Efficacy of chili plant diseases classification using deep learning: a preliminary study

Rozlan, Suhana; Hanafi, Marsyita

doi:10.11591/ijeecs.v25.i3.pp1442-1449

Cited by 4 publications

(3 citation statements)

References 17 publications

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“…The CNN is one of many Artificial Intelligence methods; more precisely is a type of the Neural Network employed in the computer vision domain [19], [20]. The main success of CNN is its ability to learn and classify large image datasets with great results [21] versus traditional neural networks and machine learning algorithms that increase the level of plant diseases detection [12].…”

Section: Convolutional Neural Network Cnnmentioning

confidence: 99%

Performance analysis of the application of convolutional neural networks architectures in the agricultural diagnosis

Belattar

Abdoun

Haimoudi

2022

IJEECS

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Agriculture is an important sector for developing countries and farmers. Recently, numerous techniques for increasing agricultural productivity have been utilized. However, different issues are still encountered by farmers including various plant diseases. Plant diseases diagnoses are challenging research, and they should be analyzed and treated by detecting the diseased plant leaves. For that reason, in this paper, we develop our proposed architecture using convolutional neural networks (OP-CNN) as a computer-aided to detect and diagnose plant diseases. The proposed architecture can assist farmers in increasing both the quantity and quality of their agricultural productivity. Besides this, the OP-CNN helps to reduce disease prevalence through early detection. The performance of our proposed model is compared with other convolutional neural networks (CNN) architectures in order to validate its capability. The strawberry dataset was employed to train and test the models since the strawberry is one of the main crops in the Larache Province (Morocco). The experimental tests demonstrate that our proposed OP-CNN reaches the highest values versus DenseNet121, VGG19, and ResNet50 with 100%, 99%, 97%, and 63% respectively for classification accuracy, 100%, 100%, 98% and, 79% respectively for precision, 100%, 99%, 97%, and 63% respectively for recall, and 100%, 99%, 97%, and 58% respectively for "F" _1Score.

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Section: Convolutional Neural Network Cnnmentioning

confidence: 99%

Performance analysis of the application of convolutional neural networks architectures in the agricultural diagnosis

Belattar

Abdoun

Haimoudi

2022

IJEECS

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“…Referring to Rozlan and Hanafi (2022), deep learning algorithms, namely VGG16, InceptionV3, and EfficientNetB0, are explored in identifying the diseases that affect the Chilli leaf. The diseases are identified as upward curling, mosaic/mottling, and bacterial spots.…”

Section: Introductionmentioning

confidence: 99%

RNDDNet: A residual nested dilated DenseNet based deep-learning model for chilli plant disease classification

Srinivasulu,

Maiti

2024

Eng. Res. Express

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The most significant peril to food safety arises from plant diseases, capable of substantially diminishing both the quantity and quality of agricultural yields. Identifying these plant diseases stands out as the foremost challenge within the agricultural sector. Convolutional and deep neural networks prove effective in resolving image classification challenges within the realm of computer vision. Numerous Deep Neural Network(DNN)-based structures have been employed to diagnose plant diseases. Many DNN models in the field make use of various iterations of Dense and DenseNet layers in order to enhance the receptive field and capture intricate features within the data. However, it is important to note that such models often come with a significant computational burden and can introduce aliasing artifacts due to their complexity and resource-intensive nature. To overcome those limitations, we proposed a novel Residual Nested Dilated DenseNet based deep-learning (RNDDNet) model in this paper. Residual Nested Dilated DenseNet model residual connections are achieving the required receptive field, and their dilation factors are effective in extracting more features. The RNDDNet model exhibits the highest level of accuracy in identifying plant diseases. This research introduces a less computational cost and compact model to detect diseases in plant leaves. The proposed model functions to identify diseases, utilizing a dataset comprising 3,800 photographs of chilli leaves, categorized into six distinct classes: five disorder classes and one healthy chilli class. Through experimentation, the outcomes indicate that the suggested model achieves an accuracy of 98.09 %, along with a precision of 97 %, a recall of 97.25 %, and an F1 score of 97.25%. The presented approach demonstrates its superiority over existing methodologies.

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“…ResNet50 obtained less accuracy than other pre-trained CNNs, 81.57%, and also consumed more training time than other CNNs with 50 epochs. Rozlan and Hanafi [12] compared the performance of pre-trained CNNs including VGG16, InceptionV3, and EfficientNetB0 for classifying diseases of the chili plant, where InceptionV3 obtained 97.67% and 98.83% accuracy, respectively, on the dataset of original and augmented images. On original images, EfficientNetB0 performed better than VGG16 and InceptionV3, which obtained 97.67% accuracy, where it acquired less accuracy on augmented images, 96.83%.…”

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confidence: 99%

Improved vision-based diagnosis of multi-plant disease using an ensemble of deep learning methods

Hridoy

Arni

Haque

2023

IJECE

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<span lang="EN-US">Farming and plants are crucial parts of the inward economy of a nation, which significantly boosts the economic growth of a country. Preserving plants from several disease infections at their early stage becomes cumbersome due to the absence of efficient diagnosis tools. Diverse difficulties lie in existing methods of plant disease recognition. As a result, developing a rapid and efficient multi-plant disease diagnosis system is a challenging task. At present, deep learning-based methods are frequently utilized for diagnosing plant diseases, which outperformed existing methods with higher efficiency. In order to investigate plant diseases more accurately, this article addresses an efficient hybrid approach using deep learning-based methods. Xception and ResNet50 models were applied for the classification of plant diseases, and these models were merged using the stacking ensemble learning technique to generate a hybrid model. A multi-plant dataset was created using leaf images of four plants: black gram, betel, Malabar spinach, and litchi, which contains nine classes and 44,972 images. Compared to existing individual convolutional neural networks (CNN) models, the proposed hybrid model is more feasible and effective, which acquired 99.20% accuracy. The outcomes and comparison with existing methods represent that the designed method can acquire competitive performance on the multi-plant disease diagnosis tasks.</span>

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Efficacy of chili plant diseases classification using deep learning: a preliminary study

Cited by 4 publications

References 17 publications

Performance analysis of the application of convolutional neural networks architectures in the agricultural diagnosis

Performance analysis of the application of convolutional neural networks architectures in the agricultural diagnosis

RNDDNet: A residual nested dilated DenseNet based deep-learning model for chilli plant disease classification

Improved vision-based diagnosis of multi-plant disease using an ensemble of deep learning methods

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