Application of artificial intelligence in insect pest identification - A review
The increasing danger of insect pests to agriculture and ecosystems calls for quick, and precise diagnosis. Conventional techniques that depend on human observation and taxonomic knowledge are frequently labour-intensive and time-consuming. Incorporating artificial intelligence (AI) into detection h...
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| Format: | Article |
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KeAi Communications Co., Ltd.
2026-03-01
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| Series: | Artificial Intelligence in Agriculture |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2589721725000686 |
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| author | Sourav Chakrabarty Chandan Kumar Deb Sudeep Marwaha Md. Ashraful Haque Deeba Kamil Raju Bheemanahalli Pathour Rajendra Shashank |
| author_facet | Sourav Chakrabarty Chandan Kumar Deb Sudeep Marwaha Md. Ashraful Haque Deeba Kamil Raju Bheemanahalli Pathour Rajendra Shashank |
| author_sort | Sourav Chakrabarty |
| collection | DOAJ |
| description | The increasing danger of insect pests to agriculture and ecosystems calls for quick, and precise diagnosis. Conventional techniques that depend on human observation and taxonomic knowledge are frequently labour-intensive and time-consuming. Incorporating artificial intelligence (AI) into detection has emerged as an effective approach in agriculture, including entomology. AI-based detection methods use machine learning, deep learning algorithms, and computer vision techniques to automate and improve the identification of insects. Deep learning algorithms, such as convolutional neural networks (CNNs), are primarily used for AI-powered insect pest identification by categorizing insects based on their visual features through image-based classification methodology. These methods have revolutionized insect identification by analyzing large databases of insect images and identifying distinct patterns and features linked to different species. AI-powered systems can improve insect pest identification by utilizing other data modalities. However, there are obstacles to overcome, such as the scarcity of high-quality labelled datasets and scalability and affordability issues. Despite these challenges, there is significant potential for AI-powered insect pest identification and pest management. Cooperation among researchers, practitioners, and policymakers is necessary to utilize AI in pest management fully. AI technology is transforming the field of entomology by enabling high-precision identification of insect pests, leading to more efficient and eco-friendly pest management strategies. This can enhance food safety and reduce the need for continuous insecticide spraying, ensuring the purity and safety of the food supply chains. This review updates AI-powered insect pest identification, covering its significance, methods, challenges, and prospects. |
| format | Article |
| id | doaj-art-e72629bb31e340f3bc80b8fa8c546a50 |
| institution | Kabale University |
| issn | 2589-7217 |
| language | English |
| publishDate | 2026-03-01 |
| publisher | KeAi Communications Co., Ltd. |
| record_format | Article |
| series | Artificial Intelligence in Agriculture |
| spelling | doaj-art-e72629bb31e340f3bc80b8fa8c546a502025-08-20T03:45:10ZengKeAi Communications Co., Ltd.Artificial Intelligence in Agriculture2589-72172026-03-01161446110.1016/j.aiia.2025.06.005Application of artificial intelligence in insect pest identification - A reviewSourav Chakrabarty0Chandan Kumar Deb1Sudeep Marwaha2Md. Ashraful Haque3Deeba Kamil4Raju Bheemanahalli5Pathour Rajendra Shashank6Division of Entomology, ICAR-Indian Agricultural Research Institute, New Delhi 110012, IndiaDivision of Computer Applications, ICAR-Indian Agricultural Statistics Research Institute, New Delhi 110012, IndiaDivision of Computer Applications, ICAR-Indian Agricultural Statistics Research Institute, New Delhi 110012, IndiaDivision of Computer Applications, ICAR-Indian Agricultural Statistics Research Institute, New Delhi 110012, IndiaDivision of Plant Pathology, ICAR-Indian Agricultural Research Institute, New Delhi 110012, IndiaDepartment of Plant and Soil Sciences, Mississippi State University, Mississippi State, MS, USADivision of Entomology, ICAR-Indian Agricultural Research Institute, New Delhi 110012, India; Corresponding author at: Division of Entomology, ICAR-Indian Agricultural Research Institute, New Delhi 110012, India.The increasing danger of insect pests to agriculture and ecosystems calls for quick, and precise diagnosis. Conventional techniques that depend on human observation and taxonomic knowledge are frequently labour-intensive and time-consuming. Incorporating artificial intelligence (AI) into detection has emerged as an effective approach in agriculture, including entomology. AI-based detection methods use machine learning, deep learning algorithms, and computer vision techniques to automate and improve the identification of insects. Deep learning algorithms, such as convolutional neural networks (CNNs), are primarily used for AI-powered insect pest identification by categorizing insects based on their visual features through image-based classification methodology. These methods have revolutionized insect identification by analyzing large databases of insect images and identifying distinct patterns and features linked to different species. AI-powered systems can improve insect pest identification by utilizing other data modalities. However, there are obstacles to overcome, such as the scarcity of high-quality labelled datasets and scalability and affordability issues. Despite these challenges, there is significant potential for AI-powered insect pest identification and pest management. Cooperation among researchers, practitioners, and policymakers is necessary to utilize AI in pest management fully. AI technology is transforming the field of entomology by enabling high-precision identification of insect pests, leading to more efficient and eco-friendly pest management strategies. This can enhance food safety and reduce the need for continuous insecticide spraying, ensuring the purity and safety of the food supply chains. This review updates AI-powered insect pest identification, covering its significance, methods, challenges, and prospects.http://www.sciencedirect.com/science/article/pii/S2589721725000686AgricultureArtificial intelligenceConventionalConvolutional neural networksDatasetsDeep learning |
| spellingShingle | Sourav Chakrabarty Chandan Kumar Deb Sudeep Marwaha Md. Ashraful Haque Deeba Kamil Raju Bheemanahalli Pathour Rajendra Shashank Application of artificial intelligence in insect pest identification - A review Artificial Intelligence in Agriculture Agriculture Artificial intelligence Conventional Convolutional neural networks Datasets Deep learning |
| title | Application of artificial intelligence in insect pest identification - A review |
| title_full | Application of artificial intelligence in insect pest identification - A review |
| title_fullStr | Application of artificial intelligence in insect pest identification - A review |
| title_full_unstemmed | Application of artificial intelligence in insect pest identification - A review |
| title_short | Application of artificial intelligence in insect pest identification - A review |
| title_sort | application of artificial intelligence in insect pest identification a review |
| topic | Agriculture Artificial intelligence Conventional Convolutional neural networks Datasets Deep learning |
| url | http://www.sciencedirect.com/science/article/pii/S2589721725000686 |
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