IoT and AI for smart agriculture in resource-constrained environments: challenges, opportunities and solutions

Abstract

Climate change is an undeniable reality with far-reaching and profound implications for agriculture and, subsequently, global food security. The highly vulnerable farming communities in underdeveloped and developing countries can overcome resource and capacity constraints with the help of technology, particularly the Internet of Things (IoT) and Artificial Intelligence (AI), making agriculture smarter and more resilient to climate change. This work aims to enrich existing research on smart agriculture by focusing on its applicability in resource-constrained environments. In this context, it presents a detailed overview of the technologies enabling smart agriculture, highlights the challenges and opportunities for farming communities in developing countries, and proposes a framework for climate change-resilient smart agriculture. The framework is loosely based on McKinsey’s 7S model for change management, consisting of hard and soft elements that are defined and adapted for the desired context. The hard elements include IoT sensors, network communications, and data management and analysis using AI, whereas the soft elements consist of policies and regulations, capacity building measures, and a supportive developmental ecosystem. This novel approach has not been employed before in this context. Furthermore, the framework’s efficacy for environmental and crop growth monitoring is demonstrated through its implementation in a low-cost, open-source IoT system within a greenhouse using Edge-Cloud architecture. Here raw, extracted, and derived features are monitored to estimate irrigation requirements and crop maturity date. We conclude with an analysis of the results, recommendations for implementing climate change-resilient smart agriculture in resource-constrained environments, and the identification of areas for future research.

Topics & Concepts