避免食品变质程度的CNN模型的开发

None Sai Prasad Baswoju, None Y Latha, None Ravindra Changala, None Annapurna Gummadi
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摘要

食物腐败是一个普遍存在的问题,它造成了食物浪费,并在全球范围内构成了重大的经济和环境挑战。为了解决这个问题,我们提出了一种卷积神经网络(CNN)模型的发展,能够预测和防止食物变质。本文概述了我们基于cnn的解决方案的方法,数据收集,模型架构和评估,旨在帮助消费者,零售商和食品生产商最大限度地减少食物浪费。研究人员正在研究创新技术,以保持食品的质量,努力延长其保质期,因为谷物容易因降水、湿度、温度和许多其他因素而变质。为了维持现行的食品质量标准,需要有效的食品变质监测系统。为了监控食品质量和控制家庭储存系统,我们创造了一个原型。首先,我们使用卷积神经网络(CNN)模型来识别不同类型的水果和蔬菜。然后,该系统使用传感器和执行器,通过监测气体排放水平、湿度水平和水果和蔬菜的温度,来检查食物变质的程度。此外,这将调节环境,并在可行的最大程度上防止食品变质。此外,根据食物的新鲜度和状况,将一条消息发送给客户端,提醒他们食物的分解程度。所使用的模型的准确率为96.3%。
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Development of CNN Model to Avoid Food Spoiling Level
Food spoilage is a pervasive issue that contributes to food waste and poses significant economic and environmental challenges worldwide. To combat this problem, we propose the development of a Convolutional Neural Network (CNN) model capable of predicting and preventing food spoilage. This paper outlines the methodology, data collection, model architecture, and evaluation of our CNN-based solution, which aims to assist consumers, retailers, and food producers in minimizing food waste. Researchers are working on innovative techniques to preserve the quality of food in an effort to extend its shelf life since grains are prone to spoiling as a result of precipitation, humidity, temperature, and a number of other factors. In order to maintain current standards of food quality, effective surveillance systems for food deterioration are needed. To monitor food quality and control home storage systems, we have created a prototype. To start, we used a Convolutional Neural Network (CNN) model to identify the different types of fruits and vegetables. The suggested system then uses sensors and actuators to check the amount of food spoiling by monitoring the gas emission level, humidity level, and temperature of fruits and vegetables. Additionally, this would regulate the environment and, to the greatest extent feasible, prevent food spoiling. Additionally, based on the freshness and condition of the food, a message alerting the client to the food decomposition level is delivered to their registered cell numbers. The model used turned out to have a 96.3% accuracy rate.
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