AI-Powered Predictive Analysis for Pest and Disease Forecasting in Crops
Hari Kumar Palani, S. Ilangovan, P. Gururama Senthilvel, D R Thirupurasundari, Rajesh Kumar K
Abstract
Artificial Intelligence(AI) has transformed agriculture by enhancing resilience, sustainability, and yield. A new AI-driven crop disease and pest outbreak prediction model is presented in this research. The algorithm uses satellite imagery, meteorological data, historical pest and disease incidence records, and field IoT sensor feeds to dynamically anticipate hazards. Recurrent neural networks (RNNs) for time-series data analysis, convolutional neural networks (CNNs) for high-resolution satellite imagery analysis, and other machine learning techniques to detect diseases and pests are major elements of the system. The model's accuracy has been carefully evaluated against historical epidemics, showing significantly better predictions than conventional methods. This method allows farmers to obtain early warnings and prevent biotic dangers from causing harm. Predictive insights help agronomists and policymakers maximize pest and disease control, promoting sustainable agriculture and food security. To conclude, AI-driven predictive analysis could revolutionize agricultural pest and disease control. This study shows how AI revolutionizes crop harvests using several data sources and cutting-edge algorithms. Future study will improve the model's precision, expandability, and adaptability to different crops and regions.