Controlling Food Security with AI and IoT for Smart Farming Among Low-Income Earners

It has become imperative that certain concerns arising from food scarcity should receive prompt attention. Since a large quantity of our agricultural products emanates from the rural areas, peasant low-income farmers must be well thought-out while arriving at a suitable solution. Interloping Internet of Things (IoT) and Artificial Intelligence can transpose into an intensified, enhanced and navigable framework for smart agricultural practices with a spotlight on guaranteeing food security in a resource-constrained environment, having eminent climate unpredictability. This study enforces the integration of IoT using real-time data and weather forecasting derivable(s) to optimize expected and moderate rainfalls, improve soil fertility and massive crop yielding mechanism output as against the traditional farming practices. The Machine Learning (ML) component strengthens the knowledge of the system and the farmers in line with the global expectations towards accurate decision making, precision, evaluation, impact assessments and validation within a farming cycle. For the participatory implementation framework that will set off affordability, usability, and data accessibility, both enhanced and traditional farming methodology would be compared in terms of data collection, implementation and usability. Symposiums and technical talks were significant enough to bridge the digital inequalities gravely experienced thus far by low-income farmers. The findings authenticated that integrating IoT for smart and large-scale agricultural practises within a cost-sensitive, friendly and limited architecture can notably ensure food security, demystify digital ignorance and scatter the barriers of hunger and poverty.