Maize Leaf Disease Detection and Classification Using Machine Learning Algorithms

Panigrahi, Kshyanaprava Panda; Das, Himansu; Sahoo, Abhaya Kumar; Moharana, Suresh Chandra

doi:10.1007/978-981-15-2414-1_66

Cited by 127 publications

(54 citation statements)

References 16 publications

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“…The performance of the MNB method is categorized as good agreement (kappa 60-80%), but the performance of the KNN method for all k is categorized as very good (kappa 80%). Compared to Panigrahi et al (2020) , who also proposed…”

Section: Resultsmentioning

confidence: 99%

“…The implementation of statistical machine learning in identifying diseases of corn plant using digital image data has been popular recently (Ngugi et al, 2021;Xian and Ngadiran, 2021;Syarief and Setiawan, 2020;Panigrahi et al, 2020;Sibiya and Sumbwanyambe, 2019;Kusumo et al, 2019;Mengistu et al, 2018). Digital image processing using the red, green, and blue (RGB) color space model is the most informative feature in de- Furthermore, this property has the highest accuracy in most machine learning approaches (Kusumo et al, 2019) .…”

Section: Introductionmentioning

confidence: 99%

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Identification of Corn Plant Diseases and Pests Based on Digital Images using Multinomial Naïve Bayes and K-Nearest Neighbor

Resti

Irsan

Putri

et al. 2022

Sci.Technol.Indonesia

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Statistical machine learning has developed into integral components of contemporary scientific methodology. This integration provides automated procedures for predicting phenomena, case diagnosis, or object identification based on previous observations, uncovering patterns underlying data, and providing insights into the problem. Identification of corn plant diseases and pests using it has become popular recently. Corn (Zea mays L) is one of the essential carbohydrate-producing foodstuffs besides wheat and rice. Corn plants are sensitive to pests and diseases, resulting in a decrease in the quantity and quality of the production. Eradicate pests and diseases according to their type is a solution to overcome the problem of disease in corn plants. This research aims to identify corn plant diseases and pests based on the digital image using the Multinomial Naïve Bayes and K-Nearest Neighbor methods. The data used consisted of 761 digital images with six classes of corn plants disease and pest. The investigation shows that the K-Nearest Neighbor method has a better predictive performance than the Multinomial Naïve Bayes (MNB) method. The MNB method with two categories has an accuracy level of 92.72%, a precision level of 79.88%, a recall level of 79.24%, F1-score 78.17%, kappa 72.44%, and AUC 71.91%. Simultaneously, the K-Nearest Neighbor approach with k=3 has an accuracy of 99.54 %, a precision of 88.57%, recall 94.38%, F1-score 93.59%, kappa 94.30%, and AUC 95.45%.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Identification of Corn Plant Diseases and Pests Based on Digital Images using Multinomial Naïve Bayes and K-Nearest Neighbor

Resti

Irsan

Putri

et al. 2022

Sci.Technol.Indonesia

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show abstract

“…In this paper, the authors reviewed many research articles and identified 129 studies eligible for systematic review using the PRISMA statement as presented in Figure S2 in the Supplementary Materials, these studies involve methodologies in image processing, machine learning, and deep learning particularly focused on the identification and classification of plant diseases. The study showed that the techniques most used in the literature, in general, are the support vector machine [22,59,61,65] (SVM), random forest [87] (RF), artificial neural network [84] (ANN) and convolutional neural network (CNN) [35,39,50].…”

Section: Discussionmentioning

confidence: 99%

On Using Artificial Intelligence and the Internet of Things for Crop Disease Detection: A Contemporary Survey

2021

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The agricultural sector remains a key contributor to the Moroccan economy, representing about 15% of gross domestic product (GDP). Disease attacks are constant threats to agriculture and cause heavy losses in the country’s economy. Therefore, early detection can mitigate the severity of diseases and protect crops. However, manual disease identification is both time-consuming and error prone, and requires a thorough knowledge of plant pathogens. Instead, automated methods save both time and effort. This paper presents a contemporary overview of research undertaken over the past decade in the field of disease identification of different crops using machine learning, deep learning, image processing techniques, the Internet of Things, and hyperspectral image analysis. Additionally, a comparative study of several techniques applied to crop disease detection was carried out. Furthermore, this paper discusses the different challenges to be overcome and possible solutions. Then, several suggestions to address these challenges are provided. Finally, this research provides a future perspective that promises to be a highly useful and valuable resource for researchers working in the field of crop disease detection.

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“…Panigrahi et al [27] focused on maize leaf disease classification using different classifiers. e performance analysis of classifiers is analyzed performed, and it was found that the Random Forest classifier suits well for their dataset.…”

Section: Related Workmentioning

confidence: 99%

A Perspective View of Cotton Leaf Image Classification Using Machine Learning Algorithms Using WEKA

Patil¹,

Burkpalli²

2021

Advances in Human-Computer Interaction

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Cotton is one of the major crops in India, where 23% of cotton gets exported to other countries. The cotton yield depends on crop growth, and it gets affected by diseases. In this paper, cotton disease classification is performed using different machine learning algorithms. For this research, the cotton leaf image database was used to segment the images from the natural background using modified factorization-based active contour method. First, the color and texture features are extracted from segmented images. Later, it has to be fed to the machine learning algorithms such as multilayer perceptron, support vector machine, Naïve Bayes, Random Forest, AdaBoost, and K-nearest neighbor. Four color features and eight texture features were extracted, and experimentation was done using three cases: (1) only color features, (2) only texture features, and (3) both color and texture features. The performance of classifiers was better when color features are extracted compared to texture feature extraction. The color features are enough to classify the healthy and unhealthy cotton leaf images. The performance of the classifiers was evaluated using performance parameters such as precision, recall, F-measure, and Matthews correlation coefficient. The accuracies of classifiers such as support vector machine, Naïve Bayes, Random Forest, AdaBoost, and K-nearest neighbor are 93.38%, 90.91%, 95.86%, 92.56%, and 94.21%, respectively, whereas that of the multilayer perceptron classifier is 96.69%.

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Maize Leaf Disease Detection and Classification Using Machine Learning Algorithms

Cited by 127 publications

References 16 publications

Identification of Corn Plant Diseases and Pests Based on Digital Images using Multinomial Naïve Bayes and K-Nearest Neighbor

Identification of Corn Plant Diseases and Pests Based on Digital Images using Multinomial Naïve Bayes and K-Nearest Neighbor

On Using Artificial Intelligence and the Internet of Things for Crop Disease Detection: A Contemporary Survey

A Perspective View of Cotton Leaf Image Classification Using Machine Learning Algorithms Using WEKA

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