The grounds have shifted the foundations of academic core facilities and the current climate demands their strategic agility in order to thrive. Boyd Butler at Molecular Devices reveals how these labs can capitalise on this opportunity to increase value and efficiency. Academic core laboratories are at an interesting inflection point. Once considered subsidised institutional necessities
AI-driven hybrid framework for enhanced pest detection and resource optimization using graph networks and deep reinforcement learning – Scientific Reports
References
-
Wu, Y., Chen, L., Yang, N. & Zongbao, S. Research progress of deep learning-based artificial intelligence technology in pest and disease detection and control. Agriculture 15 (19), 2077 (2025).
Google Scholar
-
Ugwu, O. P. C. et al. Implementing artificial intelligence and machine learning algorithms for optimized crop management: a systematic review on data-driven approach to enhancing resource use and agricultural sustainability. Cogent Food Agric. 11 (1), 2569982 (2025).
Google Scholar
-
Lv, J. et al. SAN-GAT-RL: A Multimodal drone and AI-driven framework for enhanced wheat pest and disease detection in dynamic agricultural environments. In: 2025 5th International Conference on Artificial Intelligence, Big Data and Algorithms (CAIBDA) (67–71). (IEEE. 2025)
-
Ray, R. K., Chakravarty, S., Dash, S., Mohanty, S. N. & Naga Ramesh, J. V. An interactive AI-based crop and pest management system leveraging transfer learning for enhanced sustainable agriculture. Model. Earth Syst. Environ. 11 (4), 244 (2025).
Google Scholar
-
Ajaharuddin, S. K. M. D. et al. Harnessing AI and Machine Learning for Effective Pest and Disease Control. In Ecologically Mediated Development: Promoting Biodiversity Conservation and Food Security 461–485 (Springer Nature Singapore, 2025).
Google Scholar
-
Sharada, K. et al. GeoagriGuard: AI-driven pest and disease management with remote sensing for Global Food Security. Remote Sensing in Earth Systems Sciences 8(2), 409–422 (2025).
Google Scholar
-
Aziz, D. et al. Remote sensing and artificial intelligence: Revolutionizing pest management in agriculture. Front. Sustain. Food Syst. 9, 1551460 (2025).
Google Scholar
-
Meshram, R. A. & Alvi, A. S. Design of an iterative method for crop disease analysis incorporating graph attention with spatial-temporal learning and Deep Q-networks. International J. Intell. Eng. & Systems, 17(3). (2024).
-
Ajith, S., Vijayakumar, S. & Elakkiya, N. Yield prediction, pest and disease diagnosis, soil fertility mapping, precision irrigation scheduling, and food quality assessment using machine learning and deep learning algorithms. Discover Food. 5 (1), 1–23 (2025).
Google Scholar
-
Kalusivalingam, A. K., Sharma, A., Patel, N. & Singh, V. Optimizing resource allocation with reinforcement learning and genetic algorithms: An AI-driven approach. International J. AI ML, 1(2). (2020).
-
Tariq, M. U. et al. Edge-enabled smart agriculture framework: Integrating IOT, lightweight deep learning, and agentic AI for context-aware farming. Results Eng. https://doi.org/10.1016/j.rineng.2025.107342 (2025).
Google Scholar
-
Patel, K., Matniyozova, M. & Tukhtaeva, N. Smart Citrus Farming: Deep Learning and Swarm Optimization for Leaf Disease Diagnosis. In: 2025 International Conference on Innovations in Intelligent Systems: Advancements in Computing, Communication, and Cybersecurity (ISAC3) (1–6). (IEEE, 2025)
-
Hessane, A. et al. Artificial intelligence-driven prediction system for efficient management of Parlatoria blanchardi in date palms. Multimed. Tools Appl. 84(15), 15293–15329 (2025).
Google Scholar
-
Yang, H., Jin, Y., Jiang, L., Lu, J. & Wen, G. AI roles in 4R crop pest management—A review. Agronomy 15(7), 1629 (2025).
Google Scholar
-
Batistatos, M. C. et al. AGRARIAN: A hybrid AI-driven architecture for smart agriculture. Agriculture 15(8), 904 (2025).
Google Scholar
-
Kumar, S., Shruthi, G., Sushma, K., Ramakrishna, K. & Naveen, G. AI-Driven crop infection detection and smart herbicide recommendation system. Journal Comput. Anal. & Applications. 34 (4). (2025).
-
Begum, A., Senthil, K. & David, S. A. Harnessing AI for advanced crop management and decision support in agriculture. In AIoT and Smart Sensing (69–92). CRC. (2025).
-
Wu, K. A comprehensive review of AI methods in agri-food engineering: Applications, challenges, and future directions. Electronics 14(20), 3994 (2025).
Google Scholar
-
Prashanth, J. S., Krishna, G. B., Prasad, A. V. & Rao, P. R. March). Smart farming revolution: a cutting-edge review of deep learning and IoT innovations in agriculture. In: operations research forum (6, 1, 1–39). (Springer International Publishing, 2025).
-
Vidya Madhuri, E. et al. Transforming pest management with artificial intelligence technologies: The future of crop protection. J. Crop Health. 77 (2), 48 . (2025)
Google Scholar
-
Gokeda, V. & Yalavarthi, R. Deep hybrid model for pest detection: IoT-UAV‐based smart agriculture system. J. Phytopathol. 172(5), e13381 (2024).
Google Scholar
-
Ali, Z., Muhammad, A., Lee, N., Waqar, M. & Lee, S. W. Artificial intelligence for sustainable agriculture: A comprehensive review of AI-driven technologies in crop production. Sustainability 17(5), 2281 (2025).
Google Scholar
-
Raman, R. K. et al. Reconnoitering precision agriculture and resource management: a comprehensive review from an extension standpoint on artificial intelligence and machine learning. Indian Res. J. Ext. Educ. 24 (1), 108–123 . (2024)
Google Scholar
-
Adinarayana, S. et al. Enhancing resource management in precision farming through AI-based irrigation optimization. How Mach. Learn. is Innovating Today’s World: Concise Tech. Guide, 221–251. (2024).
-
Somala, J., Aggunna, B., Gajula, H. N. S., Jadam, H. & Marisetti, V. Hybrid AI chatbot for crop yield optimization and disease prevention using deep learning techniques. In: the International Conference on Innovations and Advances in Cognitive Systems (351–363). Cham: Springer Nature Switzerland. (2025).
-
Bonthu, S. et al. Unified model for crop optimization: leveraging deep learning and XGBoost for optimized crop management. In: the 2025 International Conference on Electronics, Computing, Communication and Control Technology (ICECCC) (1–6). (IEEE. 2025).
-
Gopi, R., Tamil Selvi, M. & Saranraj, G. Automated machine learning classification framework to predict crop yield and detect pest patterns. Int. J. Exp. Res. Rev. 46, 177–190 (2024).
Google Scholar
-
Prabha, D., Subramanian, R. S., Dinesh, M. G. & Girija, P. Sustainable farming through AI-enabled precision agriculture. In Artificial intelligence for precision agriculture (159–182). Auerbach. (2024).
-
Mathivanan, S. K., Rajadurai, H., Cho, J. & Easwaramoorthy, S. V. A multi-modal geospatial–temporal LSTM based deep learning framework for predictive modeling of urban mobility patterns. Sci. Rep. 14(1), 31579 (2024).
Google Scholar
Download references
