Empowering Farmers through Smart Pest Management: A Field-Based Study on AI-IoT System Adoption in Pendurthi Mandal, Andhra Pradesh, India

James Stephen Meka, Venkateswarlu Ponnam

Abstract

The income stability and agricultural productivity of small and marginal farmers in developing countries are affected by pest infestations. Severe crop losses in India are due to increased pesticide use, limited pest-detection technologies, and restricted access to real-time advisory services. Emerging technologies such as the Internet of Things (IoT), Artificial Intelligence (AI), and Large Language Models (LLMs) offer significant opportunities to develop adaptive, farmer-centric pest management systems. This study is based on a two-component mixed method approach: (1) A large scale field study of 1000 farmers in five villages in Pendurthi Mandal, Visakhapatnam District, Andhra Pradesh, India, to assess the practice of pest control, the economic burden of pests, technology awareness and readiness for adoption of technology; and (2) A simultaneous large scale field test of a low cost AI-IoT device that includes an ESP32-CAM controller, a YOLOv8 deep learning algorithm, and a vernacular Telugu language LLM advisory engine - a new development in vernacular LLM integration tested in the field at a large scale. The survey results revealed 84% pest infestation, heavy reliance on chemical pesticides (66%), growing smartphone penetration (63%), and strong willingness to adopt (76%) when supported by government subsidies and localized AI interfaces. The field testing results verified 94% system uptime and high confidence levels of 0.87-0.94 for pest detection across four major rice pest species. This study combines findings from a survey with a concurrent field trial, confirming the efficacy of affordable pest detection and promoting sustainable agricultural practices.

Keywords

artificial intelligence; IoT pest control; LLM in farming; smart agriculture; sustainable pest management

Full Text:

PDF

References

Baart, A., Bon, A., de Boer, V., Dittoh, F., Tuijp, W., & Akkermans, H. (2019). Affordable Voice Services to Bridge the Digital Divide: Presenting the Kasadaka Platform. Lecture Notes in Business Information Processing, 372 LNBIP. https://doi.org/10.1007/978-3-030-35330-8_10

Behera, U. K., & France, J. (2016). Integrated Farming Systems and the Livelihood Security of Small and Marginal Farmers in India and Other Developing Countries. In Advances in Agronomy (Vol.138). https://doi.org/10.1016/bs.agron.2016.04.001

Bhuvaneshwar D. Patil. (2024). IOT- Based Smart Plant Protection and Pest Control by Using Raspberry Pi. Journal of Electrical Systems, 20(2s). https://doi.org/10.52783/jes.1751

Bottrell, D. G., & Schoenly, K. G. (2018). Integrated pest management for resource-limited farmers: Challenges for achieving ecological, social and economic sustainability. Journal of Agricultural Science, 156(3). https://doi.org/10.1017/S0021859618000473

Fabregas, R., Kremer, M., & Schilbach, F. (2019). Realizing the potential of digital development: The case of agricultural advice. In Science (Vol. 366, Number 6471). https://doi.org/10.1126/science.aay3038

Hebsale Mallappa, V. K., & Pathak, T. B. (2023). Climate smart agriculture technologies adoption among small-scale farmers: a case study from Gujarat, India. Frontiers in Sustainable Food Systems, 7. https://doi.org/10.3389/fsufs.2023.1202485

Kakade, K. J., More, V. A., Shinde, M., Suryawanshi, K., & Shinde, G. U. (2025). Design of Precision Agriculture System using Automating Pink Bollworm Detection in Cotton Crops: AI based Digital Approach for Sustainable Pest Management. 2025 1st International Conference on AIML-Applications for Engineering and Technology, ICAET 2025. https://doi.org/10.1109/ICAET63349.2025.10932187

Kamilaris, A., & Prenafeta-Boldú, F. X. (2018). Deep learning in agriculture: A survey. In Computers and Electronics in Agriculture (Vol.147). https://doi.org/10.1016/j.compag.2018.02.016

Liakos, K. G., Busato, P., Moshou, D., Pearson, S., & Bochtis, D. (2018). Machine learning in agriculture: A review. In Sensors (Switzerland) (Vol. 18, Number 8). https://doi.org/10.3390/s18082674

Makate, C. (2019). Effective scaling of climate smart agriculture innovations in African smallholder agriculture: A review of approaches, policy and institutional strategy needs. In Environmental Science and Policy (Vol. 96).

https://doi.org/10.1016/j.envsci.2019.01.014

Marinchenko, T. E. (2021). Digital Technology in Agricultural Sector. IOP Conference Series: Earth and Environmental Science, 666(3). https://doi.org/10.1088/1755-1315/666/3/032024

Mittal, S. (2020). The role of mobile phones in empowering women in agriculture. In Routledge Handbook of Gender and Agriculture. https://doi.org/10.4324/9780429199752-15

Oerke, E. C. (2006). Crop losses to pests. In Journal of Agricultural Science (Vol. 144, Number 1). https://doi.org/10.1017/S0021859605005708

Pimentel, D. (2005). Environmental and economic costs of the application of pesticides primarily in the United States. In Environment, Development and Sustainability (Vol. 7, Number 2). https://doi.org/10.1007/s10668-005-7314-2

Pretty, J. (2008). Agricultural sustainability: Concepts, principles and evidence. In Philosophical Transactions of the Royal Society B: Biological Sciences (Vol. 363, Number 1491). https://doi.org/10.1098/rstb.2007.2163

Qazi, S., Khawaja, B. A., & Farooq, Q. U. (2022). IoT-Equipped and AI-Enabled Next Generation Smart Agriculture: A Critical Review, Current Challenges and Future Trends. In IEEE Access (Vol. 10). https://doi.org/10.1109/ACCESS.2022.3152544

Rogers, E. M., Singhal, A., & Quinlan, M. M. (2014). Diffusion of innovations. In An integrated approach to communication theory and research. Routledge.

Sati, V. P. (2024). The Future of Food and Agriculture in India: Trends and Challenges. SAYAM, 1(1). https://doi.org/10.63419/sayam.v1i1.48

Sekabira, H., Tepa-Yotto, G. T., Djouaka, R., Clottey, V., Gaitu, C., Tamò, M., Kaweesa, Y., & Ddungu, S. P. (2022). Determinants for Deployment of Climate-Smart Integrated Pest Management Practices: A Meta-Analysis Approach. Agriculture (Switzerland), 12(7). https://doi.org/10.3390/agriculture12071052

Senoo, E. E. K., Anggraini, L., Kumi, J. A., Karolina, L. B., Akansah, E., Sulyman, H. A., Mendonça, I., & Aritsugi, M. (2024). IoT Solutions with Artificial Intelligence Technologies for Precision Agriculture: Definitions, Applications, Challenges, and Opportunities. In Electronics (Switzerland) (Vol. 13, Number 10). https://doi.org/10.3390/electronics13101894

Singh, R. K., Singh, A., Kumar, S., Sheoran, P., Sharma, D. K., Stringer, L. C., Quinn, C. H., Kumar, A., & Singh, D. (2020). Perceived Climate Variability and Compounding Stressors: Implications for Risks to Livelihoods of Smallholder Indian Farmers. Environmental Management,66(5). https://doi.org/10.1007/s00267-020-01345-x

Tebaldi, R., & Bilo, C. (2019). Gender and social protection in South Asia: an assessment of the design of non-contributory programmes. International Policy Centre for Inclusive Growth and UNICEF Regional Office South Asia.

Vasavi, S., Anandaraja, N., Murugan, P. P., Latha, M. R., & Pangayar Selvi, R. (2025). Challenges and strategies of resource poor farmers in adoption of innovative farming technologies: A comprehensive review. In Agricultural Systems (Vol. 227). https://doi.org/10.1016/j.agsy.2025.104355

Wang, Z., Qiao, X., Wang, Y., Yu, H., & Mu, C. (2024). IoT-based system of prevention and control for crop diseases and insect pests. Frontiers in Plant Science, 15. https://doi.org/10.3389/fpls.2024.1323074

Wolfert, S., Ge, L., Verdouw, C., & Bogaardt, M. J. (2017). Big Data in Smart Farming – A review. In Agricultural Systems (Vol. 153). https://doi.org/10.1016/j.agsy.2017.01.023

Wu, Y., Chen, L., Yang, N., & Sun, Z. (2025). Research Progress of Deep Learning-Based Artificial Intelligence Technology in Pest and Disease Detection and Control. In Agriculture (Switzerland) (Vol. 15, Number 19). https://doi.org/10.3390/agriculture15192077

DOI: https://doi.org/10.37637/ab.v9i1.2696

Refbacks

  • There are currently no refbacks.