Title: Ai-Based: Smart Plant Stress Monitor
Authors: Christian Mwakyusa, Allen Mwalekwa, Gift Mushi, Hawa Mponda, Francis Kidakule, John Charles, Jamali Yusuph, Japhet Kanyankole, Kelvin Rutta
Volume: 10
Issue: 4
Pages: 72-76
Publication Date: 2026/04/28
Abstract:
The increasing pressure on global agriculture due to climate change, soil degradation, and water scarcity necessitates the adoption of intelligent and sustainable farming practices. Artificial Intelligence (AI) has emerged as a transformative tool in precision agriculture, enabling real-time monitoring and management of plant health. This review paper critically examines the integration of AI and complementary technologies for the development of smart plant stress monitoring systems, with a focus on applicability in resource-constrained agricultural environments. Through a systematic analysis of previous research studies covering machine learning, computer vision, Internet of Things (IoT), and edge computing, this paper identifies key challenges such as data scarcity, high implementation costs, lack of real-time adaptability, and limited accessibility for smallholder farmers. The review reveals that while individual technologies demonstrate high accuracy in stress detection, their integration into unified, scalable, and affordable systems remains limited. In response, this paper proposes a conceptual framework for an "AI-Based Smart Plant Stress Monitor" that combines image-based disease detection, environmental sensing, predictive analytics, and offline-capable mobile platforms. The paper concludes with strategic recommendations for researchers, developers, and policymakers to promote inclusive, efficient, and sustainable agricultural technologies that enhance crop productivity and resilience.