A Novel Approach for Monitoring the Freshness of Vegetables Under Storage Using Internet of Things and Machine Learning in Varying Temperature Conditions

The degradation of fruits and vegetables is significantly influenced by temperature. It is a known fact that temperature changes can cause fruits to rot. This novel Internet of Things project uses machine learning that can forecast events in order to extend the fruit's shelf life. The study explains how weather patterns can be analyzed and forecasted using Internet of Things and a few machine learning techniques. The device measures air temperature in real-time using a microcontroller sensor. The controller, called a NodeMCU, includes a Wi-Fi communication system that allows connection to the ThingSpeak cloud platform. ThingSpeak aids in data analysis and visualization, collection, storage, and retrieval in the preferred format. The gathered data can be used as a guide to forecast temperature for an upcoming time period. Python is a helpful programming language for analysis, creativity, and innovative ways of examining data. The technology utilized to train the machine is a multilinear regression model. The project's overarching goal is to create a comprehensive package for collecting, using, and analyzing temperature data. We can monitor the temperature and humidity using the respective data sets. It will be helpful in managing moisture in vegetables to extend shelf life and in the dairy industry to maintain and monitor the purity and freshness of dairy products. A very high degree of accuracy is obtained by this model using a random forest regressor which is better than previous state of the art models.