Sensors and Big Data Can Help Reduce Food Wastage


According to the latest surveys, up to 811 million people are going hungry, more than 2 billion suffer from malnutrition. On the other side, almost 14% of the world’s food which is about one-third of the total produce, either gets wasted or is lost before reaching shops and markets, states the United Nations Food and Agriculture Organization. With malnourishment and hunger worldwide, can we afford to waste food?

It is essential to learn and understand how is food being wasted? Why is there a significant difference between the quantity of food produced and the food supplied? With the help of technology, we can get these details; however, at the same time, we need specific numbers to help us cut down on food wastage. And this is where Machine Learning (ML) and big data can help reduce food wastage to achieve a Zero Hunger world.

What is the Problem?

Vegetables and fruits can be damaged before, during and after harvest and also while being stored. Some spoilage and decay are caused by fungi, viruses, bacteria, or microbial pathogens. Products that are bruised or tightly packed are more prone to infections and do not last as long.

How Can ML and Big Data Help?

Machine Learning (ML) has made excellent achievements in identifying plant and fruit diseases. One can extend these measures to monitor the quality of all types of food. Sensors can be used to measure and monitor the physical properties, chemical composition, acidity, humidity, and various characteristics of crops and provide the data to be analyzed by computer algorithms. ML then helps analyze the quality of crops or check for any infections; thus, helping in prediction.

Crop Monitoring

The properties of vegetables, fruits, and crops, such as shape, color, and size are monitored as they grow with the help of sensors. This data enables in the indentification of the crop’s growth condition and provides the necessary water supply and fertilizers. ML can then forecast the precise harvest time. Thus, this can decrease the losses at harvest.

During such unprecedented times, developing countries can use support from big data, sensors, and ML to track weather changes, crops, and preserve nature and conserve resources.

Post-Harvest Monitoring

Produced harvest in one place is generally transported to other market places. A few products are exported, while some are used in local markets. The products that need to be exported need to be kept fresh for a long time. Specifically, with the disruption in the proper supply chain, the products must be of good quality and stored properly to reduce wastage. Sensors and ML can help categorize quality and prescribe suitable storage forms.

Market Quality Monitoring

With the idea that COVID-19 virus or (any other virus) may be present on any packaging surface or on the surface of the vegetables and fruits, humans tend to handle the foods and food packaging with some amount of fear. Thus, sensors can help to monitor the product in real-time and ML can help separate hazardous products from good products. This monitoring is essential for people’s (market) quality and safety concerns.

Wrapping Up

Thus, the bottom line is that ML and technology can most certainly help us reduce food wastage. By adopting the right methods, horticulture can become the most reliable sector of the economy as well; thus, making it highly beneficial for the whole country. Data-driven automation can indeed be the solution.