Food Quality Inspection and Grading Using Efficient Image Segmentation and Machine Learning-Based System

Hemamalini, V.; Subbaraj, Rajarajeswari; Nachiyappan, S.; Sambath, M.; Devi, T; Singh, Bhupesh Kumar; Raghuvanshi, Abhishek

doi:10.1155/2022/5262294

Cited by 77 publications

(24 citation statements)

References 27 publications

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“…Property agriculture relies on regular monitoring of plant health and the identification of disease. Plant diseases [5] have had a significant influence on civilization and the planet at large. Extensions of detection tactics and classification methods aim to identify and classify any illness that affects the plant rather than focusing on one particular disease among a wide range of diseases and symptoms.…”

Section: Introductionmentioning

confidence: 99%

Improved Support Vector Machine and Image Processing Enabled Methodology for Detection and Classification of Grape Leaf Disease

Ansari

Jawarneh²,

Ritonga

et al. 2022

Journal of Food Quality

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In recent years, agricultural image processing research has been a key emphasis. Image processing techniques are used by computers to analyze images. New advancements in image capture and data processing have simplified the resolution of a wide range of agricultural concerns. Crop disease classification and identification are crucial for the agricultural industry’s technical and commercial well-being. In agriculture, image processing begins with a digital color picture of a diseased leaf. Plant health and disease detection must be monitored on a regular basis in property agriculture. Plant diseases have had a tremendous impact on civilization and the Earth as a whole. Extensions of detection strategies and classification methods try to identify and categorize each ailment that affects the plant rather than focusing on a single disease among several illnesses and symptoms. This article describes a new support vector machine and image processing-enabled approach for detecting and classifying grape leaf disease. The given architecture includes steps for image capture, denoising, enhancement, segmentation, feature extraction, classification, and detection. Image denoising is conducted using the mean function, image enhancement is performed using the CLAHE method, pictures are segmented using the fuzzy C Means algorithm, features are retrieved using PCA, and images are eventually classed using the PSO SVM, BPNN, and random forest algorithms. The accuracy of PSO SVM is higher in performing classification and detection of grape leaf diseases.

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Section: Introductionmentioning

confidence: 99%

Improved Support Vector Machine and Image Processing Enabled Methodology for Detection and Classification of Grape Leaf Disease

Ansari

Jawarneh²,

Ritonga

et al. 2022

Journal of Food Quality

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show abstract

“…Using this technology, it is feasible to smooth photos while maintaining the borders of the images. Smoothing is done to the picture using Gaussian kernels by applying accumulative standard deviation [ 26 ].…”

Section: Introductionmentioning

confidence: 99%

Detection of Pancreatic Cancer in CT Scan Images Using PSO SVM and Image Processing

Ansari

Zamani

Mohammadi

et al. 2022

BioMed Research International

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A diagnosis of pancreatic cancer is one of the worst cancers that may be received anywhere in the world; the five-year survival rate is very less. The majority of cases of this condition may be traced back to pancreatic cancer. Due to medical image scans, a significant number of cancer patients are able to identify abnormalities at an earlier stage. The expensive cost of the necessary gear and infrastructure makes it difficult to disseminate the technology, putting it out of the reach of a lot of people. This article presents detection of pancreatic cancer in CT scan images using machine PSO SVM and image processing. The Gaussian elimination filter is utilized during the image preprocessing stage of the removal of noise from images. The K means algorithm uses a partitioning technique to separate the image into its component parts. The process of identifying objects in an image and determining the regions of interest is aided by image segmentation. The PCA method is used to extract important information from digital photographs. PSO SVM, naive Bayes, and AdaBoost are the algorithms that are used to perform the classification. Accuracy, sensitivity, and specificity of the PSO SVM algorithm are better.

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“…To set aside cash, this supposition that is made, and it is considered naive. [21][22][23][24][25][26][27][28][29][30][31][32].…”

Section: Literature Surveymentioning

confidence: 99%

An Inductive Learning approaches on Wheat Disease by using Simple Color Histogram and Gabor Filter of Image Equalization Techniques

R.¹,

V.²

2022

INDJCSE

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This work governs that to detect diseased wheat leaf infected images. Agriculture's automatic leaf infection detection system contains the image gathering, image processing, image feature extraction, selection, and learning. This system suggestions the farmer with a fast and precise diagnosis of the plant infections. Automation of plant leaf disease identification system is an important for rushing crop diagnosis. This is research work finds that the Simple Color Histogram with Bayes Net model has highest performance. Actually, the highest accuracy is given by SCHFBN model, SCHFNB model and SCHFNBU model are producing 96.67% of accuracy. The highest positive predictive rate value is shown by by SCHFBN model, SCHFNB model and SCHFNBU model are producing 0.97 of positive predictive value. The maximum hit rate value is owned by SCHFBN model, SCHFNB model and SCHFNBU model are having 0.97 of hit rate value. The highest ROC value is given by SCHFBN model is having 1 of ROC value. The highest PRC value is given by SCHFBN model which is having 1 of PRC value. The SCHFNB model and SCHFNBU model are producing same PRC value which is 0.97 of PRC value. The highest time consumption is taking to build the GFBN model which is 0.09 seconds. The least time consumption is zero seconds to build SCHFNBU model and GFNB Updateable models. The highest F1-Score value is given by SCHFBN model, SCHFNB model and SCHFNBU model are securing is 0.97. The maximum Matthews Correlation Coefficient value is given by SCHFBN model, SCHFNB model and SCHFNBU model are holding 0.95 of MCC value. The SCHF produces lowest deviation compare with GF technique. This research work recommends that the simple color histogram filter using Bayes Net gives better result compare with other models.

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Food Quality Inspection and Grading Using Efficient Image Segmentation and Machine Learning-Based System

Cited by 77 publications

References 27 publications

Improved Support Vector Machine and Image Processing Enabled Methodology for Detection and Classification of Grape Leaf Disease

Improved Support Vector Machine and Image Processing Enabled Methodology for Detection and Classification of Grape Leaf Disease

Detection of Pancreatic Cancer in CT Scan Images Using PSO SVM and Image Processing

An Inductive Learning approaches on Wheat Disease by using Simple Color Histogram and Gabor Filter of Image Equalization Techniques

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