Using Artificial Intelligence-Based Approach for Detecting Insects-Induced Grain Damage

Abstract

According to the Intergovernmental Panel on Climate Change, around 30% of global food production is wasted each year. Prevention strategies during post-harvest grain storage may reduce food waste and help distribute food surpluses more fairly globally, ultimately reducing global hunger. Insect damage is a major cause of post-harvest storage losses, resulting in both quality and nutritional loss to the grain. Recently, data-driven approaches have been used to enhance proper and efficient storage. The aim of this project is to investigate how artificial intelligence (AI) can be used to detect insect-induced grain damage during storage. This project used barley grain to test the experimental hypothesis that insect-related damage during grain storage can be detected by simultaneous imaging and AI-based analysis. An AI approach was developed using Python to build a prediction model. Data was collected by acquiring images of insect-induced damaged and undamaged grain. The AI-based model was highly (90%) accurate in identifying the damage caused by the insect based on the parameters defined in the algorithm. In summary, this innovative approach allowed us to identify grain damage, which, in the future, will help take necessary intervention(s) to prevent insect-induced grain damage and ultimately prevent loss during post-harvest storage.