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A Survey on Fruit Shelf-Life Prediction for Control of Starvation using CNN

Dr Ashok Kumar P.S., Mohammed Raiyan Ahmed, Mohammed Salman M R, Saba Samreen, Sabah Khanam

Abstract


The study by United Nations on the Global Hunger Index paints a clear picture of India's hunger situation. India stands at 101st position out of 116 nations. Starvation deaths and mass migration are the norms today in the country. The Indian government reported that 69% of children under the age of five in the country would die from malnutrition and hunger by the end of 2022. Reduction in wastage of food can lead to a reduction in starvation. This system aims to do the same by implementing the following. The system consists of two modules A fruit shelf-life predictor, and a Mobile/Web application that connects the donor and the NGO.  Fruit shelf life may be characterized as the period throughout which acceptable consuming quality is preserved in terms of safety, nutrition, and taste. During this time, the fruit should give consumers the intended sensory experience and nutrients. The image of the fruit is provided as the input to the model via the app, and the model returns the remaining shelf life of the given fruit. Deep learning algorithms used in CNN architecture are widely used to scrutinize, spot, and organize images in various errands. The specified neural networks contain neurons with learnable weights and biases, similar to the human neural system, and are competent to identify and organize various features in images.

 


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References


Iorliam, I. B., Ikyo, B. A., Iorliam, A., Okube, E. O., Kwaghtyo, K. D., & Shehu, Y. I. (2021). Application of Machine Learning Techniques for Okra Shelf Life Prediction. Journal of Data Analysis and Information Processing, 9(3), 136-150.

Khan, T., Qiu, J., Qureshi, M. A. A., Iqbal, M. S., Mehmood, R., & Hussain, W. (2020). Agricultural fruit prediction using deep neural networks. Procedia Computer Science, 174, 72-78.

Bhole, V., & Kumar, A. (2021). A transfer learning-based approach to predict the shelf life of fruit. Inteligencia Artificial, 24(67), 102-120.

Bhosale, A. A., & Sundaram, K. (2011). Equation for predicting shelf life of an apple. In Applied Mechanics and Materials (Vol. 52, pp. 1936-1941). Trans Tech Publications Ltd.

Albert-Weiss, D., & Osman, A. (2022). Interactive Deep Learning for Shelf Life Prediction of Muskmelons Based on an Active Learning Approach. Sensors, 22(2), 414.

Elngar, A. A., Arafa, M., Fathy, A., Moustafa, B., Mahmoudm, O., Shaban, M., & Fawzy, N. (2021). Image classification based on CNN: a survey. J. Cybersecurity Inf. Manag.(JCIM), 6(1), 18-50.

Duh, J., & Spears, D. (2017). Health and hunger: Disease, energy needs, and the Indian calorie consumption puzzle. The Economic Journal, 127(606), 2378-2409.

Chaganti, S. Y., Nanda, I., Pandi, K. R., Prudhvith, T. G., & Kumar, N. (2020, March). Image Classification using SVM and CNN. In 2020 International Conference on Computer Science, Engineering and Applications (ICCSEA) (pp. 1-5). IEEE.

Khalil, K., Eldash, O., Kumar, A., & Bayoumi, M. (2018, December). An efficient approach for neural network architecture. In 2018 25th IEEE International Conference on Electronics, Circuits and Systems (ICECS) (pp. 745-748). IEEE.

Krishna, M. M., Neelima, M., Harshali, M., & Rao, M. V. G. (2018). Image classification using deep learning. International Journal of Engineering & Technology, 7(2.7), 614-617.


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