.16%), recall (11.8%), F-score (9.71%), precision (5.08%), accuracy (3.23%), specificity (1.94%), and AUC (0.49%). The combination of bias and variance generated by the proposed model is quite small, indicating that the model is able to capture data trends well.

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Leaf Disease Detection and Classification by Decision Tree

Cited by 45 publications

References 6 publications

Fuzzy Discretization on the Multinomial Naïve Bayes Method for Modeling Multiclass Classification of Corn Plant Diseases and Pests

Fuzzy Discretization on the Multinomial Naïve Bayes Method for Modeling Multiclass Classification of Corn Plant Diseases and Pests

An Improved YOLOv5 for Accurate Detection and Localization of Tomato and Pepper Leaf Diseases

Performance Improvement of Decision Tree Model using Fuzzy Membership Function for Classification of Corn Plant Diseases and Pests

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