CNN based Disease Detection Approach on Potato Leaves

Asif, Md. Khalid Rayhan; Rahman, Md. Atiqur; Hena, Most. Hasna

doi:10.1109/iciss49785.2020.9316021

Cited by 65 publications

(15 citation statements)

References 7 publications

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“…Md. K. R. Asif et al [14] build a customized CNN model named sequential model and data augmentation technique for potato leaf disease detection. The customized model achieved the best result with an accuracy of 97% compared to pretrained models.…”

Section: Of 22mentioning

confidence: 99%

“…The work related to potato leaf disease detection is summarized in Table 1. RF-97% [14] Kaggle, Dataquest, and some manual images Customized CNN named Sequential Model, Data Augmentation 97% [15] Potato plantation in Malang, Indonesia, and Google images VGG16, VGG19 91%…”

Section: Of 22mentioning

confidence: 99%

“…It is a popular benchmark for accuracy or error calculations in classification problems. The performance metrics are accuracy, precision, and recall are calculated by using a confusion matrix [14,20]. The confusion matrix displays a summary of actual versus predicted values, encompassing True Positives (TP), False Positives (FP), True Negatives (TN), and False Negatives (FN) within a matrix structure.…”

Section: Evaluation and Recognitionmentioning

confidence: 99%

See 2 more Smart Citations

An Efficient Convolution Neural Network Based Novel Framework for Potato Leaf Diseases Classification and Identification

Khatun,

Halder,

Arafat

et al. 2023

Preprint

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Agriculture is one of the indispensable fields for the survival of mankind. Potatoes also have a significant role in the field of agriculture. The quality and quantity of potatoes are significantly impacted by several diseases, such as early blight and late blight, and manual interpretation of these leaf diseases is time-consuming and inconvenient. Fortunately, leaf appearance is used to detect diseases in potato plants. Productivity will considerably rise if the infections are detected early. Different types of image processing methods and machine learning methods are used for early recognition of those diseases from leaf images so that the product losses are decreased significantly. For the incredible and marvelous performance of CNN, it is the most popular deep learning method that is used immensely for the recognition of leaf diseases from images. Several pretrained deep learning models, such as VGG16, ResNet50, InceptionV3, MobileNetV2, Xception, and a deep learning model developed using CNN, are employed for potato leaf disease classification and recognition on the same dataset. The transfer learning technique is applied to the pretrained model and the data augmentation technique is applied to the Proposed CNN model for potato disease classification from leaf samples. Compared to pretrained models, the proposed CNN model offers the lowest loss and highest accuracy for potato leaf disease detection while using fewer parameters and layers. It achieves the best performance with a test accuracy of 99.33% compared to other Pretrained models used in the diagnosis of potato leaf disease.

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Section: Of 22mentioning

confidence: 99%

Section: Of 22mentioning

confidence: 99%

Section: Evaluation and Recognitionmentioning

confidence: 99%

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An Efficient Convolution Neural Network Based Novel Framework for Potato Leaf Diseases Classification and Identification

Khatun,

Halder,

Arafat

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

“…CNN learning architectures are highly dependent on the data provided by the algorithm, which is finally used for applications like forecasting or classification. The algorithm computes future maps through the use of AFs [ 46 ]. Mathematically, the function is defined as

where y j l is called the future map and f ( z j l ) is called the AF.…”

Section: Major Findingsmentioning

confidence: 99%

Applications of Computer Vision on Automatic Potato Plant Disease Detection: A Systematic Literature Review

Sinshaw

Assefa

Mohapatra

et al. 2022

Computational Intelligence and Neuroscience

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In most developing countries, the contribution of agriculture to gross domestic product is significant. Plant disease is one of the major factors that adversely affect crop yield. Traditional plant disease detection techniques are time-consuming, biased, and ineffective. Potato is among the top consumed plants in the world, in general, and in developing countries, in particular. However, potato is affected by different kinds of diseases which minimize their yield and quantity. The advancement in AI and machine learning has paved the way for new methods of tackling plant disease detection. This study presents a comprehensive systematic literature review on the major diseases that harm potato crops. In this effort, computer vision-based techniques are employed to identify potato diseases, and types of machine learning algorithms used are surveyed. In this review, 39 primary studies that have provided useful information about the research questions are chosen. Accordingly, the most common potato diseases are found to be late blight, early blight, and bacterial wilt. Furthermore, the review discovered that deep learning algorithms were more frequently used to detect crop diseases than classical machine learning algorithms. Finally, the review categorized the state-of-the-art algorithms and identifies open research problems in the area.

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“…Therefore, several studies have applied a computer vision approach to assist people with classification, defect detection, quality inspection, and grading of fruits [4][5][6][7][8][9], vegetables [10][11][12], grains [13][14][15], and other food products [16,17]. For example, Cervantes-Jilaja et al [13] proposed a computer vision-based method to detect and identify visual defects in chestnuts using external features such as shape, color, size, and texture.…”

Section: Introductionmentioning

confidence: 99%

A Novel Approach to Cashew Nut Detection in Packaging and Quality Inspection Lines

Pham¹,

Nguyen²,

Pham³

2022

IJACSA

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YOLO standing for You Only Look Once is one of the most famous algorithms in computer vision used for detecting objects in a real-time environment. The newest version of this algorithm, namely YOLO with the seventh version or YOLOv7, is proposed in the present study for cashew nut detection (good, broken and not peeled) in packaging and quality inspection lines. Furthermore, this algorithm using an efficient convolutional neural network (CNN) to be able to successfully detect and identify unsatisfactory cashew nuts, such as chipped or burnt cashews. In order to deal with the quality inspection process, a new dataset called CASHEW dataset has been built at first by collecting cashew images in environments with different brightness and camera angles to ensure the model's effectiveness. The quality inspection of cashew nuts is tested with a huge number of YOLOv7 models and their effectiveness will also be evaluated. The experimental results show that all models are able to obtain high accuracy. Among them, the YOLOv7-tiny model employs the least number of parameters, i.e. 6.2M but has many output parameters with higher accuracy than that of some other YOLO models. As a result, the proposed approach should clearly be one of the most feasible solutions for the cashew's quality inspection.

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CNN based Disease Detection Approach on Potato Leaves

Cited by 65 publications

References 7 publications

An Efficient Convolution Neural Network Based Novel Framework for Potato Leaf Diseases Classification and Identification

An Efficient Convolution Neural Network Based Novel Framework for Potato Leaf Diseases Classification and Identification

Applications of Computer Vision on Automatic Potato Plant Disease Detection: A Systematic Literature Review

A Novel Approach to Cashew Nut Detection in Packaging and Quality Inspection Lines

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