Author(s):
1. Israr Hussain:
Department of Computer Science, MNS University of Agriculture, Multan, Pakistan
2. Javeria Jabeen:
Department of Computer Science, MNS University of Agriculture, Multan, Pakistan
3. Aneeqa Shafique:
Department of Computer Science, MNS University of Agriculture, Multan, Pakistan
Abstract:
One of the biggest concerns about food security is the lack of provided facilities. This issue is estimated to affect around 1.3 billion tons of food each year. Food waste is a major contribute to global food production loss. It may also have an impact on the energy, water, and land used in food production. One of the most effective ways to prevent food spoilage is by solar drying. This process can also help combat hunger and global warming. This method of food preservation can helpful in reducing the development of molds and dangerous bacteria. This study aims to forecast fruit and vegetable drying rate using machine learning. This research examines the time period of different vegetables require to dry properly in various environments. The use of machine learning (ML)-based algorithms in the food drying process is fascinating and cutting-edge strategy for developing drying technologies. I will be using various samples of fruits and vegetables to dry in a solar dryer while measuring the moisture ratio (MR), temperature (°C), R.H and drying rate. The next step is dataset analysis and data preprocessing for prepare it for data prediction machine learning model. The ML trained model will be use for the estimation of drying time according to air conditions for different agricultural commodities.
Page(s):
80-80
DOI:
DOI not available
Published:
Journal: Abstract Book on International Conference on Food and Applied Sciences (ICFAS-23) 3-5 August 23, Volume: 0, Issue: 0, Year: 2023
Keywords:
fruit
,
machine learning
,
drying rate
,
Drying
,
solar drying
,
global food production
,
food preservation
References:
References are not available for this document.
Citations
Citations are not available for this document.