Abstract

The application of remote sensing and digital technologies in agriculture offers transformative potential for improving productivity, resource management, and climate resilience in rural farming communities. However, significant barriers hinder the equitable and effective use of these innovations. Challenges related to data accessibility and cost constraints often limit the availability of high-resolution or real-time datasets for smallholder farmers. Technological literacy gaps further prevent rural communities from fully utilizing available digital tools, compounded by infrastructural deficiencies in internet connectivity and power supply. Policy and institutional barriers, including fragmented governance and regulatory gaps, slow the integration of these technologies into national agricultural frameworks. Ethical and equity concerns add another layer of complexity, highlighting risks of deepening socio-economic divides and data exploitation. Addressing these issues requires a comprehensive, inclusive strategy that emphasizes open data policies, tailored capacity building, infrastructural investments, institutional reforms, and ethical safeguards. By overcoming these challenges, remote sensing and digital innovations can significantly contribute to sustainable, inclusive agricultural development.

Introduction

Remote sensing and cloud computing have emerged as transformative technologies in the field of agriculture, offering new possibilities for accurate crop forecasting and resource planning [1]. The increasing unpredictability of climate patterns, soil degradation, water scarcity, and pest outbreaks has made traditional agricultural forecasting methods less reliable, especially in rural regions [2]. Remote sensing, through satellite and aerial imagery, provides critical insights into vegetation health, land use patterns, and environmental conditions, enabling timely interventions [3]. When combined with the immense computational capabilities of cloud platforms, it becomes possible to process and analyze vast datasets efficiently [4], offering actionable information to farmers, policymakers, and agribusiness stakeholders [5].

The significance of accurate crop forecasting extends beyond yield predictions; it plays a central role in national food security, rural economic planning, and global commodity markets [6]. Rural agricultural ventures, often constrained by limited access to technology, stand to benefit immensely from systems that can offer predictive analytics based on remote sensing data [7]. By leveraging vegetation indices such as NDVI (Normalized Difference Vegetation Index) and EVI (Enhanced Vegetation Index) [8], stakeholders can estimate crop health and growth stages. These predictive models are further enhanced by cloud computing’s ability to integrate multiple data sources [9], including weather patterns, soil profiles, and past harvest records, into coherent forecasting frameworks [10].