Systematic Review of AI-Driven Insect Identification and Management Strategies in Modern Farming

Abstract

The rapid advancement of artificial intelligence (AI) has transformed modern farming, particularly in insect identification and management, where precision and efficiency are critical. This systematic review examines the role of AI-driven technologies in addressing pest-related challenges while improving agricultural productivity and sustainability. We analyze the integration of AI across four key dimensions: pest management, crop management, and yield improvement. Sustainable agriculture and agricultural technology adoption are discussed to assess existing research, identify trends, methodologies, and gaps in AI applications for insect detection, classification, and intervention strategies. A rigorous selection process was employed to gather relevant studies, which were evaluated based on their technical approaches, performance metrics, and practical implications. Findings reveal that machine learning and computer vision technologies dominate the field, enabling real-time insect monitoring and targeted pest control with reduced chemical usage. However, challenges such as data scarcity, model generalizability, and scalability in diverse farming environments persist. The review highlights the potential of AI to enhance decision-making in pest management while aligning with sustainable agricultural goals, though further interdisciplinary collaboration and field validation are needed to bridge the gap between research and implementation. This work provides a comprehensive foundation to guide future research and policy development in AI-driven agricultural innovation.

Introduction

The text presents a comprehensive review of AI-based insect identification and pest management in agriculture, highlighting its importance in addressing growing global food demand, climate variability, and significant crop losses caused by insect pests. Traditional reliance on chemical pesticides poses environmental, health, and resistance risks, motivating a shift toward precision agriculture, where artificial intelligence enables early pest detection, monitoring, and targeted intervention.

The review synthesizes research from 2014–2024 using PRISMA guidelines, analyzing studies across multiple databases. It examines AI’s role through four dimensions: insect detection and monitoring, crop management and yield optimization, sustainable agriculture, and integration with agricultural technologies. Results show a sharp rise in publications during 2024–2025, indicating that AI-driven pest management is an emerging but rapidly evolving field.

Deep learning—especially CNNs—dominates insect detection research, achieving over 95% accuracy in controlled environments, though performance drops in real field conditions due to environmental variability. Vision Transformers demonstrate better cross-environment generalization but face computational constraints. AI systems integrated with IoT sensors, drones, smart traps, and robotics enable real-time monitoring, predictive pest forecasting, and precision spraying, reducing pesticide use by 30–50% while improving efficiency.

Beyond pest detection, AI contributes to yield prediction, irrigation optimization, nutrient management, and resource efficiency, often using LSTM networks, computer vision, and federated learning. Sustainability-focused applications show benefits in reducing chemical use, conserving water and energy, preserving biodiversity, and lowering carbon emissions. However, sustainability remains underrepresented in the literature, and lifecycle environmental impacts of AI systems are rarely assessed.

Key challenges include data scarcity for rare or regional pest species, limited field-based validation, lack of standardized evaluation metrics, integration barriers with existing farm infrastructure, and gaps between pest detection and actionable management recommendations. The review concludes that while AI has strong potential to transform pest management from reactive to predictive and preventive, future progress depends on improving data quality, model robustness, interpretability, interoperability, and farmer trust.

Overall, the study provides a roadmap for researchers and policymakers to develop scalable, equitable, and environmentally responsible AI-driven pest management systems, positioning AI as a critical enabler of resilient and sustainable agriculture.

Conclusion

This systematic review has examined the evolving role of AI in insect identification and management strategies, highlighting the transformative potential and persistent challenges of these technologies in modern agriculture. The synthesis of findings indicates that machine learning and computer vision techniques have reached a level of maturity sufficient for practical deployment, particularly in controlled environments. However, the transition to heterogeneous field conditions remains a significant barrier, with performance gaps underscoring the need for more robust, adaptive systems. The findings collectively advance our understanding of how AI can bridge the gap between precision pest control and sustainable farming practices, though critical limitations in scalability and ecological impact assessment persist. The practical implications of this research extend to both policy and farm-level decision-making. Demonstrated reductions in pesticide use through AI-driven precision spraying present tangible pathways for regulatory bodies to incentivize technology adoption while meeting environmental protection goals. A structured framework for data-driven pest management, alongside the integration of pest monitoring data with predictive models, offers a foundation for more informed, adaptive agricultural decision-making. Nevertheless, the uneven geographic distribution of research outputs calls for targeted investments in AI solutions tailored to smallholder and resource-limited farming systems, where the need for sustainable pest management is most acute.

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Copyright

Copyright © 2026 Madan Mohan Mishra, Amit Paliwal . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.