Machine Learning and Deep Learning for Maize Leaf Disease Classification: A Review

Setiawan, Wahyudi; Rochman, E M S; Satoto, B D; Rachmad, A

doi:10.1088/1742-6596/2406/1/012019

Cited by 14 publications

(7 citation statements)

References 38 publications

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“…Here is an illustration of the AlexNet architecture in Figure 2. SqueezeNet is a CNN architecture that can achieve the accuracy of AlexNet, which incidentally won the ImageNet classification task in 2012 by using fewer parameters and fast training time [16]. The SqueezeNet architecture replaces several 3x3 convolution layers with 1x1 and filters are used less to shrink the dimensions of the activation map or called squeeze [17].…”

Section: Convolutional Neural Network (Cnn)mentioning

confidence: 99%

“…SqueezeNet c. Residual Neural Network (ResNet)ResNet is a type of architecture on CNN that was first introduced in 2015. This architecture is a residual network that has a high level of network depth where the deepest network in this architecture totals 152 layers[16]. The high level of depth in the CNN architecture provides an important role in building the CNN model, which can increase the accuracy of the system.…”

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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: Convolutional Neural Network (Cnn)mentioning

confidence: 99%

mentioning

confidence: 99%

Convolutional Neural Network-Based Classification Model of Corn Leaf Disease

Rachmad¹,

Fuad²,

Rochman³

2023

MMEP

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“…From the confusion matrix, we can derive the values of accuracy, recall, precision, and F-measure. The values of accuracy and precision can be obtained using Equation ( 5) and Equation (6). And, the values of recall and F-measure can be calculated using Equation (7) and Equation ( 8) [26].…”

Section: Table 1 Confusion Matrixmentioning

confidence: 99%

“…Corn serves as a traditional crop with diverse uses, including human food, animal feed, and raw materials for multiple industries [4] and [5]. The quality of corn kernels is closely tied to crop yield and production levels, making it essential to have a fast and effective method for assessing their quality [6].…”

Section: Introductionmentioning

confidence: 99%

Classification of Diseases on Corn Stalks using a Random Forest based on a Combination of the Feature Extraction (Local Binary Pattern and Color Histogram)

Aeri Rachmad,

Nachnul Ansori,

Silfia Rifka

et al. 2023

Technium

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Corn disease has a significant impact on both the food industry and the yield of corn crops since corn serves as a fundamental and essential source of nutrition, especially for vegetarians and vegans. Therefore, ensuring the quality of corn is crucial, and to achieve this, protection against various diseases is necessary. Consequently, there is a pressing demand for an automated method capable of early-stage disease detection and prompt action. However, detecting diseases at an early stage poses a major challenge and is of utmost importance. This research focuses on the development of a classification model for corn stalk images using Random Forest. The model generates fine and coarse features of high quality to capture discriminative, boundary, pattern, and structural information used in the classification process. This research also utilizes the LBP (Local Binary Pattern) method and Color Histogram in the feature extraction process to obtain information related to texture and distinguishing patterns, that are employed in the classification process. Furthermore, the proposed model is evaluated using the corn plant image dataset, which was directly captured by the researcher in Madura, and consists of 3,000 data. The result of this research shows that the utilization of the proposed method can classify and identifying diseases in new data of digital images of corn stalks with an accuracy rate of 99.05%.

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“…The Convolutional Neural Network (CNN) algorithm, a deep learning technique that mimics complex human neural networks, has demonstrated exceptional performance in image-based data classification processes [6][7][8]. Previous studies, such as Yamashita et al [9], have attested to the efficacy of the CNN method in medical research, stating its potential to enhance the performance of radiologists and improve patient handling efficiency.…”

Section: Introductionmentioning

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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Machine Learning and Deep Learning for Maize Leaf Disease Classification: A Review

Cited by 14 publications

References 38 publications

Convolutional Neural Network-Based Classification Model of Corn Leaf Disease

Convolutional Neural Network-Based Classification Model of Corn Leaf Disease

Classification of Diseases on Corn Stalks using a Random Forest based on a Combination of the Feature Extraction (Local Binary Pattern and Color Histogram)

An Automated System for Osteoarthritis Severity Scoring Using Residual Neural Networks

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