Nutrients Detection and Adulteration Analysis of Vegetables and Fruits for Pregnant Women Using Machine Learning

Ii. Related Work

According to M. Ayaz et.al (2019) he emphasizes how IOT-based sensors and the technologies of communication redesigned agriculture to “smart agriculture” creating a revolutionary change and providing many opportunities in every application. P. S. Arya and M. Gangwar (2021) emphasized that food production needs advancement in technology, particularly in food safety. Considering consumers' health and nutrient intake, the food should be processed and minimize food waste. Factors of Freshness are sensed using IOT sensors and output values which act as inputs for deep learning algorithms for prediction of freshness with accuracy.

According to C. C. Foong, G. K. Meng and L. L. T. (2021) improper storage of food is prone to diseases which in turn affects humans' healthy diet. Thus, a better solution than a manual inspection is to use Convolutional Neural Networks (CNNs) to extract and for classification. The harmful pesticide content level consumed by animals in (2020)D. Devi is determined using IOT sensors, microcontrollers, and using Support Vector Machine (SVM) algorithms for accurate results.The major nutrients as stated in M. Masrie et.al in 2018 are: 1) Nitrogen 2) Phosphorus 3)Potassium. It is divided into a transmission system using IOT and Arduino UNO and a detection system through light intensity measured in volts.Agriculture, is a potential field for IOT that increases profit, decreases cost, and improves the quality of food. The quality assessment is based on quality improvement and reduction of unnecessary use of fertilizers is carried out by manipulating an Arduino microcontroller. Evaluates the amounts of NPK to determine the extra amounts of N or P or K content required to increase the fertility of soil.