{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,13]],"date-time":"2026-07-13T14:43:29Z","timestamp":1783953809078,"version":"3.55.0"},"reference-count":96,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2023,4,20]],"date-time":"2023-04-20T00:00:00Z","timestamp":1681948800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"USDA Non-Assistance Cooperative Agreement","award":["58-6080-9-006"],"award-info":[{"award-number":["58-6080-9-006"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Using artificial intelligence (AI) and the IoT (Internet of Things) is a primary focus of applied engineering research to improve agricultural efficiency. This review paper summarizes the engagement of artificial intelligence models and IoT techniques in detecting, classifying, and counting cotton insect pests and corresponding beneficial insects. The effectiveness and limitations of AI and IoT techniques in various cotton agricultural settings were comprehensively reviewed. This review indicates that insects can be detected with an accuracy of between 70 and 98% using camera\/microphone sensors and enhanced deep learning algorithms. However, despite the numerous pests and beneficial insects, only a few species were targeted for detection and classification by AI and IoT systems. Not surprisingly, due to the challenges of identifying immature and predatory insects, few studies have designed systems to detect and characterize them. The location of the insects, sufficient data size, concentrated insects on the image, and similarity in species appearance are major obstacles when implementing AI. Similarly, IoT is constrained by a lack of effective field distance between sensors when targeting insects according to their estimated population size. Based on this study, the number of pest species monitored by AI and IoT technologies should be increased while improving the system\u2019s detection accuracy.<\/jats:p>","DOI":"10.3390\/s23084127","type":"journal-article","created":{"date-parts":[[2023,4,20]],"date-time":"2023-04-20T05:35:46Z","timestamp":1681968946000},"page":"4127","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":46,"title":["A Review of Successes and Impeding Challenges of IoT-Based Insect Pest Detection Systems for Estimating Agroecosystem Health and Productivity of Cotton"],"prefix":"10.3390","volume":"23","author":[{"given":"Denis O.","family":"Kiobia","sequence":"first","affiliation":[{"name":"College of Engineering, University of Georgia, Tifton, GA 31793, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3426-4904","authenticated-orcid":false,"given":"Canicius J.","family":"Mwitta","sequence":"additional","affiliation":[{"name":"College of Engineering, University of Georgia, Tifton, GA 31793, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6362-3174","authenticated-orcid":false,"given":"Kadeghe G.","family":"Fue","sequence":"additional","affiliation":[{"name":"Department of Agricultural Engineering, School of Engineering Science and Technology, Sokoine University of Agriculture, Morogoro P.O. Box 3003, Tanzania"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jason M.","family":"Schmidt","sequence":"additional","affiliation":[{"name":"Department of Entomology, University of Georgia, Tifton, GA 31793, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2059-0701","authenticated-orcid":false,"given":"David G.","family":"Riley","sequence":"additional","affiliation":[{"name":"Department of Entomology, University of Georgia, Tifton, GA 31793, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2497-5422","authenticated-orcid":false,"given":"Glen C.","family":"Rains","sequence":"additional","affiliation":[{"name":"College of Engineering, University of Georgia, Tifton, GA 31793, USA"},{"name":"Department of Entomology, University of Georgia, Tifton, GA 31793, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2023,4,20]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"105488","DOI":"10.1016\/j.compag.2020.105488","article-title":"Cotton pests classification in field-based images using deep residual networks","volume":"174","author":"Alves","year":"2020","journal-title":"Comput. 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