The infestation of stored food grains by insect pests poses an important threat to global food security and economic stability. The loss of stored food grains may be due to insect pest infestation, microbial spoilage, or inadequate storage conditions. Among these losses, insect pest infestations cause major losses in stored food commodities. Therefore, early detection of storage pests is highly important to farmers and warehouse managers/owners. Conventional detection methods such as visual/manual inspection, grain probes and insect traps, pheromones, visual lures, and Berlese funnel methods are time-consuming and destructive methods. As conventional detection requires repeated sampling and monitoring, the insect pests can be detected only after the adult emergence. At present, non-destructive methods, viz., improved and advanced techniques such as hidden infestation detectors, NIR, X-ray imaging, uric acid analysis, microwave resonators, conductive roller mills, ELISA, acoustic detection, environmental sensing, electronic-nose methods, thermal imaging, solid-phase microextraction (SPME), and machine vision are explored for their potential in enhancing the efficiency and scalability of detection systems. The advantages and limitations of each method are critically assessed, considering factors such as accuracy, cost-effectiveness, and applicability in storage environments. The present review explores various improved and advanced detection techniques employed in the monitoring of insect pests in stored food commodities.
Keerthana et al. (Tue,) studied this question.