Diagnosis of some apple fruit diseases by using image processing and artificial neural network

Azgomi, Hossein; Haredasht, Fatemeh Roshannia; Motlagh, Mohammad Reza Safari

doi:10.1016/j.foodcont.2022.109484

Cited by 40 publications

(18 citation statements)

References 11 publications

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“…However, due to their complicated structure, their proposed system could process only a small number of apples in real-time sorting. Azgomi et al (2023) 28 employed digital image processing and an artificial neural network for automatic diagnosis of apple diseases with 73.7% accuracy. Another study employed the Gabor transform to rate the exterior quality of apples, which is based on a PCI (Image Processing Technique) Express Interface and an image sensing device.…”

Section: Apple Sorting Based On Digital Image Processingmentioning

confidence: 99%

New Insights into Recent Trends and Emerging Technologies in Apple Sorting

Meenu,

Choudhary,

Kaushal

et al. 2024

ACS Food Sci. Technol.

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Apple sorting is a crucial step for their grading and determination of their commercial value. Sorting techniques are employed at several stages of production process, including harvest, storage, and packing. Manually apples are sorted and graded according to size and color, which is time-consuming, labor-intensive, and error-prone. Therefore, investigating nondestructive, precise, and effective methods of apple sorting is necessary to surmount the drawbacks connected with this. Technology advancements have led to the invention of several automated sorting techniques based on digital image processing, spectral and optical properties that are rapid and efficient compared to manual sorting. These technologies have advanced significantly for assessing the physical and nutritional quality of apples along with internal defects. In the present review, an overview of these advanced apple sorting methods is provided in detail. The contemporary techniques have improved apple sorting accuracy and efficiency while reducing labor costs and improving the quality. Computer vision systems can achieve high levels of accuracy and consistency by enabling nondestructive and quick assessment of many quality metrics, decreasing the subjectivity involved with human sorting. Spectral analysis and hyperspectral imaging have potential for determining interior characteristics like sugar content and ripeness. Robotics, machine learning, and sensor technologies can improve the efficiency, precision, and adaptability of sorting systems by learning from prior sorting data and adjusting to fresh variations in apple quality. In addition, sorting methods combining different techniques need to be developed to detect internal and external quality of apples.

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Section: Apple Sorting Based On Digital Image Processingmentioning

confidence: 99%

New Insights into Recent Trends and Emerging Technologies in Apple Sorting

Meenu,

Choudhary,

Kaushal

et al. 2024

ACS Food Sci. Technol.

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“…Nevertheless, food security continues to be hindered by several concerns, including plant pests and diseases, climate change, and others. Plant pests and diseases traditionally affect the public because an enormous population across the globe depends on subsistence farming 1 . Globally, different plants, including pears ( Pyrus communis ), cherries ( Prunus avium ), apples ( Malus domestica ), peaches ( Prunus persica ), apricots ( Prunus armeniaca ), and plums ( Prunus domestica ), amongst others, are grown for both subsistence and commercial welfare 2 …”

Section: Introductionmentioning

confidence: 99%

“…Modern science has provided farmers with the capacity to increase food production to meet the current food demand of approximately 8 billion people, as reported by the United Nations. 1 Nevertheless, food security continues to be hindered by several concerns, including plant pests and diseases, climate change, and others. Plant pests and diseases traditionally affect the public because an enormous population across the globe depends on subsistence farming.…”

Section: Introductionmentioning

confidence: 99%

“…Plant pests and diseases traditionally affect the public because an enormous population across the globe depends on subsistence farming. 1 Globally, different plants, including pears (Pyrus communis), cherries (Prunus avium), apples (Malus domestica), peaches (Prunus persica), apricots (Prunus armeniaca), and plums (Prunus domestica), amongst others, are grown for both subsistence and commercial welfare. 2 Plant pests and disease symptoms are complex with respect to being identified at an early stage, increasing disease spread on the farm at a cost to the farmers.…”

Section: Introductionmentioning

confidence: 99%

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Using a novel convolutional neural network for plant pests detection and disease classification

et al. 2023

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Background Early plant diseases and pests identification reduces social, economic, and environmental deficiencies entailing toxic chemical utilization on agricultural farms, thus posing a threat to global food security. Methodology An enhanced convolutional neural network (CNN) along with long short‐term memory (LSTM) using a majority voting ensemble classifier has been proposed to tackle plant pest and disease identification and classification. Within pre‐trained models, deep feature extractions have been obtained from connected layers. Deep features have been extracted and are sent to the LSTM layer to build a robust, enhanced LSTM‐CNN model for detecting plant pests and diseases. Experiments were carried out using a Turkey dataset, with 4447 apple pests and diseases categorized into 15 different classes. Results The study was evaluated in different CNNs using logistic regression (LR), LSTM, and extreme learning machine (ELM), focusing on plant disease detection problems. The ensemble majority voting classifier was used at the LSTM layer to detect and classify plant disease labels. Furthermore, an autonomous selection of the optimal LSTM layer network parameters was applied. Finally, the performance was validated based on sensitivity, F1 score, accuracy, and specificity using LSTM, ELM, and LR classifiers. Conclusion The presented model attained 99.2% accuracy compared to the cutting‐edge models on different classifiers such as LSTM, LR, and ELM, and performed better compared to transfer learning. Pre‐trained models, such as VGG19, VGG18, and AlexNet, demonstrated better accuracy when the fc6 layer was compared with other layers. © 2023 Society of Chemical Industry.

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“…Ghooshkhaneh and Mollazade (2023) used all the optical methods to identify citrus fungal diseases, including imaging‐based methods and spectroscopic‐based methods. Azgomi et al (2023) introduced a low‐cost method of apple disease diagnosis using a neural network and fruits were classified into four classes: scab, bitter rot, black rot and healthy fruits. A digital image‐based CNN model was developed by Pathmanaban et al (2023) to classify the quality of damaged and diseased fruits.…”

Section: Introductionmentioning

confidence: 99%

Implementing deep‐learning techniques for accurate fruit disease identification

Sujatha

Mahalakshmi

Chatterjee³

2023

Plant Pathology

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To overcome the problems of manual identification of fruit disease, this work proposes a deep‐learning model to analyse fruit images to detect diseases in the fruit. We are proposing here a convolutional neural network (CNN)‐based model for fruit disease classification. By including many layers, the proposed CNN model extracts numerous features from the fruit, deals with the large data set and finally evaluates it. With the MobileNetv2 model, the disease prediction accuracy for papaya, guava and citrus was 99.4%, 98.8% and 95.8% and the recall values were 99.4%, 98.8% and 93.8%, respectively. With VGG16, the disease prediction accuracy for papaya, guava and citrus was 97.7%, 99.6% and 94.2% and the recall values were 96.5%, 99.6% and 89.2%, respectively. Finally, with DenseNet121, the disease prediction accuracy for papaya, guava and citrus was 99.4%, 97.6% and 99.2%, and the recall values were 98.8%, 97.6% and 99.2%, respectively.

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Diagnosis of some apple fruit diseases by using image processing and artificial neural network

Cited by 40 publications

References 11 publications

New Insights into Recent Trends and Emerging Technologies in Apple Sorting

New Insights into Recent Trends and Emerging Technologies in Apple Sorting

Using a novel convolutional neural network for plant pests detection and disease classification

Implementing deep‐learning techniques for accurate fruit disease identification

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