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weight prediction Using the Hybrid Stacked-LSTM Food Selection Model

Computer Systems Science & Engineering 2023年第7期46卷 765-781页

作者： Ahmed M.Elshewey Mahmoud Y.Shams Zahraa Tarek Mohamed Megahed El-Sayed M.El-kenawy Mohamed A.El-dosuky Faculty of Computers and Information Computer Science DepartmentSuez UniversitySuezEgypt Faculty of Artificial Intelligence Kafrelsheikh UniversityKafrelsheikh33511Egypt Faculty of Computers and Information Computer Science DepartmentMansoura University35561Egypt Faculty of Physical Education Track and Field Competitions DepartmentArish UniversityEgypt Delta Higher Institute of Engineering and Technology Mansoura35111Egypt Department of Computer Science Arab East CollegesRiyadh13544Saudi Arabia

Food choice motives(i.e.,mood,health,natural content,convenience,sensory appeal,price,familiarities,ethical concerns,and weight control)have an important role in transforming the current food system to ensure the healthiness of people and the sustainability of the *** from several domains have presented several models addressing issues influencing food choice over the ***,a multidisciplinary approach is required to better understand how various aspects interact with one another during the decision-making *** this paper,four Deep Learning(DL)models and one Machine Learning(ML)model are utilized to predict the weight in pounds based on food *** Long Short-Term Memory(LSTM)model,stacked-LSTM model,Conventional Neural Network(CNN)model,and CNN-LSTM model are the used deep learning *** the applied ML model is the K-Nearest Neighbor(KNN)*** efficiency of the proposed model was determined based on the error rate obtained from the experimental *** findings indicated that Mean Absolute Error(MAE)is 0.0087,the Mean Square Error(MSE)is 0.00011,the Median Absolute Error(MedAE)is 0.006,the Root Mean Square Error(RMSE)is 0.011,and the Mean Absolute Percentage Error(MAPE)is ***,the results demonstrated that the stacked LSTM achieved improved results compared with the LSTM,CNN,CNN-LSTM,and KNN regressor.

关键词： weight prediction machine learning deep learning LSTM CNN KNN

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prediction of shelled shrimp weight by machine vision

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Journal of Zhejiang University-Science B(Biomedicine & Biotechnology) 2009年第8期10卷 589-594页

作者： Peng-min PAN Jian-ping LI Gu-lai LV Hui YANG Song-ming ZHU Jian-zhong LOU Department of Biosystems Engineering School of Biosystems Engineering and Food Science Zhejiang University Hangzhou 310029 China

The weight of shelled shrimp is an important parameter for grading *** weight prediction of shelled shrimp by contour area is not accurate enough because of the ignorance of the shrimp *** this paper,a multivariate prediction model containing area,perimeter,length,and width was established.A new calibration algorithm for extracting length of shelled shrimp was proposed,which contains binary image thinning,branch recognition and elimination,and length reconstruction,while its width was calculated during the process of length *** model was further validated with another set of images from 30 shelled *** a comparison purpose,artificial neural network(ANN) was used for the shrimp weight *** ANN model resulted in a better prediction accuracy(with the average relative error at 2.67%),but took a tenfold increase in calculation time compared with the weight-area-perimeter(WAP) model(with the average relative error at 3.02%).We thus conclude that the WAP model is a better method for the prediction of the weight of shelled red shrimp.

关键词： Shelled shrimp Image Feature Length extracting weight prediction weight-area-perimeter (WAP) model

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Machine Vision Based Fish Cutting Point prediction for Target weight

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Computers, Materials & Continua 2023年第4期75卷 2247-2263页

作者： Yonghun Jang Yeong-Seok Seo Department of Computer Engineering Yeungnam UniversityGyeongsan38541Korea

Food processing companies pursue the distribution of ingredientsthat were packaged according to a certain weight. Particularly, foods like fishare highly demanded and supplied. However, despite the high quantity offish to be supplied, most seafood processing companies have yet to installautomation equipment. Such absence of automation equipment for seafoodprocessing incurs a considerable cost regarding labor force, economy, andtime. Moreover, workers responsible for fish processing are exposed to risksbecause fish processing tasks require the use of dangerous tools, such aspower saws or knives. To solve these problems observed in the fish processingfield, this study proposed a fish cutting point prediction method based onAI machine vision and target weight. The proposed method performs threedimensional(3D) modeling of a fish’s form based on image processing techniquesand partitioned random sample consensus (RANSAC) and extracts 3Dfeature information. Then, it generates a neural network model for predictingfish cutting points according to the target weight by performing machinelearning of the extracted 3D feature information and measured weight *** study allows for the direct cutting of fish based on cutting pointspredicted by the proposed method. Subsequently, we compared the measuredweight of the cut pieces with the target weight. The comparison result verifiedthat the proposed method showed a mean error rate of approximately 3%.

关键词： Machine vision fish cutting weight prediction artificial intelligence deep learning image processing

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