Advancing Food Production Efficiency and Waste Reduction in Smart Cities Using Machine Learning

Abstract

The growth of cities is a challenge for sustaining food systems, as there is a limited supply of resources to support these systems. This paper seeks to understand the impact of IoT and Machine Learning (ML) on the production systems of smart cities through agriculture waste and resource consumption reduction strategies. An IoT equipped urban agriculture system that incorporates machine learning (ML) can optimize resource use, forecast crop yield and reduce food losses at every stage of the production and supply chain. This study offers a water-saving irrigation system, soil health diagnostics, and predictive crop yield reporting to illustrate how these technologies can facilitate agriculture system productivity. This paper outlines the issues associated with data aggregation and model building, including privacy and control of the information, identifies gaps, and makes suggestions for further curriculum and system building. Smart cities wiht the help of proper datasets, policies, algorithms, and models, will averts the negative consequences of urban agriculture. The framework presented in this paper emphasizes that environmentally sustainable agriculture in cities offers the ability to simultaneously resolve a plethora of problems related to the smart city solutions.