Abstract
The purpose of this research is to explore the feasibility of applying an electronic nose for the intelligent monitoring of injurious insects in a stored grain environment. In this study, we employed an electronic nose to sample rough rice that contained three degrees of red flour beetle (Tribolium castaneum Herbst) infestation for different durations—light degree (LD), middle degree (MD), and heavy degree (HD)—and manually investigated the insect situation at the same time. Manual insect situation investigation shows that, in all three rice treatments, the insect amounts gradually decreased after infestation. When the insect population of stored rough rice was under 13 insects per 60 g of rough rice, the natural speed of decrease of the insect population became very slow and reached the best artificial insect killing period. Linear discriminant analysis (LDA) provided good performance for MD and HD insect harm duration identification, but performed poorly for LD insect harm duration identification. Both k-means clustering analysis (K-means) and fuzzy c-means analysis (FCM) effectively identified the insect harm duration for stored rough rice. The results from the back-propagation artificial neural network (BPNN) insect prevalence prediction for the three degrees of rough rice infestation demonstrated that the electronic nose could effectively predict insect prevalence in stored grain (fitting coefficients were larger than 0.89). The predictive ability was best for LD, second best for MD, and least accurate for HD. This experiment demonstrates the feasibility of electronic noses for detecting both the duration and prevalence of an insect infestation in stored grain and provides a reference for the intelligent monitoring of an insect infestation in stored grains.
Highlights
Rice is the most important crop in China
According to trends of insect variation in the different infestation treatments, we can infer that insect numbers naturally decline slowly when populations in storage grain are under 13 individuals/60 g, which is the necessary period for manual insect killing
This paper explored the feasibility of using an electronic nose for inferring infestation duration and prevalence in stored grains
Summary
Rice is the most important crop in China. Approximately 65% of Chinese people live on rice.China is the largest rice-producing country in the world, amounting to approximately 30% of the world’s total production [1]. Rice is the most important crop in China. 65% of Chinese people live on rice. China is the largest rice-producing country in the world, amounting to approximately 30% of the world’s total production [1]. Pest insects are one of the main factors that cause grain loss. Researchers have reported that [2] 5% of the total grain in the world is lost due to infestation by insects every year. Material resources, and technology cannot meet the needs of grain protection, losses can reach 20%–30% of total grain. Annual losses of grain depots in China were approximately 0.2% of total grain production. Pest insects must be accurately detected to purposely administer prophylaxis and Sensors 2017, 17, 688; doi:10.3390/s17040688 www.mdpi.com/journal/sensors
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