Electronic Nose for Agricultural Grain Pest Detection, Identification, and Monitoring: A Review

Biotic pest attacks and infestations are major causes of stored grain losses, leading to significant food and economic losses. Conventional, manual, sampling-based pest recognition methods are labor-intensive, time-consuming, costly, require expertise, and may not even detect hidden infestations. In recent years, the electronic nose (e-nose) approach has emerged as a potential alternative for agricultural grain pest recognition and monitoring. An e-nose mimics human olfactory systems by integrating a sensor array, data acquisition, and analysis for recognizing grain pests by analyzing volatile organic compounds (VOCs) emitted by grain and pests. However, well-documented, curated, and synthesized literature on the use of e-nose technology for grain pest detection is lacking. Therefore, this systematic literature review provides a comprehensive overview of the current state-of-the-art e-nose technology for agricultural grain pest monitoring. The review examines employed sensor technology, targeted pest species type, grain medium, data processing, and pattern recognition techniques. An e-nose is a promising tool that offers a rapid, low-cost, non-destructive solution for detecting, identifying, and monitoring grain pests, including microscopic and hidden insects, with good accuracy. We identified the factors that influence the e-nose performance, which include pest species, storage duration, temperature, moisture content, and pest density. The major challenges include sensor array optimization or selection, large data processing, poor repeatability, and comparability among measurements. An inexpensive and portable e-nose has the potential to help stakeholders and storage managers take timely and data-driven informed actions or decisions to reduce overall food and economic losses.

Agricultural Object Detection with You Look Only Once (YOLO) Algorithm: A Bibliometric and Systematic Literature Review

Vision is a major component in several digital technologies and tools used in agriculture. The object detector, You Look Only Once (YOLO), has gained popularity in agriculture in a relatively short span due to its state-of-the-art performance. YOLO offers real-time detection with good accuracy and is implemented in various agricultural tasks, including monitoring, surveillance, sensing, automation, and robotics. The research and application of YOLO in agriculture are accelerating rapidly but are fragmented and multidisciplinary. Moreover, the performance characteristics (i.e., accuracy, speed, computation) of the object detector influence the rate of technology implementation and adoption in agriculture. Thus, the study aims to collect extensive literature to document and critically evaluate the advances and application of YOLO for agricultural object recognition. First, we conducted a bibliometric review of 257 articles to understand the scholarly landscape of YOLO in agricultural domain. Secondly, we conducted a systematic review of 30 articles to identify current knowledge, gaps, and modifications in YOLO for specific agricultural tasks. The study critically assesses and summarizes the information on YOLO's end-to-end learning approach, including data acquisition, processing, network modification, integration, and deployment. We also discussed task-specific YOLO algorithm modification and integration to meet the agricultural object or environment-specific challenges. In general, YOLO-integrated digital tools and technologies show the potential for real-time, automated monitoring, surveillance, and object handling to reduce labor, production cost, and environmental impact while maximizing resource efficiency. The study provides detailed documentation and significantly advances the existing knowledge on applying YOLO in agriculture, which can greatly benefit the scientific community.