Classification of Corn Diseases using Random Forest, Neural Network, and Naive Bayes Methods

Ubaidillah, Ahmad; Rochman, Eka Mala Sari; Fatah, Doni Abdul; Rachmad, Aeri

doi:10.1088/1742-6596/2406/1/012023

Cited by 12 publications

(9 citation statements)

References 16 publications

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“…all., who classified corn leaf diseases using machine learning using several methods such as Random Forest, Neural Network, and Naï ve Bayes. The results obtained from this study resulted in an accuracy of 60.76% using Naï ve Bayes, while the accuracy results using Random Fores were 69.76% and using Neural Networks were 74.44% [5].…”

Section: Introductionmentioning

confidence: 69%

“…In general, the disease that attacks corn plants is gray leaf spot, which is a disease on corn leaves that causes brownish yellow spots caused by the fungus Helminthoporium maydis [4], leaf blight disease or what is known as corn blight disease [4] and common rust disease, namely corn blight disease. leaf rust [5]. As a form of prevention so that harvest failure does not occur, monitoring corn plants for diseases that are susceptible to corn plants.…”

Section: Introductionmentioning

confidence: 99%

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Convolutional Neural Network-Based Classification Model of Corn Leaf Disease

Rachmad¹,

Fuad²,

Rochman³

2023

MMEP

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The decline in corn production can affect the continuity of food grown in society, especially in Indonesia, which is a country with a high level of corn consumers. Several factors cause a decrease in the production of corn plants, one of which is unhealthy plants so that their growth slows down and even makes the corn plants not bear fruit or are damaged. Therefore, a system is needed that can identify diseases in corn plants so that appropriate treatment can be carried out as early as possible to prevent severe damage to corn plants. With this research, the system can be built by utilizing machine learning in building a classification system using the Convolutional Neural Network (CNN) algorithm with a dataset of corn leaf images taken from farmers' fields in the Madura Region with four target classes namely healthy, gray leaf spot, blight, and common rust. Testing was carried out using several CNN architectural models such as SqueezeNet, AlexNet, ResNet-101, ResNet-50, and ResNet-18. The parameters used were 5 epochs with 100 iterations, a learning rate of 0.0001, using Adam optimization, and a data distribution of 70% for training data and 30% for testing data. The test results obtained in classifying corn images using the Convolutional Neural Network method with the ResNet-50 architecture provide a very good accuracy value of 95.59%.

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Section: Introductionmentioning

confidence: 69%

Section: Introductionmentioning

confidence: 99%

Convolutional Neural Network-Based Classification Model of Corn Leaf Disease

Rachmad¹,

Fuad²,

Rochman³

2023

MMEP

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“…At this stage, there are several common and frequently used performance matrices, namely accuracy, precision, and recall. The equations to find the values of accuracy, precision, and recall are as follows [26,27]…”

Section: Confution Matrixmentioning

confidence: 99%

Comparison of CNN Architectures for Mycobacterium Tuberculosis Classification in Sputum Images

Rachmad,

Syarief,

Hutagalung

et al. 2024

ISI

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Tuberculosis (TB) is a preventable and treatable infectious disease, but remains a serious problem in high-risk countries. Accurate early detection remains a challenge despite prevention efforts. The primary method of detecting tuberculosis is identifying bacteria in sputum samples using a microscope. This research focuses on the use of Convolutional Neural Network (CNN) with the AlexNet, ResNet-18, ResNet-50, and VGG-16 architectures in the early detection and classification of Tuberculosis (TB) through processing images of TB patients' sputum. A dataset of sputum images was collected and processed to ensure quality and adequate representation. Each CNN model was trained using deep learning techniques on the prepared dataset. The aim of this research is to compare the performance of each model in recognizing and classifying sputum images containing Mycobacterium tuberculosis bacteria and those without TB bacteria. The research results show that AlexNet architecture outperforms ResNet-18, ResNet-50 and VGG-16 in classification accuracy of Mycobacterium tuberculosis. The best validation accuracy achieved was 93.42% with the fastest time of 5 minutes and 52 seconds using AlexNet architecture. Identifying the most appropriate AlexNet architectural model could unlock the potential for developing automated systems that efficiently identify TB, thereby enabling faster and more timely medical intervention.

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“…The confusion matrix is a tool that can be used to determine the correctness of a system [24]. The confusion matrix contains information from the actual and predicted classification results at the time of text classification [25]. The performance of the system is evaluated using data in a matrix which is illustrated in Figure 3 below.…”

Section: Accuracymentioning

confidence: 99%

An Automated System for Osteoarthritis Severity Scoring Using Residual Neural Networks

Rachmad,

Sonata,

Hutagalung

et al. 2023

MMEP

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Osteoarthritis (OA) is a chronic disease, characterized by progressive deterioration of cartilage tissue and consequent thinning of the cartilage layer within joints. This degradation leads to an increased likelihood of bone collision during movement, typically manifesting in patients as joint pain, knee swelling, stiffness, and difficulties in executing daily activities. The diagnosis of OA often involves the analysis of physical examination results, patient anamnesis, and additional supportive examinations, which are predominantly conducted manually. Addressing these challenges, this study harnesses Convolutional Neural Network (CNN) algorithms, specifically the Residual Neural Network and Mobile Neural Network architectures, to develop an automated system for classifying OA severity. Utilizing a knee image dataset comprised of 8260 records procured from NDA OAI, the model is trained and tested with a data split of 80% and 20% respectively. The Residual Neural Network (ResNet-101) architecture is employed for model training, utilizing Adam optimization with a learning rate set at 0.0001 over 50 epochs. The resulting model yields a training accuracy of 67.65%, and a validation accuracy of 57.06%. This study demonstrates the potential of CNN methods for automated, accurate classification of OA severity using knee imagery, thus offering a promising avenue for enhancing diagnostic efficiency and precision.

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Classification of Corn Diseases using Random Forest, Neural Network, and Naive Bayes Methods

Cited by 12 publications

References 16 publications

Convolutional Neural Network-Based Classification Model of Corn Leaf Disease

Convolutional Neural Network-Based Classification Model of Corn Leaf Disease

Comparison of CNN Architectures for Mycobacterium Tuberculosis Classification in Sputum Images

An Automated System for Osteoarthritis Severity Scoring Using Residual Neural Networks

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