Food Spoilage Detection Using Convolutional Neural Networks and K Means Clustering

Megalingam, Rajesh Kannan; Sree, Gadde Sakhita; Reddy, Gunnam Monika; Krishna, Inti Rohith Sri; Suriya, L.U.

doi:10.1109/rdcape47089.2019.8979114

Cited by 16 publications

(4 citation statements)

References 15 publications

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“…The k -NN method algorithm was selected to build a classification model. k -NN is a nonparametric machine learning method used for classification and regression . In this case, nonparametric machine learning is selected because the assumptions of any structure of the classification model are based upon the provided data without any further assumptions.…”

Section: Materials and Methodsmentioning

confidence: 99%

“…k-NN is a nonparametric machine learning method used for classification and regression. 38 In this case, nonparametric machine learning is selected because the assumptions of any structure of the classification model are based upon the provided data without any further assumptions. Each data point was assigned to the most common cluster among its nearest k neighbors, and at every point, on the colormap, the data were assigned a label (in our case 0 or 1) by a plurality vote of its neighbors.…”

Section: Ph and Color Measurementsmentioning

confidence: 99%

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Real-Time and Rapid Food Quality Monitoring Using Smart Sensory Films with Image Analysis and Machine Learning

Pounds

Bao

Luo

et al. 2022

ACS Food Sci. Technol.

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Detecting and reporting the quality of packaged food to the consumer in real-time can reduce the consumption of poor-quality food products. Current food quality detection and reporting technologies of perishable foods are usually expensive, complicated, and take a significantly long time to convey results. Herein, a real-time, simple, and user-friendly food freshness detection prototype was developed by combining a glycerol-based sensory film with unique visual color analysis and the k-nearest neighbors algorithm (KNN). We established the quantitative relationship between the pH, organic acid level, digital color variance, and food storage time. By measuring the color variations of sensor films as a function of food storage time, we demonstrate a technology to record the quantitative “RGB” values of sensory films to represent real-time and precise pH changes of the food sample and trace the real-time food spoilage degree (e.g., pork loin spoilage). Next, a quick-response (QR) reader with a center sensor film was designed to eliminate the environmental effects on the color variation in real time Furthermore, the KNN was implemented to classify food quality by training data from different sources. This study provides a technology well suited for large-scale food storage applications by combining a smart sensor film with a QR code design followed by image analysis and KNN. This real-time and rapid food quality monitoring technology will ultimately lead to a reduction in food waste and loss (FLW).

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Section: Materials and Methodsmentioning

confidence: 99%

Section: Ph and Color Measurementsmentioning

confidence: 99%

Real-Time and Rapid Food Quality Monitoring Using Smart Sensory Films with Image Analysis and Machine Learning

Pounds

Bao

Luo

et al. 2022

ACS Food Sci. Technol.

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show abstract

“…It consists of camera sensor and sensors for humidity, gas and heat which can notify the user about the food spoilage using voice-activated commands or via display and works with an accuracy of 95%. Megalingam et al (2019) presented a novel idea for detecting food spoilage using image classification with artificial intelligence, deep convolutional neural networks, computer vision and machine learning algorithms. In this system, testing and processing of images are done in a computer which will then perform image classification and machine learning algorithms for getting the colours in the image and spoilage is detected by Hue Saturation values and percentages of each colour.…”

Section: Application Of Ai In Quality Assessmentmentioning

confidence: 99%

Application of Artificial Intelligence in Horticulture: A Review

Sebastian,

Balakrishnan,

Koshy

et al. 2023

EEC

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India, with its diverse soil and climate conditions and varied agro ecological regions, provides a possibility to grow many horticultural crops, which significantly contribute to the Indian economy by enhancing farm output, generating employment, and providing raw materials to various food-processing businesses. The amount of land allotted for horticulture is minimal, but the demand for the production of horticulture crops is high. Therefore, meeting the demand with minimum resources is a bit challenging, nevertheless can be achieved by the introduction of Artificial Intelligence (AI) to the field. Application of AI in horticultural crop production by predicting adverse climate conditions, providing suitable micro-environment through remote controlled drip irrigation, and its application in providing automated irrigation, determining crop genotype, disease diagnosis, pest and weed management, maturity identification of fruits and vegetables, yield forecasting, automated harvesting using robots, reducing post-harvest losses and its role in fruit quality assessment and in processing industry is elaborated in this review.

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“…The two chemicals followed different trajectories during spoiling, according to principal component analysis (PCA). Rajesh Megalingam et al introduce a unique method for detecting food deterioration by combining picture classification with machine learning techniques and artificial intelligence (20). They have used AI, deep CNN networks, computer vision, and ML techniques such as the k clusters method for color classifications in pictures and its HSV values for spoiling detection to identify food rotting.…”

Section: Literature Reviewmentioning

confidence: 99%

An Artificial Intelligence Approach Toward Food Spoilage Detection and Analysis

Sonwani

Bansal

Alroobaea

et al. 2022

Front. Public Health

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Aiming to increase the shelf life of food, researchers are moving toward new methodologies to maintain the quality of food as food grains are susceptible to spoilage due to precipitation, humidity, temperature, and a variety of other influences. As a result, efficient food spoilage tracking schemes are required to sustain food quality levels. We have designed a prototype to track food quality and to manage storage systems at home. Initially, we have employed a Convolutional Neural Network (CNN) model to detect the type of fruit and veggies. Then the proposed system monitors the gas emission level, humidity level, and temperature of fruits and veggies by using sensors and actuators to check the food spoilage level. This would additionally control the environment and avoid food spoilage wherever possible. Additionally, the food spoilage level is informed to the customer by an alert message sent to their registered mobile numbers based on the freshness and condition of the food. The model employed proved to have an accuracy rate of 95%. Finally, the experiment is successful in increasing the shelf life of some categories of food by 2 days.

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Food Spoilage Detection Using Convolutional Neural Networks and K Means Clustering

Cited by 16 publications

References 15 publications

Real-Time and Rapid Food Quality Monitoring Using Smart Sensory Films with Image Analysis and Machine Learning

Real-Time and Rapid Food Quality Monitoring Using Smart Sensory Films with Image Analysis and Machine Learning

Application of Artificial Intelligence in Horticulture: A Review

An Artificial Intelligence Approach Toward Food Spoilage Detection and Analysis

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