TITLE:
Data-Driven Smart Agriculture: Use of AI/ML Technologies for Enhancing Crop Prediction
AUTHORS:
Lamia Islam, Tasnim Sultana, Raisa Tasneem
KEYWORDS:
Crop Management, Artificial Intelligence (AI), Agricultural Big Data, Smart Agriculture, Crop Prediction, Agricultural Forecasting, AI/ML Models
JOURNAL NAME:
Agricultural Sciences,
Vol.16 No.3,
March
5,
2025
ABSTRACT: In the face of unpredictable global economic conditions, urban populations in developing countries are experiencing heightened challenges compared to their rural counterparts. Recognizing the importance of agriculture as a resilient support system during economic downturns, particularly in the wake of climate-related uncertainties such as heat waves, our team, embarked on a mission to identify root causes and viable solutions. Through extensive field visits and analysis, we identified that urban middle-class populations are particularly vulnerable, lacking the resilience afforded by rural agriculture. To address this, we propose the implementation of artificial intelligence (AI) to optimize crop related data, thereby enhancing crop production predictability. Focusing our efforts in Bangladesh, where recent heat waves have exacerbated food security concerns, we seek to leverage AI to develop and maintain accurate crop data prediction as well as crop calendars, ensuring sufficient food reserves for urban and rural populations alike. By sharing our experiences and proven solutions, we aim to contribute to addressing the challenges posed by economic volatility and climate change, safeguarding food security for all.