Authors : Pallapolu Sukeerti; Pilli Anusha; Sane Poojitha

Volume/Issue : Volume 11 - 2026, Issue 4 - April

Google Scholar : https://tinyurl.com/bdf26xyp

Scribd : https://tinyurl.com/2wufcdvj

DOI : https://doi.org/10.38124/ijisrt/26apr1384

Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.

Abstract : Pest infestations remain a critical challenge in modern agriculture, significantly affecting crop productivity and sustainability. This paper presents AgriSense AI, an intelligent, data-driven pest prediction and decision support system designed to enhance precision farming. The system integrates real-time environmental parameters, including temperature, humidity, and rainfall, dynamically retrieved via the OpenWeather API based on geolocation inputs. By combining these parameters with crop-specific data, the proposed framework predicts pest risk levels, identifies the most probable pest, and provides detailed insights such as causes, symptoms, and preventive strategies. Additionally, the system incorporates a shortterm forecasting module capable of predicting pest risk trends for up to five days, enabling proactive and informed agricultural decision-making. The proposed solution contributes to sustainable agriculture by reducing dependency on reactive pest control methods and promoting efficient crop management through intelligent, technology-driven interventions.

Keywords : Smart Agriculture, Predictive Modeling, Pest Risk Assessment, Precision Farming, Machine Learning, Environmental Intelligence, Decision Support System, Crop Protection, Agro-Informatics, Climate-Based Prediction.

References :

1. R. B. Olatinwo and G. Hoogenboom, “Weather-based pest forecasting for sustainable agriculture,” Agricultural Systems, vol. 162, pp. 1–10, 2018.
2. D. Marković, M. Stanković, and S. Krčo, “Predicting pest insect occurrence using machine learning and sensor data,” Computers and Electronics in Agriculture, vol. 162, pp. 104–112, 2019.
3. I. Domingues, G. Pereira, and P. Gomes, “Machine learning for detection and prediction of pests and diseases in agriculture: A review,” Agricultural Informatics, vol. 11, no. 2, pp. 1–12, 2020.
4. S. Sarkar, S. Dutta, and S. Ghosh, “Weather-based prediction of pest infestation using data-driven models,” International Journal of Agricultural Technology, vol. 16, no. 4, pp. 987–996, 2020.
5. J. Basak, S. K. Saha, and A. Das, “Time-series analysis for forecasting pest incidence in crops,” Journal of Agricultural Science and Technology, vol. 22, no. 3, pp. 567–575, 2020.
6. S. Mittal, A. Gandhi, and K. Tripathi, “Artificial intelligence in agriculture: Opportunities and challenges,” IEEE Access, vol. 9, pp. 101–112, 2021.
7. P. Patil, R. Patil, and S. Patil, “Supervised machine learning approaches for crop pest prediction,” in Proc. IEEE Int. Conf. on Smart Systems and Inventive Technology (ICSSIT), 2021, pp. 450–455.
8. Y. Li, X. Chen, and J. Huang, “Deep learning in precision agriculture: A review,” IEEE Access, vol. 8, pp. 203164–203181, 2020.
9. S. Ramesh and D. Vydeki, “IoT-based pest monitoring system for smart agriculture,” International Journal of Advanced Research in Computer Science, vol. 9, no. 2, pp. 45–50, 2018.
10. P. Sharma, A. Verma, and S. Singh, “Climate-driven pest prediction models in precision agriculture,” IEEE Access, vol. 9, pp. 112345–112356, 2021.