by Sourav Chakrabarty and Shashank P. R.
Insect pests pose significant challenges to global agriculture, causing substantial crop losses annually (Ali et al., 2023; Tiwari, 2024). Traditional pest detection and monitoring methods, though effective, are usually labour-intensive, time-consuming, laboratory-oriented and need huge expertise (Zhang et al., 2022). With the rapid advancements in entomological science, AI-driven automatic detection of insect pests is revolutionizing the identification and management of these agricultural threats, offering unprecedented accuracy, efficiency, and scalability for sustainable pest control solutions (Fig 1.).
The primary motivation for integrating AI into insect pest detection initiates from the pressing need for sustainable and precise crop management. Conventional pest control strategies often rely on broad-spectrum insecticides, which can harm beneficial insects as well as the environment. Early detection of pests through automated systems can therefore enable targeted interventions, reducing the overuse of pesticides and supporting eco-friendly farming practices. AI-driven pest detection offers the ability to quickly and accurately identify insect species, developmental stages, and damage symptoms using the real-time field images (Fig 2.). This allows the farmers and agricultural professionals to implement timely measures, preventing the rapid spread of pest infestation and minimizing crop losses.
Application of AI in this regard signifies the transition from controlled laboratory settings to dynamic field situations, which is also a very critical step. Field conditions introduce challenges like varying light intensities, complex backgrounds, and overlapping objects (or insects) (Chakrabarty et al., 2024). Despite these obstacles, AI-powered models have the abilities to show robust performances, highlighting the practical utilities for farmers and agricultural stakeholders. Real-time identification of pests and damage symptoms significantly reduces the time lag between detection and intervention. This precision enables targeted pest control, supporting reduced pesticide use and promoting ecological balance. Furthermore, AI-based solutions are scalable, with potential applications across various crops and pest species.
Though AI is not a new topic, the integration of it in agriculture is just at the beginning. The advancements could include new mobile applications providing user-friendly interfaces for farmers to upload field images and receive instant pest diagnostics. AI-powered drones and sensors could enable continuous pest monitoring and data collection, while predictive analytics models could forecast pest outbreaks based on environmental and historical data. These innovations would make pest management more proactive and efficient, helping to safeguard crops against losses while ensuring environmental sustainability. The widespread adoption of such technologies could play a pivotal role in meeting the growing global food demand. Therefore, it has the potential to transform pest management from a reactive to a proactive practice. By bridging the gap between lab-based innovations and field-level applications, AI empowers farmers with the tools they need for smarter, and more sustainable agricultural systems. As the technology continues to evolve, it promises to create a future where crop protection is not only efficient but also environmentally responsible.
Let’s take at leap from lab to field and harness the power of AI for a brighter agricultural future!
For more details check the following
Ali, M. A., Abdellah, I. M., & Eletmany, M. R. (2023). Towards sustainable management of insect pests: Protecting food security through Ecological Intensification. International Journal of Chemical and Biochemical Sciences, 24(4), 386-394.
Chakrabarty, S., Shashank, P. R., Deb, C. K., Haque, M. A., Thakur, P., Kamil, D., Marwaha, S. & Dhillon, M. K. (2024). Deep Learning-Based Accurate Detection of Insects and Damage in Cruciferous Crops Using YOLOv5. Smart Agricultural Technology, 100663.
Tiwari, A. K. (2024). Insect Pests in Agriculture Identifying and Overcoming Challenges through IPM. Archives of Current Research International, 24(3), 124-130.
Zhang, W., Huang, H., Sun, Y., & Wu, X. (2022). AgriPest-YOLO: A rapid light-trap agricultural pest detection method based on deep learning. Frontiers in Plant Science, 13, 1079384.
Sourav Chakrabarty is a M.Sc. Student at the Division of Entomology, ICAR- Indian Agricultural Research Institute, New Delhi. He is working AI-based real-time insect detection in crucifer fields.
Email: tublu0002@gmail.com
Shashank P. R. is a Senior Scientist (Entomology) at the Division of Entomology, ICAR- Indian Agricultural Research Institute, New Delhi. His field of specialization is insect taxonomy and invasive pests. He is the Founding Managing Editor of Indian Entomologist.
Email: spathour@gmail.com
Disclaimer: The contents, style, language, plagiarism, references, mention of any products if any, etc., are the sole responsibility of the authors.
Very much informative and need-based topic.Hope it will be very much helpful for the researchers working in this burning topic.