Prediction of Crop Viability in Drought Conditions Using BCI Interface

Climate variability and water scarcity severely threaten agricultural productivity in drought- prone regions. This paper explores the development of an intelligent system for predicting crop growth under drought conditions, enhanced by Brain-Computer Interface (BCI) alignment to support precision agriculture through human-computer collaboration. The proposed solution integrates remote sensing data, soil health metrics, and climatic variables with machine learning models to forecast crop growth and stress responses. By incorporating BCI technology, the system enables real-time, cognitive feedback-driven decision- making for farmers and researchers, facilitating proactive interventions in irrigation scheduling, crop selection, and stress management. The model leverages convolutional neural networks (CNNs) and long short-term memory (LSTM) networks for spatial-temporal data analysis and incorporates reinforcement learning for adaptive crop management strategies. The result is a robust, scalable, and user-aware system designed to optimize yield and sustainability in vulnerable agricultural ecosystems. Keywords- Crop Viability, Drought Prediction, Precision Agriculture, Brain-Computer Interface (BCI), Deep Learning, Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), Reinforcement Learning