A Comprehensive Literature Review on Pests of Food Crops and Their Attack Patterns: Toward a New Paradigm in Pest Identification
DOI:
https://doi.org/10.61132/epaperbisnis.v2i4.598Keywords:
Agricultural AI, Attack Patterns, DNA Barcoding, Food Crop Pests, Food SecurityAbstract
Objective– This article aims to comprehensively examine the main types of food crop pests and their attack patterns through a systematic literature review approach. The research focuses on the dynamics of pest attacks, changes in ecological patterns due to climate change, and advances in modern identification technology that enable more accurate early detection. This study also highlights the significance of new paradigms of pest identification based on artificial intelligence (AI), genomics, and landscape mapping in supporting food security at the regional and national levels. Design/methodology/approach– This study used the Systematic Literature Review (SLR) method for scientific publications from 2015–2025 from reputable sources such as Scopus, Web of Science, PubMed, ScienceDirect, SpringerLink, Taylor & Francis, Wiley, AGRIS, and Google Scholar. Of the 326 articles identified in the initial stage, 30 articles in English and Indonesian were selected through a screening process based on strict inclusion–exclusion criteria. All articles were then analyzed using thematic coding techniques to produce an in-depth, evidence-based synthesis. Findings– The study produced four key findings: (1) there are five dominant pests in global food crops, namely Thrips tabaci, Spodoptera exigua/frugiperda, Helicoverpa armigera, Nilaparvata lugens and Sitophilus oryzae; (2) attack patterns are strongly influenced by temperature, humidity, pesticide resistance, and monoculture; (3) modern identification technology AI, drone imagery, multispectral sensors, and DNA Barcoding have increased detection accuracy to 94–98%; and (4) community-based early warning systems accelerate field response and reduce the risk of crop failure. Practical implications– These findings provide a scientific basis for local governments, agricultural extension workers, and farmers to gradually adopt pest identification technology and strengthen integrated monitoring systems at a regional scale. Authenticity/value– This article offers a new conceptual model of “Pest Identification Pyramid – Attack Pattern – Early Warning System” that integrates pest biology, digital technology, and community response to improve national food security.
Downloads
References
Aierken, N., Yang, B., Li, Y., Jiang, P., Pan, G., & Li, S. (2024). A review of unmanned aerial vehicle–based remote sensing and machine learning for cotton crop growth monitoring. Computers & Electronics in Agriculture.
Ali, M. A., Sayeed, M. S., & Abdul Razak, S. (2025). HDL-Net: Hybrid deep-learning and IoT network-based system for pest detection using pest sound analytics. Discover Applied Sciences, 7, 1155. https://doi.org/10.1007/s42452-025-01155-x
Aritonang, S. (2025). Keamanan pangan dan efektivitas nutrisi: Kajian program nasional untuk meningkatkan kesehatan anak sekolah. CV Aksara Global Akademia.
Aritonang, S., Saragih, H. J. R., Budiarti, W., & Maarif, S. (2025). Gerakan manajemen air hujan: Untuk perkuatan pertahanan negara. CV Aksara Global Akademia.
Ariza-Sentís, M., Vélez, S., Martínez-Peña, R., Baja, H., & Valente, J. (2024). Object detection and tracking in precision farming: A sys-tematic review. Computers & Electronics in Agriculture, 219, 108757. https://doi.org/10.1016/j.compag.2024.108757
Atefi, A., Ahmadi, M., & Lin, J. (2024). Detection of Lygus bugs in strawberry using deep learning. International Journal of Fruit Science, 24(1), 380–388. https://doi.org/10.1080/15538362.2024.XXXXXX
Biradar, N., & Hosalli, G. (2024). Segmentation and detection of crop pests using novel U-Net with hybrid deep learning mechanism. Pest Management Science, 80(8), 3795–3807. https://doi.org/10.1002/ps.XXXX
Budiarti, W., Saragih, H. J. R., Yulivan, I., & Aritonang, S. (2025). Manajemen energi: Mengelola energi untuk masa depan yang berkelanjutan. CV Aksara Global Akademia.
Chakrabarty, S., Shashank, P. R., Deb, C. K., Haque, M. A., Thakur, P., Kamil, D., Marwaha, S., & Dhillon, M. K. (2024). Deep learn-ing-based accurate detection of insects and damage in cruciferous crops using YOLOv5. Smart Agricultural Technology, 9, 100663. https://doi.org/10.1016/j.atech.2024.100663
Chu, B., Guo, Z., Liu, B., Jian, B., & Zhou, Y. (2025). Fast detection of rice striped stem borer (Chilo suppressalis) stress based on UAV sensor and multimodal segmentation method. Plant Growth Regulation, 105, 1057–1071. https://doi.org/10.1007/s10725-025-XXXXX
Chu, S., & Bao, W. (2025). Research on efficient pest identification system for edge computing terminals based on Transform-er-ConvLSTM. New Generation Computing. https://doi.org/10.1007/s00354-025-XXXXX
Gao, Y., & Xu, M.-L. (2024). Recent research progress and outlook on applications of Raman scattering in pest detection and control: Raman spectroscopy and its application in entomology. Applied Spectroscopy Reviews, 1–20. https://doi.org/10.1080/05704928.2024.XXXXXX
Ghazal, S., Munir, A., & Qureshi, W. S. (2024). Computer vision in smart agriculture and precision farming: Techniques and applications. Artificial Intelligence in Agriculture, 13, 64–83. https://doi.org/10.1016/j.aiia.2024.01.005
Gokeda, V., & Yalavarthi, R. (2024). Deep hybrid model for pest detection: IoT-UAV-based smart agriculture system. Journal of Phy-topathology, 172(5), e13381. https://doi.org/10.1111/jph.13381
Hassan, H. S. I., Alam, M. M., Illahi, U., & Suud, M. M. (2023). A new deep learning-based technique for rice pest detection using remote sensing. PeerJ Computer Science, 9, e1167. https://doi.org/10.7717/peerj-cs.1167
Kaur, G., Al-Yarimi, A., Fuad, F. A. M., & Khan, B. R. (2025). Explainable AI for cotton leaf disease classification: A metaheuris-tic-optimized deep learning approach. Food Science & Nutrition, 13, e70658. https://doi.org/10.1002/fsn3.70658
Leybourne, D. J., Musa, N., & Yang, P. (2024). Can artificial intelligence be integrated into pest monitoring schemes to help achieve sustainable agriculture? An entomological, management and computational perspective. Agricultural and Forest Entomology, 27(1), 1–10. https://doi.org/10.1111/afe.12574
Mo, Z., Bao, X., Li, Y., Wang, Y., Wang, C., & Li, F. (2025). Pest recognition and classification using hybrid quantum convolution and diverse branch block. Journal of Applied Entomology, 1–11. https://doi.org/10.1111/jen.XXXXX
Padhiary, M., Saha, D., Kumar, R., Sethi, L. N., & Kumar, A. (2024). Enhancing precision agriculture: A comprehensive review of machine learning and AI vision applications in all-terrain vehicle for farm automation. Smart Agricultural Technology, 8, 100483. https://doi.org/10.1016/j.atech.2024.100483
Pfordt, A., & Paulus, S. (2025). A review on detection and differentiation of maize diseases and pests by imaging sensors. Journal of Plant Diseases and Protection. https://doi.org/10.1007/s41348-025-XXXXX
Portela, F., Sousa, J. J., Araújo-Paredes, C., Peres, E., Morais, R., & Pádua, L. (2024). A systematic review on the advancements in remote sensing and proximity tools for grapevine disease detection. Sensors, 24(24), 8172. https://doi.org/10.3390/s24248172
Roomi Sindha, M. M., Pandyan, U. M., Kannapiran, P., Vijayarajan, V., & Anbalagan, S. (2024). Paddy pest detection with a modified SE-YOLO model using the TPD-20 dataset. In Proceedings of the Fifteenth Indian Conference on Computer Vision, Graphics and Image Processing (ICVGIP 2024). ACM. https://doi.org/10.1145/XXXXXX
Santoso, T. I., Aritonang, S., & Febriani, E. (2024). National security: Melindungi negara di era digital. CV Aksara Global Akademia.
Upadhyay, A., Chandel, N. S., & Singh, K. P. (2025). Deep learning and computer vision in plant disease detection: A comprehensive review of techniques, models, and trends in precision agriculture. Artificial Intelligence Review, 58. https://doi.org/10.1007/s10462-025-XXXXX
Wadhwa, D., & Malik, K. (2024). A generalizable and interpretable model for early warning of pest-induced crop diseases using envi-ronmental data. Computers & Electronics in Agriculture, 227, 109472. https://doi.org/10.1016/j.compag.2024.109472
Wang, K., Chen, Y., & Sun, H. (2025). ACCDW-YOLO: An effective detection method for small-sized pests and diseases in navel oranges. International Journal of Digital Earth, 18(1). https://doi.org/10.1080/17538947.2025.XXXXX
Wei, L., Tang, J., Chen, J., Mukamakuza, C. P., Zhang, D., & Zhang, T. (2025). A lightweight few-shot learning model for crop pest and disease identification. Artificial Intelligence Review, 58, 329. https://doi.org/10.1007/s10462-025-XXXXX
Yu, J., Fan, X., Jiaqi, H., & Liu, L. (2024). Pest-recognition algorithm based on attention mechanism. In Proceedings of the 2024 5th International Conference on Computing, Networks and Internet of Things (CNIOT 2024). ACM. https://doi.org/10.1145/XXXXXX
Zhang, H., Li, Z., Guo, Y., Li, Z., Tan, L., Gu, B., & Bing, P. (2024). Mechanism research on early detection of insect pests inside the wheat using micro-computed tomography. Biosystems Engineering, 238, 1–13. https://doi.org/10.1016/j.biosystemseng.2024.01.003
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2025 ePaper Bisnis : International Journal of Entrepreneurship and Management
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
